Package 'MSEtool'

Description

Example objects of class Stock

Usage

Albacore

Blue_shark

Bluefin_tuna

Bluefin_tuna_WAtl

Butterfish

Herring

Mackerel

Porgy

Rockfish

Snapper

Sole

Toothfish

Format

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

An object of class Stock of length 1.

Examples

avail("Stock")

Description

Example objects of class MOM

Usage

Albacore_TwoFleet

Format

An object of class MOM of length 1.

Examples

avail("MOM")

Description

Apply multi Management Procedures (class MMP) to a hierarchical list of Data class objects

Usage

applyMMP(
  DataList,
  MP = NA,
  reps = 1,
  nsims = NA,
  silent = FALSE,
  parallel = snowfall::sfIsRunning()
)

Arguments

Value

A hierarchical list of management recommendations (object class Rec), Fleets nested in Stocks

Description

Apply Management Procedures to an object of class Data

Usage

applyMP(
  Data,
  MPs = NA,
  reps = 100,
  nsims = NA,
  silent = FALSE,
  parallel = snowfall::sfIsRunning()
)

Arguments

Value

A list with the first element a list of management recommendations, and the second the updated Data object

Description

Reads a fitted ASAP model and uses the MLE estimates with identical reconstruction among simulations. Future recruitment is sampled from a lognormal distribution with autocorrelation. ASAP2Data imports a Data object.

Usage

ASAP2OM(
  asap,
  nsim = 48,
  proyears = 50,
  mcmc = FALSE,
  Name = "ASAP Model",
  Source = "No source provided",
  nyr_par_mu = 3,
  Author = "No author provided",
  report = FALSE,
  silent = FALSE
)

ASAP2Data(asap, Name = "ASAP assessment")

Arguments

Details

Length at age is not used in ASAP so arbitrary placeholder values are used for length-based parameters. Update these parameters to model length in the operating model.

Value

An operating model OM class.

Author(s)

Q. Huynh

See Also

Assess2OM

Description

A function that develops a multiple fleet operating model (MOM) and either models a unisex or 2-sex stock from arrays of abundance, fishing mortality, and biological parameters. The user still needs to parameterize most of the observation and implementation portions of the operating model.

Usage

Assess2MOM(
  Name = "MOM created by Assess2MOM",
  proyears = 50,
  interval = 2,
  CurrentYr = as.numeric(format(Sys.Date(), "%Y")),
  h = 0.999,
  Obs = MSEtool::Imprecise_Unbiased,
  Imp = MSEtool::Perfect_Imp,
  naa,
  faa,
  waa,
  Mataa,
  Maa,
  laa,
  fecaa,
  nyr_par_mu = 3,
  LowerTri = 1,
  recind = 0,
  plusgroup = TRUE,
  altinit = 0,
  fixq1 = TRUE,
  report = FALSE,
  silent = FALSE,
  ...
)

Arguments

Details

Use a seed for the random number generator to sample future recruitment.

Value

An object of class MOM.

Author(s)

Q. Huynh

See Also

SS2MOM multiMSE Assess2OM

Description

A function that uses a set of bootstrap estimates of numbers-at-age, fishing mortality rate-at-age, M-at-age, weight-at-age, length-at-age and Maturity-at-age to define a fully described MSEtool operating model. The user still needs to parameterize most of the observation and implementation portions of the operating model.

Usage

Assess2OM(
  Name = "A fishery made by VPA2OM",
  proyears = 50,
  interval = 2,
  CurrentYr = as.numeric(format(Sys.Date(), "%Y")),
  h = 0.999,
  Obs = MSEtool::Imprecise_Unbiased,
  Imp = MSEtool::Perfect_Imp,
  naa,
  faa,
  waa,
  Mataa,
  Maa,
  laa,
  nyr_par_mu = 3,
  LowerTri = 1,
  recind = 0,
  plusgroup = TRUE,
  altinit = 0,
  fixq1 = TRUE,
  report = FALSE,
  silent = FALSE,
  ...
)

VPA2OM(
  Name = "A fishery made by VPA2OM",
  proyears = 50,
  interval = 2,
  CurrentYr = as.numeric(format(Sys.Date(), "%Y")),
  h = 0.999,
  Obs = MSEtool::Imprecise_Unbiased,
  Imp = MSEtool::Perfect_Imp,
  naa,
  faa,
  waa,
  Mataa,
  Maa,
  laa,
  nyr_par_mu = 3,
  LowerTri = 1,
  recind = 0,
  plusgroup = TRUE,
  altinit = 0,
  fixq1 = TRUE,
  report = FALSE,
  silent = FALSE,
  ...
)

Arguments

Details

Use a seed for the random number generator to sample future recruitment.

Value

An object of class OM.

Author(s)

T. Carruthers

See Also

SS2OM iSCAM2OM WHAM2OM ASAP2OM

Description

Example objects of class Data

Usage

Atlantic_mackerel

China_rockfish

Cobia

Example_datafile

Gulf_blue_tilefish

ourReefFish

Red_snapper

Simulation_1

Format

An object of class Data of length 1.

An object of class Data of length 1.

An object of class Data of length 1.

An object of class Data of length 1.

An object of class Data of length 1.

An object of class Data of length 1.

An object of class Data of length 1.

An object of class Data of length 1.

Examples

avail("Data")

Description

Generic class finder

Usage

avail(classy, package = NULL, msg = TRUE)

Arguments

Details

Finds objects of the specified class in the global environment or the openMSE packages.

Author(s)

T. Carruthers

See Also

Can Cant avail

Examples

avail("OM", msg=FALSE)
Stocks <- avail("Stock")
Fleets <- avail("Fleet")
MPs <- avail("MP")

Description

A function that generates an operating model from the MCMC samples of an Awatea model. Code optimized for the BC Pacific ocean perch assessment (Haigh et al. 2018).

Usage

Awatea2OM(
  AwateaDir,
  nsim = 48,
  proyears = 50,
  Name = "OM made by Awatea2OM",
  Source = "No source provided",
  Author = "No author provided",
  verbose = TRUE
)

Arguments

Details

This function averages biological parameters across sex and then sends arrays to VPA2OM, assumes unfished status (B/B0 = 1) in the first year, and assumes a single fishing fleet.

Author(s)

Q. Huynh and T. Carruthers

References

Haigh, R., et al. 2018. Stock assessment for Pacific Ocean Perch (Sebastes alutus) in Queen Charlotte Sound, British Columbia in 2017. Canadian Science Advisory Secretariat (CSAS) Research Document 2018/038. 232 pp. https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2018/2018_038-eng.html

Description

Import a multi-stock, multi-fleet OM from a BAM object

Usage

BAM2MOM(
  rdat,
  nsim = 48,
  proyears = 50,
  interval = 1,
  stock_name = NULL,
  fleet_name = NULL,
  LowerTri = 0,
  report = FALSE,
  ...
)

BAM2OM(rdat, nsim = 48, proyears = 50, interval = 2, report = FALSE, ...)

