Package 'mizer'

Description

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

Details

Using mizer is relatively simple. There are three main stages:

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

   • newSingleSpeciesParams()

   • newCommunityParams()

   • newTraitParams()

   • newMultispeciesParams()

2. Running a simulation. This is done by calling the project() function with the model parameters. This produces an object of MizerSim that contains the results of the simulation.

3. Exploring results. After a simulation has been run, the results can be explored using a range of plotting_functions, summary_functions and indicator_functions.

See the mizer website for full details of the principles behind mizer and how the package can be used to perform size-based modelling.

Author(s)

Maintainer: Gustav Delius [email protected] (ORCID) [copyright holder]

Authors:

• Finlay Scott [email protected] [copyright holder]

• Julia Blanchard [email protected] (ORCID) [copyright holder]

• Ken Andersen [email protected] (ORCID) [copyright holder]

Other contributors:

• Richard Southwell [email protected] [contributor, copyright holder]

See Also

Useful links:

• https://sizespectrum.org/mizer/

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

• Report bugs at https://github.com/sizespectrum/mizer/issues

Description

Takes a MizerParams object and adds additional species with given parameters to the ecosystem. It sets the initial values for these new species to their steady-state solution in the given initial state of the existing ecosystem. This will be close to the true steady state if the abundances of the new species are sufficiently low. Hence the abundances of the new species are set so that they are at most 1/100th of the resource power law. Their reproductive efficiencies are set so as to keep them at that 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 where possible, but if one of the new species needs a larger range of w (either because a new species has an egg size smaller than those of existing species or a maximum size larger than those of existing species) then the grid 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 user to values other than those calculated as default from the species parameters, then these will be preserved. Only the rates for the new species will be calculated from their species parameters.

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

Value

An object of type MizerParams

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)

Description

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

Usage

age_mat(params)

Arguments

Details

Using that by definition of the growth rate $g(w) = dw/dt$ 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 are the ages at maturity.

Examples

age_mat(NS_params)

Description

This is not a good way to determine the age at maturity because the von Bertalanffy growth curve is not reliable for larvae and juveniles. However this was used in previous versions of mizer and is supplied for backwards compatibility.

Usage

age_mat_vB(object)

Arguments

Details

Uses the age at maturity that is implied by the von Bertalanffy growth curve specified by the w_inf, k_vb, t0, a and b parameters in the species_params data frame.

If any of k_vb is missing for a species, the function returns NA for that species. Default values of b = 3 and t0 = 0 are used if these are missing. If w_inf is missing, w_max is used instead.

Value

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

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,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)

Description

Takes the density-independent rates $R_{di}$ of egg production (as calculated by getRDI()) and returns reduced, density-dependent reproduction rates $R_{dd}$ given as

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

where $R_{max}$ are the maximum possible reproduction rates that must be specified in a column in the species parameter dataframe. (All quantities in the above equation are species-specific but we dropped the species index for simplicity.)

Usage

BevertonHoltRDD(rdi, species_params, ...)

Arguments

Details

This is only one example of a density-dependence. You can write your own function based on this example, returning different density-dependent reproduction rates. Three other examples provided are RickerRDD(), SheperdRDD(), noRDD() and constantRDD(). For more explanation see setReproduction().

Value

Vector of density-dependent reproduction rates.

See Also

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

Description

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

Usage

box_pred_kernel(ppmr, ppmr_min, ppmr_max)

Arguments

Details

Writing the predator mass as $w$ and the prey mass as $w_p$, the feeding kernel is 1 if $w/w_p$ is between ppmr_min and ppmr_max and zero otherwise. The parameters need to be given in the species parameter dataframe in the columns ppmr_min and ppmr_max.

Value

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

See Also

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

Description

This function calculates the selectivity for each gear, species and size from the gear parameters. It is called by setFishing() when the selectivity is not set by the user.

Usage

calc_selectivity(params)

Arguments

Value

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

Examples

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

Description

Given a MizerParams object params for which biomass observations are available for at least some species via the biomass_observed column in the species_params data frame, this function returns an updated MizerParams object which is rescaled with scaleModel() so that the total biomass in the 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 species parameter data frame. If this is missing, it is assumed that all sizes are included in the observed biomass, i.e., it includes larval biomass.

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

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

Value

A MizerParams object

Examples

params <- NS_params
species_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 <- 10
params2 <- calibrateBiomass(params)
plotBiomassObservedVsModel(params2)

Description

Given a MizerParams object params for which number observations are available for at least some species via the number_observed column in the species_params data frame, this function returns an updated MizerParams object which is rescaled with scaleModel() so that the total number in the 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 species parameter data frame. If this is missing, it is assumed that all sizes are included in the observed number, i.e., it includes larval number.

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

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

Value

A MizerParams object

Examples

params <- NS_params
species_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 <- 10
params2 <- calibrateNumber(params)

Description

Usage

calibrateYield(params)

Arguments

Details

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

Given a MizerParams object params for which yield observations are available for at least some species via the yield_observed column in the species_params data frame, this function returns an updated MizerParams object which is rescaled with scaleModel() so that the total yield in the model agrees with the total observed yield.

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

If you have observations of species biomasses instead of yields, you can use calibrateBiomass() instead of this function.

Value

A MizerParams object

Examples

params <- NS_params
species_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)

Description

Usage

compareParams(params1, params2)

Arguments

Value

String describing the differences

Examples

params1 <- NS_params
params2 <- params1
species_params(params2)$w_mat[1] <- 10
compareParams(params1, params2)

Description

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

Usage

completeSpeciesParams(species_params)

Arguments

Details

validateGivenSpeciesParams() checks the validity of the given species parameter It throws an error if

• the species 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-based parameter is given, as well as the a and b parameters for length-weight conversion, then the weight-based parameters are added. If both length and weight are given, then weight is used and a warning is issued if the two are inconsistent.

If a w_inf column is given but no w_max then the value from w_inf is used. This is for backwards compatibility. But note that the von Bertalanffy parameter w_inf is not the maximum size of the largest individual, but the asymptotic size of an average individual.

Some inconsistencies in the size parameters are resolved as follows:

• Any w_max that is larger than w_repro_max is set to w_repro_max.

• Any w_mat that is not smaller than w_max is set to w_max / 4.

• Any w_mat25 that is not smaller than w_mat is set to NA.

• Any w_min that is not smaller than w_mat is set to 0.001 or w_mat /10, whichever is smaller.

The row names of the returned data frame will be the species names. If species_params was provided as a tibble it is converted back to an ordinary data frame.

The function tests for some typical misspellings of parameter names, like wrong capitalisation or missing underscores and issues a warning if it detects such a name.

validSpeciesParams() first calls validateGivenSpeciesParams() but then goes further by adding default values for species parameters that were not provided. The function sets default values if any of the following species parameters are missing or NA:

• w_repro_max is set to w_max

• w_mat is set to w_max/4

• w_min is set to 0.001

• alpha is set to 0.6

• interaction_resource is set to 1

• n is set to 3/4

Note that the species parameters returned by these functions are not guaranteed to produce a viable model. More checks of the parameters are performed by the individual rate-setting functions (see setParams() for the list of these functions).

Value

A valid species parameter data frame with additional parameters with default values.

A valid species parameter data frame without additional parameters.

See Also

species_params(), validGearParams(), validParams()

Description

Helper function to keep other components constant

Usage

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

Arguments

Value

The current value of the component

Description

The new egg production is set to compensate for the loss of individuals from the smallest size class through growth and mortality. The result should not be modified by density dependence, so this should be used together with the noRDD() 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 object
params <- NS_params
# We set the reproduction rate functions
params <- setRateFunction(params, "RDI", "constantEggRDI")
params <- setRateFunction(params, "RDD", "noRDD")
# Now the egg density should stay fixed no matter how we fish
sim <- 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 densities
no_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 end
all.equal(finalN(sim)[idx], initialN(params)[idx])

Description

Simply returns the value from species_params$constant_reproduction.