Arguments

Value

An object of class MOM

Functions

• BAM2OM(): Create a single stock/fleet OM from a BAM object

Description

Boxplot of TAC recommendations

Usage

## S3 method for class 'Data'
boxplot(x, upq = 0.9, lwq = 0.1, ylim = NULL, outline = FALSE, col = NULL, ...)

Arguments

Value

Returns a data frame containing the information shown in the plot

Author(s)

A. Hordyk

Description

Calculate Reference Yield

Usage

calcRefYield(x, StockPars, FleetPars, pyears, Ncurr, nyears, proyears)

Arguments

Author(s)

A. Hordyk

Description

A function that condenses the number of catch-at-length bins in a data object

Usage

CALsimp(Data, nbins = 10, simno = 1)

Arguments

Author(s)

T. Carruthers

Description

Diagnostic tools that look up the slot requirements of each MP and compares to the data available in the Data object.

Usage

Can(Data, timelimit = 1, MPs = NA, dev = FALSE, silent = FALSE)

Cant(Data, timelimit = 1, silent = FALSE)

DLMdiag(
  Data,
  command = c("available", "not available", "needed"),
  reps = 5,
  timelimit = 1,
  funcs1 = NA,
  dev = FALSE,
  silent = FALSE
)

Needed(Data, timelimit = 1, silent = FALSE)

Arguments

Functions

• Can(): Identifies MPs that have the correct data, do not produce errors, and run within the time limit.

• Cant(): Identifies MPs that don't have sufficient data, lead to errors, or don't run in time along with a list of their data requirements.

• DLMdiag(): Internal function called by Can and Cant

• Needed(): Identifies what data are needed to run the MPs that are currently not able to run given a Data object

See Also

avail Data

Examples

CanMPs <- Can(MSEtool::Cobia)
CantMPs <- Cant(MSEtool::Cobia)
Needs <- Needed(MSEtool::Cobia)

Description

Custom MPs cannot have the same names of MPs in MSEtool and related packages

Usage

CheckDuplicate(MPs)

Arguments

Value

An error if duplicated MP names, otherwise nothing

Description

Check that specified MPs are valid and will run on MSEtool::SimulatedData

Usage

CheckMPs(MPs = NA, silent = FALSE)

Arguments

Value

MP names

Description

Utility functions for MSE objects

Usage

checkMSE(MSEobj)

addMPs(MSEobjs)

joinMSE(MSEobjs = NULL)

joinHist(Hist_List)

updateMSE(MSEobj, save.name = NULL)

Arguments

Value

An object of class MSE

A new object of class Hist

Functions

• checkMSE(): Check that an MSE object includes all slots in the latest version of DLMtool

• addMPs(): Adds additional MPs to an MSE object by combining multiple MSE objects that have identical historical OM values but different MPs.

• joinMSE(): Joins two or more MSE objects together across simulations. MSE objects must have identical number of historical years, and projection years.

• joinHist(): Join objects of class Hist. Does not join slot OM

• updateMSE(): Updates an existing MSE object (class MSE) from a previous version of the MSEtool to include slots new to the latest version. Also works with Stock, Fleet, Obs, Imp, and Data objects. The new slots will be empty, but avoids the 'slot doesn't exist' error that sometimes occurs. Returns an object of class matching class(MSEobj)

Author(s)

A. Hordyk

See Also

joinData

Description

Check OM object is complete

Usage

CheckOM(OM, msg = TRUE, stop_if_missing = TRUE)

Arguments

Value

The OM object with default values (if needed)

Examples

testOM <- CheckOM(testOM)

Description

Interactive plots to specify trends and variability in fishing effort, fleet selectivity, and natural mortality for the operating model.

Usage

ChooseEffort(Fleet, Years = NULL)

ChooseM(OM, type = c("age", "length"), x = NULL, y = NULL)

ChooseSelect(Fleet, Stock, FstYr = NULL, SelYears = NULL)

Arguments

Details

Value

ChooseEffort and ChooseSelect return a Fleet object while ChooseM returns an OM object.

Author(s)

A. Hordyk

Description

Create a blank MP recommendations object (class Rec) of the right dimensions

Usage

CombineMMP(temp, nareas)

Arguments

Author(s)

T. Carruthers

Description

Have I undertaken enough simulations (nsim)? Has my MSE converged on stable (reliable) performance metrics?

Usage

Converge(
  MSEobj,
  PMs = c("Yield", "P10", "AAVY"),
  maxMP = 15,
  thresh = 0.5,
  ref.it = 20,
  inc.leg = FALSE,
  all.its = FALSE,
  nrow = NULL,
  ncol = NULL,
  silent = FALSE
)

Arguments

Details

Performance metrics are plotted against the number of simulations. Convergence diagnostics are calculated over the last ref.it (default = 20) iterations. The convergence diagnostics are:

1. Is the order of the MPs stable over the last ref.it iterations?

2. Is the average difference in performance statistic over the last ref.it iterations < thresh?

By default three commonly used performance metrics are used:

1. Average Yield Relative to Reference Yield

2. Probability Spawning Biomass is above 0.1BMSY

3. Probability Average Annual Variability in Yield is < 20 per cent

Additional or alternative performance metrics objects can be supplied. Advanced users can develop their own performance metrics.

Value

A table of convergence results for each MP

Author(s)

A. Hordyk

Examples

## Not run: 
MSE <- runMSE()
Converge(MSE)

## End(Not run)

Description

Current default thresholds for COSEWIC satisficing

Usage

Cos_thresh_tab(Ptab1)

Arguments

Author(s)

T. Carruthers

Description

Internal function for checking that the OM@cpars is formatted correctly

Usage

cparscheck(cpars)

Arguments

Value

either an error and the length of the first dimension of the various cpars list items or passes and returns the number of simulations in cpars

Author(s)

T. Carruthers

Description

Compare median biomass and yield in first year and last 5 years of projection

Usage

Cplot(
  MSEobj,
  MPs = NA,
  lastYrs = 5,
  point.size = 2,
  lab.size = 4,
  axis.title.size = 12,
  axis.text.size = 10,
  legend.title.size = 12
)

Arguments

Examples

## Not run: 
MSE <- runMSE()
Cplot(MSE)

## End(Not run)

Description

A function to read in Data object from an Excel spreadsheet with tabs named following specific convention.

Usage

Data_xl(fname, stkname, fpath = "", saveCSV = FALSE)

Arguments

Details

The Excel spreadsheet must have tabs named with the following convention. For example if stkname is 'myFish', the Data parameters are in a tab named 'myFishData'.