Usage

constantRDD(rdi, species_params, ...)

Arguments

Value

Vector species_params$constant_reproduction

See Also

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

Description

This function allows you to make arbitrary changes to how mizer works by allowing you to replace any mizer function with your own version. You should do this only as a last resort, when you find that you can not use the 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 use setRateFunction() instead of this function. Similarly you should use ⁠resource_dynamics()<-⁠ to change the resource dynamics and setReproduction() to change the density-dependence in reproduction. You should also investigate whether you can achieve your goal by introducing additional ecosystem components with setComponent().

If you find that your goal really does require you to overwrite a mizer function, please also create an issue on the mizer issue tracker at https://github.com/sizespectrum/mizer/issues to describe your goal, because it will be interesting to the mizer community and may motivate future improvements to the mizer functionality.

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

This function does not in any way check that your replacement function is compatible 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)

Description

If the predation kernel type has not been specified for a species, then it is set to "lognormal" and the default values are set for the parameters beta and sigma.

Usage

default_pred_kernel_params(object)

Arguments

Value

The object with updated columns in the species params data frame.

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 default values for parameters that are not provided by the user. Sometimes we find better ways to choose the defaults and update mizer accordingly. When we do this, we will increase the edition number.

If you call defaults_edition() without an argument it returns the currently active edition. Otherwise it sets the active edition to the given value.

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

The most recent edition is edition 2. It will become the default in the next release. The current default is edition 1. The following defaults are changed in edition 2:

• catchability = 0.3 instead of 1

• initial effort = 1 instead of 0

Value

The current edition number.

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 slot in MizerParams is different from the existing value in order to give the appropriate messages.

Value

TRUE or FALSE

Description

This function can be used in projectToSteady() to decide when sufficient convergence 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()

Description

Calculates the sum squared difference between log(N) in current and previous state. This function can be used in projectToSteady() to decide when sufficient 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()

Description

A hump-shaped selectivity function with a sigmoidal rise and an independent sigmoidal drop-off. This drop-off is what distinguishes this from the function sigmoid_length() and it is intended to model the escape of large individuals 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, one rising and one dropping. The sigmoidal rise is based on the two parameters l25 and l50 which determine the length at which 25% and 50% of the stock is selected respectively. The sigmoidal drop-off is based on the two parameters l50_right and l25_right which determine the length 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 is length based, it uses the length-weight parameters a and b to convert between 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()

Description

An internal function. Sets up a valid MizerParams object with all the slots initialised and given dimension names, but with some slots left empty. This function 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 that the log-sizes are equally spaced. The spacing is chosen so that there will be no_w fish size bins, with the smallest starting at min_w and the largest starting at max_w. For the resource spectrum there is a larger set of bins containing additional bins below min_w, with the same log size. The number of extra bins is such that min_w_pp comes to lie within the smallest bin.

Changes to species params

The species_params slot of the returned MizerParams object may differ from the data frame supplied as argument to this function because default values are set for missing parameters.

See Also

See newMultispeciesParams() for a function that fills the slots left empty by this function.

Description

Size spectra at end of simulation

Usage

finalN(sim)

finalNResource(sim)

Arguments

Value

For finalN(): An array (species x size) holding the consumer number densities at the end of the simulation

For finalNResource(): A vector holding the resource number densities at the end of the simulation for all size classes

See Also

idxFinalT()

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))

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 the end of the simulation

Description

These functions allow you to get or set the gear parameters stored in a MizerParams object. These are used by setFishing() to set up the selectivity and catchability and thus together with the fishing effort determine the fishing mortality.

Usage

gear_params(params)

gear_params(params) <- value

Arguments

Details

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

• species The name of the species

• gear The name of the gear

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

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

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

For the details see setFishing().

There can optionally also be a column yield_observed that allows you to specify for each gear and species the total annual fisheries yield.

The fishing effort, which is also needed to determine the fishing mortality exerted by a gear is not set via the gear_params data frame but is set with initial_effort() or is specified when calling project().

If you change a gear parameter, this will be used to recalculate the selectivity and catchability arrays by calling setFishing(), unless you have previously set these by hand.

⁠gear_params<-⁠ automatically sets the row names to contain the species name and the gear name, separated by a comma and a space. The last example below illustrates 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 example
gear_params(params)

# setting totally different gears
gear_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 entry
gear_params(params)["Cod, gear1", "catchability"] <- 0.8

Description

Fills in any missing values for f0 so that if the prey abundance was described by the power law $\kappa w^{-\lambda}$ then the encounter rate coming from the given gamma parameter would lead to the feeding level $f_0$. This is thus doing the inverse of get_gamma_default(). Only for internal use.

Usage

get_f0_default(params)

Arguments

Details

For species for which no value for gamma is specified in the species parameter data frame, the f0 values is kept as provided in the species parameter 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()

Description

Fills in any missing values for gamma so that fish feeding on a resource spectrum described by the power law $\kappa w^{-\lambda}$ achieve a feeding level $f_0$. 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()

Description

This function uses the model parameters and other parameters to calculate initial values for the species number densities. These initial abundances 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)

Description

Fills in any missing values for ks so that the critical feeding level needed to sustain the species is as specified in the fc column in the species parameter data frame. If that column is not provided the default critical feeding level $f_c = 0.2$ 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()

Description

This involves finding the feeding kernel function for each species, using the pred_kernel_type parameter in the species_params data frame, checking that it is valid and all its arguments are contained in the species_params data frame, 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 kernel function

Description

Determine reproduction rate needed for initial egg abundance

Usage

get_required_reproduction(params)

Arguments

Value

A vector of reproduction rates for all species

Description

Helper function that returns an array (species x size) of boolean values indicating whether that size bin is within the size limits specified by the arguments. Either the size limits can be the same for all species or they can 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

If min_l is specified there is no need to specify min_w and so on. However, if a length is specified (minimum or maximum) then it is necessary for the species parameter data.frame to include the parameters a and b that determine the relation between length $l$ and weight $w$ by

$w = a l^b.$

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

Description

Internal function to get the array element references of the time dimension for 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 included in the range or not.

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 minimum and maximum weight or length range for the species. Lengths take precedence over weights (i.e. if both min_l and min_w are supplied, only min_l will be used).

Usage

getBiomass(object, ...)

Arguments

Value

If called with a MizerParams object, a vector with the biomass in grams for each species in the model. If called with a MizerSim object, an array (time x species) containing the biomass in grams at each time step for 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"]

Description

Calculates the slope of the community abundance through time by performing a linear regression on the logged total numerical abundance at weight and logged weights (natural logs, not log to base 10, are used). You can specify minimum and maximum weight or length range for the species. Lengths take precedence over weights (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

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

Arguments

Value

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

See Also

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

Examples

# Slope based on biomass, using all species and sizes
slope_biomass <- getCommunitySlope(NS_sim)
slope_biomass[1, ] # in 1976
slope_biomass[idxFinalT(NS_sim), ] # in 2010

# Slope based on numbers, using all species and sizes
slope_numbers <- getCommunitySlope(NS_sim, biomass = FALSE)
slope_numbers[1, ] # in 1976

# Slope based on biomass, using all species and sizes between 10g and 1000g
slope_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 1000g
dem_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

Description

Get information about other ecosystem components

Usage

getComponent(params, component)

Arguments

Value

A list with the entries initial_value, dynamics_fun, encounter_fun, mort_fun, component_params for the requested component. If the requested component does not exist, NULL is returned. If no component argument is given, then a list of lists for all components is returned.