Value

A object of class Data

Author(s)

A. Hordyk

Examples

## Not run: 
OM <- OM_xl(fname='OMTables.xlsx', stkname='myFish')

## End(Not run)

Description

An object for storing fishery data for analysis

Slots

Name

The name of the Data object. Single value. Character string

Common_Name

Common name of the species. Character string

Species

Scientific name of the species. Genus and species name. Character string

Region

Name of the general geographic region of the fishery. Character string

LHYear

The last historical year of the simulation (before projection). Single value. Positive integer

MPrec

The previous recommendation of a management procedure. Vector of length nsim. Positive real numbers

Units

Units of the catch/absolute abundance estimates. Single value. Character string

MPeff

The current level of effort. Vector of length nsim. Positive real numbers

nareas

Number of fishing areas. Vector of length nsim. Non-negative integer

MaxAge

Maximum age. Vector nsim long. Positive integer

Mort

Natural mortality rate. Vector nsim long. Positive real numbers

CV_Mort

Coefficient of variation in natural mortality rate. Vector nsim long. Positive real numbers

vbLinf

Maximum length. Vector nsim long. Positive real numbers

CV_vbLinf

Coefficient of variation in maximum length. Vector nsim long. Positive real numbers

vbK

The von Bertalanffy growth coefficient K. Vector nsim long. Positive real numbers

CV_vbK

Coefficient of variation in the von Bertalanffy K parameter. Vector nsim long. Positive real numbers

vbt0

Theoretical age at length zero. Vector nsim long. Non-positive real numbers

CV_vbt0

Coefficient of variation in age at length zero. Vector nsim long. Positive real numbers

wla

Weight-Length parameter alpha. Vector nsim long. Positive real numbers

CV_wla

Coefficient of variation in weight-length parameter a. Vector nsim long. Positive real numbers

wlb

Weight-Length parameter beta. Vector nsim long. Positive real numbers

CV_wlb

Coefficient of variation in weight-length parameter b. Vector nsim long. Positive real numbers

steep

Steepness of stock-recruitment relationship. Vector nsim long. Value in the range of one-fifth to 1

CV_steep

Coefficient of variation in steepness. Vector nsim long. Positive real numbers

sigmaR

Recruitment variability. Vector nsim long. Positive real numbers

CV_sigmaR

Coefficient of variation in recruitment variability. Vector nsim long. Positive real numbers

L50

Length at 50 percent maturity. Vector nsim long. Positive real numbers

CV_L50

Coefficient of variation in length at 50 per cent maturity. Vector nsim long. Positive real numbers

L95

Length at 95 percent maturity. Vector nsim long. Positive real numbers

LenCV

Coefficient of variation of length-at-age (assumed constant for all age classes). Vector nsim long. Positive real numbers

LFC

Length at first capture. Vector nsim long. Positive real numbers

CV_LFC

Coefficient of variation in length at first capture. Vector nsim long. Positive real numbers

LFS

Shortest length at full selection. Vector nsim long. Positive real numbers

CV_LFS

Coefficient of variation in length at full selection. Vector nsim long. Positive real numbers

Vmaxlen

Vulnerability of individuals at asymptotic length. Vector nsim long. Real number between 0 and 1.

Year

Years that corresponding to catch and relative abundance data. Vector nyears long. Positive integer

Cat

Total annual catches. Matrix of nsim rows and nyears columns. Non-negative real numbers

CV_Cat

Coefficient of variation in annual catches. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Cat for all years.

Effort

Annual fishing effort. Matrix of nsim rows and nyears columns. Non-negative real numbers

CV_Effort

Coefficient of variation in annual effort. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Effort for all years.

Ind

Relative total abundance index. Matrix of nsim rows and nyears columns. Non-negative real numbers

CV_Ind

Coefficient of variation in the relative total abundance index. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Ind for all years

SpInd

Relative spawning abundance index. Matrix of nsim rows and nyears columns. Non-negative real numbers

CV_SpInd

Coefficient of variation in the relative spawning abundance index. Matrix nsim rows and either 1 or nyear columns. Positive real numbers.

VInd

Relative vulnerable abundance index. Matrix of nsim rows and nyears columns. Non-negative real numbers

CV_VInd

Coefficient of variation in the relative vulnerable abundance index. Matrix nsim rows and either 1 or nyear columns. Positive real numbers.

AddInd

Optional additional indices. Array of dimensions nsim, n additional indices, and nyears (length Year).

CV_AddInd

Coefficient of variation for additional indices. Array of same dimensions as AddInd

AddIndV

Vulnerability-at-age schedules for the additional indices. Array with dimensions: nsim, n additional indices, MaxAge+1.

AddIunits

Units for the additional indices - biomass (1; default) or numbers (0). Numeric vector length n.ind.

AddIndType

Index calculated from total stock (1, default), spawning stock (2), or vulnerable stock (3). Numeric vector of length n.ind

Rec

Recent recruitment strength. Matrix of nsim rows and nyears columns. Non-negative real numbers

CV_Rec

Log-normal CV for recent recruitment strength. Matrix nsim rows and either 1 or nyear columns. Positive real numbers. Note: built-in MPs use only the first value of CV_Rec for all years.

ML

Mean length time series. Matrix of nsim rows and nyears columns. Non-negative real numbers

Lc

Modal length of catches. Matrix of nsim rows and nyears columns. Positive real numbers

Lbar

Mean length of catches over Lc. Matrix of nsim rows and nyears columns. Positive real numbers

Vuln_CAA

Optional vulnerability-at-age schedule for catch-at-age samples. Used to condition OM for closed-loop simulation testing. Replaces the fleet selectivity schedule in the OM used to generate CAA samples. Matrix with dimensions nsim x MaxAge+1.

CAA

Catch at Age data (numbers). Array of dimensions nsim x nyears x MaxAge+1. Non-negative integers

Vuln_CAL

Optional vulnerability-at-length schedule for catch-at-length samples. Used to condition OM for closed-loop simulation testing. Replaces the fleet selectivity schedule in the OM used to generate CAL samples. Matrix with dimensions nsim x length(CAL_mids).

CAL_bins

The values delimiting the length bins for the catch-at-length data. Vector. Non-negative real numbers

CAL_mids

The values of the mid-points of the length bins. Optional, calculated from CAL_bins if not entered. Vector. Non-negative real numbers.

CAL

Catch-at-length data. An array with dimensions nsim x nyears x length(CAL_mids). Non-negative integers. By default the CAL data will be the retained lengths (i.e, not including discards). If OM@control$CAL =="removals" then the CAL data will include all removals (retained + discards).

Dep

Stock depletion SSB(current)/SSB(unfished). Vector nsim long. Fraction.

CV_Dep

Coefficient of variation in current stock depletion. Vector nsim long. Positive real numbers

Abun

An estimate of absolute current vulnerable abundance. Vector nsim long. Positive real numbers

CV_Abun

Coefficient of variation in estimate of absolute current stock size. Vector nsim long. Positive real numbers

SpAbun

An estimate of absolute current spawning stock abundance. Vector nsim long. Positive real numbers

CV_SpAbun

Coefficient of variation in estimate of absolute spawning current stock size. Vector nsim long. Positive real numbers

FMSY_M

An assumed ratio of FMSY to M. Vector nsim long. Positive real numbers

CV_FMSY_M

Coefficient of variation in the ratio in FMSY/M. Vector nsim long. Positive real numbers

BMSY_B0

The most productive stock size relative to unfished. Vector nsim long. Fraction

CV_BMSY_B0

Coefficient of variation in the position of the most productive stock size relative to unfished. Vector nsim long. Positive real numbers

Cref

Reference or target catch level (eg MSY). Vector of length nsim. Positive real numbers

CV_Cref

Log-normal CV for reference or target catch level. Vector of length nsim. Positive real numbers

Bref

Reference or target biomass level (eg BMSY). Vector of length nsim. Positive real numbers

CV_Bref

Log-normal CV for reference or target biomass level. Vector of length nsim. Positive real numbers

Iref

Reference or target relative abundance index level (eg BMSY / B0). Vector of length nsim. Positive real numbers

CV_Iref

Log-normalCV for reference or target relative abundance index level. Vector of length nsim. Positive real numbers

t

The number of years corresponding to AvC and Dt. Single value. Positive integer

AvC

Average catch over time t. Vector nsim long. Positive real numbers

CV_AvC

Coefficient of variation in average catches over time t. Vector nsim long. Positive real numbers