Description

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

Usage

getCriticalFeedingLevel(params)

Arguments

Value

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

Description

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

Usage

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

Arguments

Details

The rates $D_{ij}(w)$ at which a predator of species $i$ and size $w$ consumes biomass from prey species $j$ are calculated from the predation kernel $\phi_i(w, w_p)$, the search volume $\gamma_i(w)$, the feeding level $f_i(w)$, the species interaction matrix $\theta_{ij}$ and the prey abundance density $N_j(w_p)$:

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

The prey index $j$ runs over all species and the resource.

Extra columns are added for the external encounter rate and for any extra ecosystem components in your model for which you have defined an encounter rate function. These encounter rates are multiplied by $1-f_i(w)$ to give the rate of consumption of biomass from these extra components.

This function performs the same integration as getEncounter() but does not aggregate over prey species, and multiplies by $1-f_i(w)$ to get the consumed biomass rather than the available biomass. Outside the range of sizes 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)

Description

Note that the array returned may not be exactly the same as the effort argument that was passed in to project(). This is because only the saved effort is stored (the frequency of saving is determined by the argument t_save).

Usage

getEffort(sim)

Arguments

Value

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

Examples

str(getEffort(NS_sim))

Description

Calculates the energy rate $g_i(w)$ (grams/year) available by species and size for growth after metabolism, movement and reproduction have been accounted 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 default getEGrowth() calls mizerEGrowth(). However you can replace this with your own alternative growth rate function. If your function is called "myEGrowth" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerEGrowth(), with the same 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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the energy at a particular time step
getEGrowth(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)

Description

Returns the rate at which a predator of species $i$ and weight $w$ 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) with the encounter rates.

Predation encounter

The encounter rate $E_i(w)$ at which a predator of species $i$ and weight $w$ encounters food has contributions from the encounter of fish prey and of resource. This is determined by summing over all prey species and the resource spectrum and then integrating over all prey sizes $w_p$, weighted by predation kernel $\phi(w,w_p)$:

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

Here $N_j(w)$ is the abundance density of species $j$ and $N_R(w)$ is the abundance density of resource. The overall prefactor $\gamma_i(w)$ determines the predation power of the predator. It could be interpreted as a search volume and is set with the setSearchVolume() function. The predation kernel $\phi(w,w_p)$ is set with the setPredKernel() function. The species interaction matrix $\theta_{ij}$ is set with setInteraction() and the resource interaction vector $\theta_{ip}$ is taken from the interaction_resource column in params@species_params.

Details

The encounter rate is multiplied by $1-f_0$ to obtain the consumption rate, where $f_0$ is the feeding level calculated with getFeedingLevel(). This is used by the project() function for performing simulations.

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

If your model contains additional components that you added with setComponent() and for which you specified an encounter_fun function then the encounters of these components will be included in the returned value.

Your own encounter function

By default getEncounter() calls mizerEncounter(). However you can replace this with your own alternative encounter function. If your function is called "myEncounter" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerEncounter(), with the same 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)

Description

Calculates the energy rate (grams/year) available for reproduction after growth 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

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

where $E_{r.i}(w)$ is the rate at which energy becomes available for growth and reproduction, calculated with getEReproAndGrowth(), and $\psi_i(w)$ is the proportion of this energy that is used for reproduction. This proportion is taken from the params object and is set with setReproduction().

Your own reproduction rate function

By default getERepro() calls mizerERepro(). However you can replace this with your own alternative reproduction rate function. If your function is called "myERepro" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerERepro(), with the same 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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the energy at a particular time step
getERepro(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)

Description

Calculates the energy rate $E_{r.i}(w)$ (grams/year) available for reproduction and growth after metabolism and movement have been accounted for.

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 by getFeedingLevel(), this can also be expressed as

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

where $h_i$ is the maximum intake rate, set with setMaxIntakeRate(). The assimilation rate $\alpha_i$ is taken from the species parameter data frame in params. The metabolic rate metab is taken from params and set with setMetabolicRate().

The return value can be negative, which means that the energy intake does not cover the cost of metabolism and movement.