Dt

Depletion over time t SSB(now)/SSB(now-t+1). Vector nsim long. Fraction

CV_Dt

Coefficient of variation in depletion over time t. Vector nsim long. Positive real numbers

Ref

A reference management level (eg a catch limit). Single value. Positive real number

Ref_type

Type of reference management level (eg 2009 catch limit). Single value. Character string

Log

A record of events. Single value. Character string

params

A place to store estimated parameters. An object. R list

PosMPs

The methods that can be applied to these data. Vector. Character strings

TAC

The calculated catch limits (function TAC). An array with dimensions PosMPs x replicate TAC samples x nsim. Positive real numbers

Sense

The results of the sensitivity analysis (function Sense). An array with dimensions PosMPs x sensitivity increments. Positive real numbers

MPs

The methods that were applied to these data. Vector. Character strings

OM

A table of operating model conditions. R table object of nsim rows. Real numbers

Obs

A table of observation model conditions. R table object of nsim rows. Real numbers

Misc

Other information for MPs. An object. R list

Objects from the Class

Objects can be created by calls of the form new('Data', stock)

Author(s)

T. Carruthers and A. Hordyk

Examples

newdata<-new('Data')

Description

A function that writes a correctly formatted .csv file from a MSEtool Data object

Usage

Data2csv(Data, file = NULL, simno = 1, overwrite = F, keepNAs = T)

Arguments

Author(s)

T. Carruthers

Description

A data.frame with description of slots for class Data

Usage

DataDescription

Format

An object of class data.frame with 94 rows and 2 columns.

Description

A way of locating where the package was installed so you can find example data files and code etc.

Usage

DataDir(stock = NA)

Arguments

Value

The file path to the object

Author(s)

T. Carruthers

Examples

## Not run: 
tilefish_location <- DataDir("Gulf_blue_tilefish")
tilefish_Data <- new("Data", tilefish_location)

## End(Not run)

Description

Creates template for the Data input file (Excel or CSV) and Data documentation file (Markdown) in the working directory or the directory specified by the dir argument

Usage

DataInit(name = "Data", ext = c("xlsx", "csv"), overwrite = FALSE, dir = NULL)

Arguments

Value

Nothing. Creates template data files in the working directory.

Author(s)

A. Hordyk

Examples

## Not run: 
DataInit("Example") # populated example
DataInit("myData") # empty template

## End(Not run)

Description

Dataframe with details of slots in Dat object

Usage

DataSlots

Format

An object of class tbl_df (inherits from tbl, data.frame) with 101 rows and 4 columns.

Description

Example objects of class Fleet

Usage

DecE_Dom

DecE_HDom

DecE_NDom

FlatE_Dom

FlatE_HDom

FlatE_NDom

Generic_DecE

Generic_FlatE

Generic_Fleet

Generic_IncE

IncE_HDom

IncE_NDom

Low_Effort_Non_Target

Target_All_Fish

Targeting_Small_Fish

Format

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

An object of class Fleet of length 1.

Examples

avail("Fleet")

Description

A plot of biomass relative to BMSY over projected years

Usage

DFO_bar(MSEobj, yres = 10)

Arguments

Author(s)

T. Carruthers

Description

A plot of current and historical stock status by simulation according to the stock status zones and reference points of DFO. http://www.dfo-mpo.gc.ca/reports-rapports/regs/sff-cpd/precaution-eng.htm

Usage

DFO_hist(OM, panel = T, nsim = 48)

Arguments

Author(s)

T. Carruthers

Description

A plot of mean biomass relative to BMSY and fishing mortality rate relative to FMSY over the final 5 years of the projection http://www.dfo-mpo.gc.ca/reports-rapports/regs/sff-cpd/precaution-eng.htm

Usage

DFO_plot(MSEobj, zero_origin = T)

Arguments

Author(s)

T. Carruthers

Description

A preliminary plot for returning trade-offs plots and performance table for probability of obtaining half reference (FMSY) yield and probability of biomass dropping below 50 per cent BMSY

Usage

DFO_plot2(MSEobj, nam = NA, panel = T, Bcut = 50, Ycut = 50)

Arguments

Value

A table of performance metrics.

Author(s)

T. Carruthers

Description

A projection plot of MP performance by simulation according to the stock status zones and reference points of DFO. http://www.dfo-mpo.gc.ca/reports-rapports/regs/sff-cpd/precaution-eng.htm

Usage

DFO_proj(MSEobj, maxplot = 6)

Arguments

Author(s)

T. Carruthers

Description

A plot of biomass relative to BMSY quantiles over projected years

Usage

DFO_quant(
  MSEobj,
  maxcol = 6,
  qcol = rgb(0.4, 0.8, 0.95),
  lcol = "dodgerblue4",
  curyr = 2018,
  quants = c(0.05, 0.25, 0.75, 0.95),
  addline = T,
  forreport = T
)

Arguments

Author(s)

T. Carruthers

Description

Provides performance plots typical in the assessment of Canadian fish stocks.

Usage

DFO_report(
  MSEobj,
  output_file = NA,
  author = "Author not specified",
  title = NA,
  maxMPs = 15
)

Arguments

Author(s)

T. Carruthers

Description

DFO performance spider plot (top three MPs)

Usage

DFO_spider(MSEobj)

Arguments

Author(s)

T. Carruthers

Description

P_Cr_S is the probability of being in the critical zone in the first 10 projected years P_Ct_S is the probability of being in the cautious zone in the first 10 projected years P_H_S is the probability of being in the healthy zone in the first 10 projected years POF_S is the probability of overfishing in the first 10 projected years STY is the mean yield relative to FMSY management over the first 10 projected years P_Cr_L is the probability of being in the critical zone in the last 10 projected years P_Ct_L is the probability of being in the cautious zone in the last 10 projected years P_H_L is the probability of being in the healthy zone in the last 10 projected years POF_L is the probability of overfishing in the last 10 projected years LTY is the mean yield relative to FMSY management over the last 10 projected years AAVY is the average annual variability in yield over the whole projection phrased as a CV percentage P_Reb is the probability the stock has rebuilt to over BMSY in 2 mean generation times

Usage

DFO_tab(MSEobj, maxMPs = 15, rnd = 0)

Arguments

Author(s)

T. Carruthers

Description

Crit_S is the probability of being in the critical zone in the first 10 projected years Caut_S is the probability of being in the cautious zone in the first 10 projected years Health_S is the probability of being in the healthy zone in the first 10 projected years OvFish_S is the probability of overfishing in the first 10 projected years Yield_S is the mean yield relative to FMSY management over the first 10 projected years Crit is the probability of being in the critical zone in the last 10 projected years Caut is the probability of being in the cautious zone in the last 10 projected years Health is the probability of being in the healthy zone in the last 10 projected years OvFish is the probability of overfishing in the last 10 projected years Yield is the mean yield relative to FMSY management over the last 10 projected years AAVY is the average annual variability in yield over the whole projection phrased as a CV percentage Reb is the probability the stock has rebuilt to over BMSY in 2 mean generation times

Usage

DFO_tab_formatted(
  Ptab1,
  thresh = c(30, 50, 40, 60, 50, 20, 40, 50, 60, 50, 30, 50),
  ret_thresh = F
)

Arguments

Author(s)

T. Carruthers

Description

Directory of the installed package on your computer

Usage

DLMDataDir(stock = NA)

Arguments

Value

The file path to the object

Description

Double-normal selectivity curve

Usage

dnormal(lens, lfs, sl, sr)

Arguments

Description

A hockey stick (2 inflection points) HCR that accepts estimated level relative to reference level and modifies a proposed TAC based on control points for the x axis (est/ref) and y axis (fraction of TAC)

Usage

doHCR(trial_TAC, est, ref, CP = c(0, 1), CPy = c(0, 1))

Arguments

Value

A real number (TAC advice but theoretically could be used for effort, size limits etc).