Your own energy rate function

By default getEReproAndGrowth() calls mizerEReproAndGrowth(). However you can replace this with your own alternative energy rate function. If your function is called "myEReproAndGrowth" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerEReproAndGrowth(), with the same arguments.

See Also

The part of this energy rate that is invested into growth is calculated with getEGrowth() and the part that is invested into reproduction is calculated with getERepro().

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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the energy at a particular time step
getEReproAndGrowth(params, n = N(sim)[15, , ], 
                   n_pp = NResource(sim)[15, ], t = 15)

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

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

where $E_{r.i}(w)$ is the rate at which energy becomes available for growth and reproduction, calculated with getEReproAndGrowth(), and $\psi_i(w)$ is the proportion of this energy that is used for reproduction. This proportion is taken from the params object and is set with setReproduction().

Your own reproduction rate function

By default getERepro() calls mizerERepro(). However you can replace this with your own alternative reproduction rate function. If your function is called "myERepro" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerERepro(), with the same 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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the energy at a particular time step
getERepro(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)

Description

Returns the feeding level. By default this function uses mizerFeedingLevel() to calculate the feeding level, but this can be overruled via setRateFunction().

Usage

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

Arguments

Value

If a MizerParams object is passed in, the function returns a two dimensional array (predator species x predator size) based on the abundances also passed in. If a MizerSim object is passed in, the function returns a three dimensional array (time step x predator species x predator size) with the feeding level calculated at every time step in the simulation. If drop = TRUE then the dimension of length 1 will be removed from the returned array.

Feeding level

The feeding level $f_i(w)$ is the proportion of its maximum intake rate at which the predator is actually taking in fish. It is calculated from the encounter rate $E_i$ and the maximum intake rate $h_i(w)$ as

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

The encounter rate $E_i$ is passed as an argument or calculated with getEncounter(). The maximum intake rate $h_i(w)$ is taken from the params object, and is set with setMaxIntakeRate(). 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 the predator actually consumes.

Your own feeding level function

By default getFeedingLevel() calls mizerFeedingLevel(). However you can replace this with your own alternative feeding level function. If your function is called "myFeedingLevel" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerFeedingLevel(), with the same 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 level
fl <- getFeedingLevel(params)
# Project with constant fishing effort for all gears for 20 time steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the feeding level at all saved time steps
fl <- getFeedingLevel(sim)
# Get the feeding level for years 15 - 20
fl <- getFeedingLevel(sim, time_range = c(15, 20))

Description

Calculates the total fishing mortality (in units 1/year) from all gears by species and size and possibly time. See setFishing() for details of how 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 mortalities imposed by each gear, $\mu_{f.i}(w)=\sum_g F_{g,i,w}$. The fishing mortality for each gear is obtained as catchability x selectivity x effort.

Value

An array. If the effort argument has a time dimension, or object is of class MizerSim, the output array has three dimensions (time x species x size). If the effort argument does not have a time dimension, the output array has two dimensions (species x size).

The effort argument is only used if a MizerParams object is passed in. The effort argument can be a two dimensional array (time x gear), a vector of length equal to the number of gears (each gear has a different effort that is constant in time), or a single numeric value (each gear has the same effort that is constant in time). The order of gears in the effort argument must be the same as in the MizerParams object.

If the object argument is of class MizerSim then the effort slot of the MizerSim object is used and the effort argument is not used.

Your own fishing mortality function

By default getFMort() calls mizerFMort(). However you can replace this with your own alternative fishing mortality function. If your function is called "myFMort" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerFMort(), with the same 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 when effort is constant for all 
# gears and time:
getFMort(params, effort = 1)
# Get the total fishing mortality when effort is different
# between the four gears but constant in time:
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)
getFMort(params, effort = effort)
# Get the total fishing mortality using the effort already held in a 
# MizerSim object.
sim <- project(params, t_max = 20, effort = 0.5)
getFMort(sim)
getFMort(sim, time_range = c(10, 20))

Description

Calculates the fishing mortality rate $F_{g,i,w}$ by gear, species and size 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 a MizerSim is passed in, the output array has four dimensions (time x gear x species x size). If the effort argument does not have a time dimension (i.e. it is a vector or a single numeric), the output array has three dimensions (gear x species x size).