Author(s)

T. Carruthers

Description

Given an index (historical period and projected period) this function creates sparsity in the projected index to simulate varying frequency (intensity) of data collection.

Usage

doIfreq(I_hist, I_freq, LHYr, CurYr, Year)

Arguments

Value

A thinned ector I_hist long of index observations.

Author(s)

T. Carruthers

Description

MPs that perform worse than comparable MPs across all performance metrics are considered 'dominated' as other options are always preferable.

Usage

Dom(MSEobj, ..., PMlist = NULL, Refs = NULL, Yrs = NULL)

Arguments

Details

The Dom function compares the probabilities calculated in the performance metric (PM) functions and determines the MPs that have a lower probability across all PMs compared to other MPs of the same management type (e.g., size limit, TAC, etc). Consequently, it is important that all PM functions are constructed so that higher probabilities = better performance (e.g, PNOF is the probability of NOT overfishing)

Value

A named list of length 2 with a character vector of non-dominated MPs in MPs and a data.frame of dominated MPs and the names of the relevant dominated MPs in DomMPs

Author(s)

A. Hordyk

Examples

## Not run: 
MSE <- runMSE(MPs=NA) # run all MPs
Nondom <- Dom(MSE, "P10", "LTY", "PNOF")
# Non-dominated MPs
Nondom$MPs

# Dominated MPs
Nondom$DomMPs


## End(Not run)

Description

Creates a TAC management recommendation given constraints on how much that can change from previous TAC and contraints on minimum and maximum TAC

Usage

doRec(MPrec, mod, delta_down, delta_up, TACrng)

Arguments

Value

n object of class Rec.

Author(s)

T. Carruthers

Description

An all-purpose empirical MP that runs of Indices of relative abundance

Usage

Emp(
  x,
  Data,
  reps = 1,
  Inds = NA,
  I_freq = NA,
  I_wt = NA,
  calib_yrs = 2,
  enp_mult = 0.3,
  Ind_fac = NA,
  TACrng = NA,
  delta_down = c(0.01, 0.5),
  delta_up = c(0.01, 0.5),
  resp = 1,
  curI_2_target = NA,
  HCR_CP_B = c(0, 0),
  HCR_CP_TAC = c(0, 1),
  Mode = 1
)

Arguments

Value

An object of class MP.

Author(s)

T. Carruthers

Description

What MPs may be run (best case scenario) for various data-availability scenarios and management constraints?

Usage

Fease(
  Data = NULL,
  TAC = TRUE,
  TAE = TRUE,
  SL = TRUE,
  Spatial = TRUE,
  names.only = TRUE,
  msg = TRUE,
  include.ref = FALSE
)

Arguments

Value

Either a vector of MP names that are feasible for the fishery (default) or a 3 column data frame (names.only=FALSE).

Author(s)

T. Carruthers & A. Hordyk

Examples

## Not run: 
Fease(TAC=FALSE)
Fease(SL=FALSE, Spatial=FALSE)
Fease(Atlantic_mackerel, TAE=FALSE, names.only=FALSE)

## End(Not run)

Description

The component of the operating model that controls fishing dynamics

Slots

Name

Identifying name for the fleet. Usually includes location and gear type.

nyears

The number of years for the historical simulation. Single value. For example, if the simulated population is assumed to be unfished in 1975 and this is the year you want to start your historical simulations, and the most recent year for which there is data available is 2019, then nyears equals 45.

CurrentYr

The last historical year simulated before projections begin. Single value. Note that this should match the last historical year specified in the Data object, which is usually the last historical year for which data is available.

EffYears

Vector indicating the historical years where there is information available to infer the relative fishing effort expended.This vector is specified in terms of the position of the year in the vector rather than the calendar year. For example, say our simulation starts with an unfished stock in 1975,and the current year (the last year for which there is data available) is 2019. Then there are 45 historical years simulated, and EffYears should include numbers between 1 and 45. Note that there may not be information available for every historical year, especially for data poor fisheries. In these situations, the EffYears vector should include only the positions of the years for which there is information, and the vector may be shorter than the total number of simulated historical years (nyears).

EffLower

Lower bound on relative fishing effort corresponding to EffYears. EffLower must be a vector that is the same length as EffYears describing how fishing effort has changed over time. Information on relative fishing effort can be entered in any units provided they are consistent across the entire vector because the data provided will be scaled to 1 (divided by the maximum number provided).

EffUpper

Upper bound on relative fishing effort corresponding to EffYears. EffUpper must be a vector that is the same length as EffYears describing how fishing effort has changed over time. Information on relative fishing effort can be entered in any units provided they are consistent across the entire vector because the data provided will be scaled to 1 (divided by the maximum number provided).

Esd

Additional inter-annual variability in fishing mortality rate. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. If this parameter has a positive (non-zero) value, the yearly fishing mortality rate is drawn from a log-normal distribution with a standard deviation (in log space) specified by the value of Esd drawn for that simulation. This parameter applies only to historical projections.

qinc

Mean temporal trend in catchability (also though of as the efficiency of fishing gear) parameter, expressed as a percentage change in catchability (q) per year. For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. Positive numbers indicate an increase and negative numbers indicate a decrease. q then changes by this amount for in each year of the simulation This parameter applies only to forward projections.

qcv

Inter-annual variability in catchability expressed as a coefficient of variation. For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This parameter applies only to forward projections.

L5

Shortest length at which 5% of the population is vulnerable to selection by the gear used in this fleet. Values can either be specified as lengths (in the same units used for the maturity and growth parameters in the stock object) or as a percentage of the size of maturity (see the parameter isRel for more information). For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the same in all years unless cpars is used to provide time-varying selection.

LFS

Shortest length at which 100% of the population is vulnerable to selection by the gear used by this fleet. Values can either be specified as lengths (in the same units used for the maturity and growth parameters in the stock object) or as a percentage of the size of maturity (see the parameter isRel for more information). For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the same in all years unless cpars is used to provide time-varying selection.

Vmaxlen

Proportion of fish selected by the gear at the asymptotic length (Stock@Linf). Upper and Lower bounds between 0 and 1. A value of 1 indicates that 100% of fish are selected at the asymptotic length, and the selection curve is logistic. If Vmaxlen is less than 1 the selection curve is dome shaped. For example, if Vmaxlen is 0.4, then only 40% of fish are vulnerable to the fishing gear at the asymptotic length.

isRel

Specify whether selection and retention parameters use absolute lengths or relative to the size of maturity. Single logical value (TRUE or FALSE).

LR5

Shortest length at which 5% of the population is vulnerable to retention by the fleet. Values can either be specified as lengths (in the same units used for the maturity and growth parameters in the stock object) or as a percentage of the size of maturity (see the parameter isRel for more information). For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the same in all years unless cpars is used to provide time-varying selection.

LFR

Shortest length where 100% of the population is vulnerable to retention by the fleet. Values can either be specified as lengths (in the same units used for the maturity and growth parameters in the stock object) or as a percentage of the size of maturity (see the parameter isRel for more information). For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the same in all years unless cpars is used to provide time-varying selection.

Rmaxlen

Proportion of fish retained at the asymptotic length (Stock@Linf). Upper and Lower bounds between 0 and 1. A value of 1 indicates that 100% of fish are retained at the asymptotic length, and the selection curve is logistic. If Rmaxlen is less than 1 the retention curve is dome shaped. For example, if Rmaxlen is 0.4, then only 40% of fish at the asymptotic length are retained.

DR

Discard rate, defined as the proportion of fully selected fish that are discarded by the fleet. Upper and Lower bounds between 0 and 1, with a value of 1 indicates that 100% of selected fish are discarded. For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided.

Spat_targ

Distribution of fishing in relation to vulnerable biomass (VB) across areas. The distribution of fishing effort is proportional to VB^Spat_targ. Upper and lower bounds of a uniform distribution. For each simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This parameter allows the user to model either avoidance or spatial targeting behavior by the fleet. If the parameter value is 1, fishing effort is allocated across areas in proportion to the population density of that area. Values below 1 simulate an avoidance behavior and values above 1 simulate a targeting behavior.

MPA

Logical argument (TRUE or FALSE). Creates an MPA in Area 1 for all years if true is selected. Defaults to FALSE.

Misc

Miscellaneous list for bio-economic parameters

Creating Object

Objects can be created by calls of the form new('Fleet')

Author(s)

T. Carruthers and A. Hordyk

Examples

showClass('Fleet')

Description

A data.frame with description of slots for class Fleet

Usage

FleetDescription

Format

An object of class data.frame with 20 rows and 2 columns.

Description

Several reference MPs for your operating model to use in the management strategy evaluation. FMSYref (and related) assume perfect information about FMSY (FMSY is taken from the operating model stored at Data@Misc$ReferencePoints$ByYear$FMSY), and set an effort limit (TAE) so that F=FMSY (or some fraction of FMSY) in each year the MP is applied. NFref sets annual catch to zero and is used for looking at variability in stock with no fishing.

Usage

FMSYref(x, Data, reps = 100, plot = FALSE)

FMSYref50(x, Data, reps = 100, plot = FALSE)

FMSYref75(x, Data, reps = 100, plot = FALSE)

NFref(x, Data, reps = 100, plot = FALSE)

curEref(x, Data, reps = 100, plot = FALSE)

Arguments

Details

Note that you can out-perform FMSYref easily. The requirement for fixed F is actually quite strict and is by no means the upper limit in terms of yield. Don't panic if your method beats this one for yield, especially for short-lived species of high temporal variability in productivity!

Value

An object of class Rec with the TAC slot populated with a numeric vector of length reps

Functions

• FMSYref(): A reference FMSY method that fishes at FMSY

• FMSYref50(): A reference FMSY method that fishes at 50% of FMSY

• FMSYref75(): A reference FMSY method that fishes at 75% of FMSY

• NFref(): A reference MP that sets annual catch to almost zero (1e-15)

• curEref(): A reference MP that keeps fishing effort at the level of the last historical year

Required Data

See Data for information on the Data object

Author(s)

T. Carruthers, A. Hordyk

Examples

FMSYref(1, MSEtool::SimulatedData, plot=TRUE)
FMSYref50(1, MSEtool::SimulatedData, plot=TRUE)
FMSYref75(1, MSEtool::SimulatedData, plot=TRUE)
NFref(1, MSEtool::SimulatedData, plot=TRUE)
curEref(1, MSEtool::SimulatedData)

Description

Example objects of class Obs

Usage

Generic_Obs

Imprecise_Biased

Imprecise_Unbiased

Perfect_Info

Precise_Biased

Precise_Unbiased

Format

An object of class Obs of length 1.

An object of class Obs of length 1.

An object of class Obs of length 1.

An object of class Obs of length 1.

An object of class Obs of length 1.

An object of class Obs of length 1.

Examples

avail("Obs")

Description

Internal function for determining if object is of classy

Usage

getclass(x, classy)

Arguments

Value

TRUE or FALSE

Author(s)

T. Carruthers with nasty hacks from A. Hordyk

Description

Get part of an MP specific data-list

Usage

getDataList(MSElist, mm)

Arguments

Value

a sublist of MSElist for a specific MP

Description

Extract the first dimension of a hierarchical list of recommendation objects

Usage

getfirstlev(x, name, pp, ff)

Arguments

Author(s)

T. Carruthers

Description

The user specifies the probability of staying in the same area and spatial heterogeneity (both in the unfished state).

Usage

getmov2(x, Prob_staying, Frac_area_1)

Arguments

Details

This is paired with movfit to find the correct movement model.

Value

A markov movement matrix

Author(s)

T. Carruthers

Examples

Prob_staying<-0.8 # probability  that individuals remain in area 1 between time-steps
Frac_area_1<-0.35 # the fraction of the stock found in area 1 under equilibrium conditions
markovmat<-getmov2(1,Prob_staying, Frac_area_1)
vec<-c(0.5,0.5) # initial guess at equilibrium distribution (2 areas)
for(i in 1:300)vec<-apply(vec*markovmat,2,sum) # numerical approximation to stable distribution
c(markovmat[1,1],vec[1]) # pretty close right?

Description

Calls dynGet(), then get() in order to find the MP definition in the R session.

Usage

getMP(MP)

Arguments

Value

The function definition or an error message from try() if unsuccessful

Author(s)

Q. Huynh

Description

Count independent variables for a MICE relationship at position x in a Rel list

Usage

getnIVs(x, Rel)

Arguments

Author(s)

T.Carruthers

Description

Calculate selectivity curve

Usage

getsel(x, lens, lfs, sls, srs)

Arguments

Description

Convert stock recruit parameters from steepness parameterization to alpha/beta (and vice versa)

Usage

hconv(alpha, phi0, SR = 1, type = 1)

R0conv(alpha, beta, phi0, SR = 1, type = 1)

SRalphaconv(h, phi0, SR = 1, type = 1)

SRbetaconv(h, R0, phi0, SR = 1, type = 1)

Arguments

Details

The Type 1 Beverton-Holt equation is

$R = \alpha S/(1 + \beta S)$

The Type 2 Beverton-Holt equation is

$R = S/(\alpha + \beta S)$

The Ricker equation is

$R = \alpha S \exp(-\beta S)$

Value

A numeric.

Functions

• hconv(): Returns steepness (h) from alpha and phi0

• R0conv(): Returns unfished recruitment (R0) from alpha, beta, and phi0

• SRalphaconv(): Returns alpha from h and phi0

• SRbetaconv(): Returns beta from h, R0, and phi0

Author(s)

Q. Huynh

Description

• expandHerm expands the Herm list in SexPars to a matrix of fractions at age

• checkHerm checks that each array in the list has dimension nsim x maxage+1 x nyears + proyears. For backwards compatibility, also converts matrices to arrays by adding the year dimension.

• subsetHerm returns year-specific Herm values.