Note

Here: fishing mortality = catchability x selectivity x effort.

The effort argument is only used if a MizerParams object is passed in. The effort argument can be a two dimensional array (time x gear), a vector of length equal to the number of gears (each gear has a different effort that is constant in time), or a single numeric value (each gear has the same effort that is constant in time). The order of gears in the effort argument must be the same the same as in the MizerParams object. If the effort argument is not supplied, its value is taken from the ⁠@initial_effort⁠ slot in the params object.

If the object argument is of class MizerSim then the effort slot of the MizerSim object is used and the effort argument is not used.

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 when effort is constant
# for all gears and time:
getFMortGear(params, effort = 1)
# Get the fishing mortality when effort is different
# between the four gears but constant in time:
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)
getFMortGear(params, effort = effort)
# Get the fishing mortality using the effort already held in a MizerSim object.
sim <- project(params, t_max = 20, effort = 0.5)
getFMortGear(sim)
getFMortGear(sim, time_range = c(10, 20))

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]")

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 a MizerParams object is passed in, the function returns a two dimensional array (prey species x prey size) based on the abundances also passed in. If a MizerSim object is passed in, the function returns a three dimensional array (time step x prey species x prey size) with the predation mortality calculated at every time step in the simulation. Dimensions may be dropped if they have length 1 unless drop = FALSE.

Your own predation mortality function

By default getPredMort() calls mizerPredMort(). However you can replace this with your own alternative predation mortality function. If your function is called "myPredMort" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerPredMort(), with the same arguments.

See Also

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

Examples

params <- NS_params
# With constant fishing effort for all gears for 20 time steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get predation mortality at one time step
getPredMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])
# Get predation mortality at all saved time steps
getPredMort(sim)
# Get predation mortality over the years 15 - 20
getPredMort(sim, time_range = c(15, 20))

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 default getResourceMort() calls mizerResourceMort(). However you can replace this with your own alternative resource mortality function. If your function is called "myResourceMort" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerResourceMort(), with the same 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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get resource mortality at one time step
getResourceMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])

Description

Calculates the mean maximum weight of the community through time. This can be calculated by numbers or biomass. The calculation is the sum of the w_max * abundance of each species, divided by the total abundance community, where abundance is either in biomass or numbers. You can specify minimum and maximum weight or length range for the species. Lengths take precedence over weights (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 the measure argument. If measure = “both” then you get a matrix with two columns, one with values by numbers, the other with values by biomass at each saved time step. If measure = “numbers” or “biomass” you get a vector of the respective values at each 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, ]

Description

Calculates the mean weight of the community through time. This is simply the total biomass of the community divided by the abundance in numbers. You can specify minimum and maximum weight or length range for the species. Lengths take precedence over weights (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

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]

Description

Calculates the total mortality rate $\mu_i(w)$ (in units 1/year) on each species by size from predation mortality, background mortality and fishing mortality 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 with setComponent() and for which you specified a mort_fun function then the mortality inflicted by these components will be included in the returned value.

Value

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

Your own mortality function

By default getMort() calls mizerMort(). However you can replace this with your own alternative mortality function. If your function is called "myMort" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerMort(), with the same 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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the total mortality at a particular time step
getMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], 
        t = 15, effort = 0.5)

Description

Calculates the number of individuals within user-defined size limits. The default option is to use the whole size range. You can specify minimum and maximum weight or lengths for the species. Lengths take precedence over weights (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 for each species in the model. If called with a MizerSim object, an array (time x 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"]

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
# simulation
sim <- project(NS_params, t_max = 1)
identical(getParams(sim), NS_params)

Description

This is deprecated and is no longer used by the mizer project() method. Calculates the amount $E_{a,i}(w)$ of food exposed to each predator as a 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_params
sim <- 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)

Description

Calculates the total predation mortality rate $\mu_{p,i}(w_p)$ (in units of 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 rate pred_rate is returned by getPredRate().