Usage

expandHerm(Herm, maxage, np, nsim)

checkHerm(Herm, maxage, nsim, nyears, proyears)

subsetHerm(Herm, y)

Arguments

Author(s)

T. Carruthers

Q. Huynh

Description

An object for storing information generated by the end of the historical simulations

Slots

Data

The Data object at the end of the historical period

OMPars

A numeric data.frame with nsim rows with sampled Stock, Fleet, Obs, and Imp parameters.

AtAge

A named list with arrays of dimensions: c(nsim, maxage+1, nyears+proyears) or c(nsim, maxage+1, nyears, nareas)

• Length: Length-at-age for each simulation, age, and year

• Weight: Weight-at-age for each simulation, age, and year

• Select: Selectivity-at-age for each simulation, age, and year

• Retention: Retention-at-age for each simulation, age, and year

• Maturity: Maturity-at-age for each simulation, age, and year

• N.Mortality: Natural mortality-at-age for each simulation, age, and year

• Z.Mortality: Total mortality-at-age for each simulation, age, year and area

• F.Mortality: Fishing mortality-at-age for each simulation, age, year and area

• Fret.Mortality: Fishing mortality-at-age for retained fish for each simulation, age, year and area

• Number: Total numbers by simulation, age, year and area

• Biomass: Total biomass by simulation, age, year and area

• VBiomass: Vulnerable biomass by simulation, age, year and area

• SBiomass: Spawning biomass by simulation, age, year and area

• Removals: Removals (biomass) by simulation, age, year and area

• Landings: Landings (biomass) by simulation, age, year and area

• Discards: Discards (biomass) by simulation, age, year and area

TSdata

A named list with population and fleet dynamics:

• Number: Total numbers; array dimensions c(nsim, nyears, nareas)

• Biomass: Total biomass; array dimensions c(nsim, nyears, nareas)

• VBiomass: Vulnerable biomass; array dimensions c(nsim, nyears, nareas)

• SBiomass: Spawning Biomass; array dimensions c(nsim, nyears, nareas)

• Removals: Removals (biomass); array dimensions c(nsim, nyears, nareas)

• Landings: Landings (biomass); array dimensions c(nsim, nyears, nareas)

• Discards: Discards (biomass); array dimensions c(nsim, nyears, nareas)

• Find: Historical fishing mortality (scale-free); matrix dimensions c(nsim, nyears)

• RecDev: Recruitment deviations (historical and projection); matrix dimensions c(nsim, nyears+proyears+maxage)

• SPR: Named list with Equilibrium and Dynamic SPR (both matrices iwth dimensions c(nsim, nyears))

• Unfished_Equilibrium: A named list with unfished equilibrium numbers and biomass-at-age

Ref

A named list with biological reference points:

• ByYear: A named list with asymptotic reference points (i.e., calculated annually without recruitment deviations) all matrices with dimensions nsim by nyears+proyears:

  • N0: Asymptotic unfished total number

  • SN0: Asymptotic unfished spawning number

  • B0: Asymptotic unfished total biomass

  • SSB0: Asymptotic unfished spawning biomass

  • VB0: Asymptotic unfished vulnerable biomass

  • MSY: Asymptotic MSY

  • FMSY: Fishing mortality corresponding with asymptotic MSY

  • SSBMSY: Spawning stock biomass corresponding with asymptotic MSY

  • BMSY: total biomass corresponding with asymptotic MSY

  • VBMSY: Vulnerable biomass corresponding with asymptotic MSY

  • F01: Fishing mortality where the change in yield per recruit is 10% of that at F = 0

  • Fmax: Fishing mortality that maximizes yield per recruit

  • F_SPR: Fishing mortality corresponding to spawning potential ratio of 20 - 60% in increments of 5%; array dimensions c(nsim, 9, nyears+proyears)

  • Fcrash: Fishing mortality corresponding to the recruits-per-spawner at the origin of the stock-recruit relationship

  • Fmed: Fishing mortality corresponding to the median recruits-per-spawner in the historical period

  • SPRcrash: SPR corresponding to the recruits-per-spawner at the origin of the stock-recruit relationship

• Dynamic_Unfished: A named list with dynamic unfished reference points for each simulation and year:

  • N0: Unfished total numbers

  • B0: Unfished total biomass

  • SN0: Unfished spawning numbers

  • SSB0: Unfished spawning biomass

  • VB0: Unfished vulnerable biomass

  • Rec: Unfished recruitment

• ReferencePoints: A data.frame with nsim rows with with biological reference points calculated as an average over age-of-maturity ageM years around the current year (i.e. nyears):

  • N0: Average unfished numbers

  • B0: Average unfished biomass

  • SSB0: Average unfished spawning biomass (used to calculate depletion)

  • SSN0: Average unfished spawning numbers

  • VB0: Average unfished vulnerable biomass (used to calculate depletion if cpar$control$D='VB')

  • MSY: Average maximum sustainable yield (equilibrium)

  • FMSY: Average fishing mortality corresponding with MSY

  • SSBMSY: Average spawning stock biomass corresponding with MSY

  • BMSY: Average total biomass corresponding with MSY

  • VBMSY: Average vulnerable biomass corresponding with MSY

  • UMSY: Average exploitation rate corresponding with MSY

  • FMSY_M: Average FMSY/M ratio

  • SSBMSY_SSB0: Average ratio of SSBMSY to SSB0

  • BMSY_B0: Average ratio of BMSY to B0

  • VBMSY_VB0: Average ratio of VBMSY to VB0

  • RefY: Maximum yield obtained in forward projections with a fixed F

SampPars

A named list with all sampled Stock, Fleet, Obs, and Imp parameters

OM

The OM object (without cpars)

Misc

A list for additional information

Author(s)

A. Hordyk

Description

Produces a blank plot if all values in x are equal

Usage

hist2(x, col, axes = FALSE, main = "", breaks = 10, cex.main = 1)

Arguments

Description

A data.frame with description of slots for class Hist

Usage

HistDescription

Format

An object of class data.frame with 76 rows and 2 columns.

Description

An operating model component that specifies the degree of adherence to management recommendations (Implementation error)

Slots

Name

The name of the Implementation error object. Single value. Character string.

Name

The name of the Implementation error object. Single value. Character string.

TACFrac

Mean fraction of recommended TAC that is actually taken. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the mean TAC fraction obtained across all years of that simulation, and a yearly TAC frac is drawn from a log-normal distribution with the simulation mean and a coefficient of variation specified by the value of TACSD drawn for that simulation. If the value drawn is greater than 1 the amount of catch taken is greater than that recommended by the TAC, and if it is less than 1 the amount of catch taken is less than that recommended by the TAC. Positive real numbers.

TACSD

Log-normal coefficient of variation in the fraction of recommended TAC that is actually taken. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is used, along with the TACFrac drawn for that simulation, to create a log-normal distribution that yearly values specifying the actual amount of catch taken are drawn from. Positive real numbers.

TAEFrac

Mean fraction of recommended TAE that is actually taken. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the mean TAE fraction obtained across all years of that simulation, and a yearly TAE frac is drawn from a log-normal distribution with the simulation mean and a coefficient of variation specified by the value of TAESD drawn for that simulation. If the value drawn is greater than 1 the amount of effort employed is greater than that recommended by the TAE, and if it is less than 1 the amount of effort employed is less than that recommended by the TAE. Positive real numbers.