Usage

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

Arguments

Value

If a MizerParams object is passed in, the function returns a two dimensional array (prey species x prey size) based on the abundances also passed in. If a MizerSim object is passed in, the function returns a three dimensional array (time step x prey species x prey size) with the predation mortality calculated at every time step in the simulation. Dimensions may be dropped if they have length 1 unless drop = FALSE.

Your own predation mortality function

By default getPredMort() calls mizerPredMort(). However you can replace this with your own alternative predation mortality function. If your function is called "myPredMort" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerPredMort(), with the same arguments.

See Also

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

Examples

params <- NS_params
# With constant fishing effort for all gears for 20 time steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get predation mortality at one time step
getPredMort(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])
# Get predation mortality at all saved time steps
getPredMort(sim)
# Get predation mortality over the years 15 - 20
getPredMort(sim, time_range = c(15, 20))

Description

Calculates the potential rate (in units 1/year) at which a prey individual of a given size $w$ is killed by predators from species $j$. 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 getPredMort() to calculate 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 default getPredRate() calls mizerPredRate(). However you can replace this with your own alternative predation rate function. If your function is called "myPredRate" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerPredRate(), with the same arguments.

See Also

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

Examples

params <- NS_params
# With constant fishing effort for all gears for 20 time steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the feeding level at one time step
getPredRate(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ])

Description

Calculates the proportion of large fish through time in the MizerSim class within user defined size limits. The default option is to use the whole size range. You can specify minimum and maximum size ranges for the species and also the threshold size for large fish. Sizes can be expressed as weight or size. Lengths take precedence over weights (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. This function can be used to calculate the Large Fish 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]

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 from mizerEncounter()

• feeding_level from mizerFeedingLevel()

• e from mizerEReproAndGrowth()

• e_repro from mizerERepro()

• e_growth from mizerEGrowth()

• pred_rate from mizerPredRate()

• pred_mort from mizerPredMort()

• f_mort from mizerFMort()

• mort from mizerMort()

• rdi from mizerRDI()

• rdd from BevertonHoltRDD()

• resource_mort from mizerResourceMort()

However you can replace any of these rate functions by your own rate function if you wish, see setRateFunction() 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))

Description

Calculates the density dependent rate of egg production $R_i$ (units 1/year) for each species. This is the flux entering the smallest size class of each species. The density dependent rate is the density independent rate obtained with getRDI() after it has been put through the density dependence function. This is the Beverton-Holt function BevertonHoltRDD() by default, but this can be changed. See setReproduction() 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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the rate at a particular time step
getRDD(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)

Description

Calculates the density-independent rate of total egg production $R_{di}$ (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 rate $E_r(w)\psi(w)$ at which energy is invested in reproduction, as calculated by getERepro(), multiplying it by the number of individuals$N(w)$ and integrating over all sizes $w$ and then multiplying by the reproductive efficiency $\epsilon$ and dividing by the egg size w_min, and by a factor of two to account for the two sexes:

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

Used by getRDD() to calculate the actual, density dependent rate. See setReproduction() for more details.

Value

A numeric vector the length of the number of species.

Your own reproduction function

By default getRDI() calls mizerRDI(). However you can replace this with your own alternative reproduction function. If your function is called "myRDI" then you register it in a MizerParams object params with

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

Your function will then be called instead of mizerRDI(), with the same arguments. For an example of an alternative reproduction function see constantEggRDI().

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 steps
sim <- project(params, t_max = 20, effort = 0.5)
# Get the density-independent reproduction rate at a particular time step
getRDI(params, n = N(sim)[15, , ], n_pp = NResource(sim)[15, ], t = 15)

Description

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

Usage

getReproductionLevel(params)

Arguments