TAESD

Log-normal coefficient of variation in the fraction of recommended TAE that is actually taken. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is used, along with the TAEFrac drawn for that simulation, to create a log-normal distribution that yearly values speciying the actual amount of efort employed are drawn from. Positive real numbers.

SizeLimFrac

Mean fraction of recommended size limit that is actually retained. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is the mean size limit fraction obtained across all years of that simulation, and a yearly size limit fraction is drawn from a log-normal distribution with the simulation mean and a coefficient of variation specified by the value of SizeLimSD drawn for that simulation. If the value drawn is greater than 1 the size of fish retained is greater than that recommended by the size limit, and if it is less than 1 the amount of size of fish retained is less than that recommended by the size limit. Positive real numbers.

SizeLimSD

Log-normal coefficient of variation in the fraction of recommended size limit that is actually retained. For each historical simulation a single value is drawn from a uniform distribution specified by the upper and lower bounds provided. This value is used, along with the SizeLimFrac drawn for that simulation, to create a log-normal distribution that yearly values speciying the actual fraction of the size limit retained are drawn from. Positive real numbers.

Objects from the Class

Objects can be created by calls of the form new('Imp')#'

Author(s)

T. Carruthers and A. Hordyk

Examples

showClass('Imp')

Description

A data.frame with description of slots for class Imp

Usage

ImpDescription

Format

An object of class data.frame with 7 rows and 2 columns.

Description

~~ Methods for Function initialize ~~

Methods

list('signature(.Object = \'DLM\')')

%% ~~describe this method here~~

list('signature(.Object = \'Fleet\')')

%% ~~describe this method here~~

list('signature(.Object = \'MSE\')')

%% ~~describe this method here~~

list('signature(.Object = \'Obs\')')

%% ~~describe this method here~~

list('signature(.Object = \'OM\')')

%% ~~describe this method here~~

list('signature(.Object = \'Stock\')')

%% ~~describe this method here~~

list('signature(.Object = \'Fease\')')

%% ~~describe this method here~~

list('signature(.Object = \'DLM_general\')')

%% ~~describe this method here~~

Description

Runs a set of input control methods are returns the output in a single table

Usage

Input(Data, MPs = NA, reps = 100, timelimit = 10, CheckMPs = TRUE, msg = TRUE)

Arguments

Author(s)

A. Hordyk

Examples

## Not run: 
library(MSEtool)
Input(MSEtool::Cobia)

## End(Not run)

Description

Internal functions for reading iSCAM input and output files into R

Usage

load.iscam.files(model.dir, burnin = 1000, thin = 1, verbose = FALSE)

fetch.file.names(path, filename)

read.report.file(fn)

read.data.file(file = NULL, verbose = FALSE)

read.control.file(
  file = NULL,
  num.gears = NULL,
  num.age.gears = NULL,
  verbose = FALSE
)

read.projection.file(file = NULL, verbose = FALSE)

read.par.file(file = NULL, verbose = FALSE)

read.mcmc(model.dir = NULL, verbose = TRUE)

Arguments

Functions

• load.iscam.files(): Wrapper function to generate R list

• fetch.file.names(): A function for returning the three types of iSCAM input and output files

• read.report.file(): A function for returning the results of the .rep iscam file

• read.data.file(): A function for returning the results of the .dat iscam file

• read.control.file(): A function for returning the results of the iscam control file

• read.projection.file(): A function for returning the results of the iscam projection file

• read.par.file(): A function for returning the results of the iscam .par file

• read.mcmc(): A function for returning the results of the iscam mcmc files

Author(s)

Chris Grandin (DFO PBS)

See Also

iSCAM2OM

Description

Functions for importing an iSCAM assessment. From a fitted model, iSCAM2OM populates the various slots of an operating model and iSCAM2Data generates a Data object. These functions rely on several functions written by Chris Grandin (DFO PBS).

Usage

iSCAM2OM(
  iSCAMdir,
  nsim = 48,
  proyears = 50,
  mcmc = FALSE,
  spawn_time_frac = 0,
  Name = "iSCAM model",
  Source = "No source provided",
  length_timestep = 1,
  nyr_par_mu = 2,
  Author = "No author provided",
  report = FALSE,
  silent = FALSE
)

iSCAM2Data(
  iSCAMdir,
  Name = "iSCAM assessment",
  Source = "No source provided",
  length_timestep = 1,
  Author = "No author provided"
)

Arguments

Biological parameters

The function calls model <- load.iscam.files(iSCAMdir) and grabs the following matrices:

• model$mpd$d3_weight_mat - fecundity (product of weight and maturity at age)

• model$mpd$ma - maturity at age

MPD historical reconstruction

The function calls model <- load.iscam.files(iSCAMdir) and then grabs the following matrices:

• model$mpd$N - abundance at age

• model$mpd$F - fishing mortality at age

• model$mpd$M - natural mortality at age

If a delay-difference model is recognized, then the following is used instead:

• model$mpd$F_dd - fishing mortality at age

• model$mpd$M_dd - natural mortality at age

Abundance at age is reconstructed using model$mpd$rt (recruitment) and projected with F_dd and M_dd to match model$mpd$numbers.

MCMC historical reconstruction

If mcmc = TRUE the function calls mcmc_model <- read.mcmc(iSCAMdir), and grabs nsim sub-samples of the MCMC output through the following arrays:

• mcmc_model$params and mcmc_model$ft - fishing mortality at age from the fleet selectivity parameters and apical F's

• mcmc_model$m - year-specific natural mortality at age

• mcmc_model$params$rinit and mcmc_model$rt - recruitment and abundance

Start age

While the iSCAM start age can be greater than zero, abundance at age is back-calculated to age zero with M, maturity, growth = 0. In this way, the stock-recruit dynamics from iSCAM are preserved.

These arrays are then passed to Assess2OM to generate the operating model.

Reference points

iSCAM calculates the stock-recruit relationship and subsequently a single set of MSY and unfished reference points using R0, steepness, and unfished spawners per recruit from the mean M, fecundity, and growth (mean with respect to time).

R0 and h are recalculated for the operating model by obtaining the stock-recruit alpha and beta from the iSCAM parameters and the mean unfished spawners per recruit in the first ageM (age of 50% maturity) years.

Author(s)

T. Carruthers, Q. Huynh

Description

iSCAM assessments are often fitted to numerous fleets that have differing age selectivities. iSCAMcomps is a simple way of providing the aggregate catch at age data. It should be noted that this process is important and in a real application would require due diligence (ie peer reviewed data workshop).

Usage

iSCAMcomps(replist, Year)

Arguments

Author(s)

T. Carruthers

Description

iSCAM assessments often make use of multiple indices of abundance. The data object and MPs currently only make use of a single index. combiSCAMinds is a function that creates a single index from many using linear modelling. It is a simple way of providing initial calculations of management recommendations and it should be noted that this process is important and in a real application would require due diligence (ie peer reviewed data workshop).

Usage

iSCAMinds(idata, Year, fleeteffect = T)

Arguments

Author(s)

T. Carruthers

Description

Plot several plots with a shared legend

Usage

join_plots(
  plots,
  ncol = length(plots),
  nrow = 1,
  position = c("right", "bottom"),
  legend = TRUE
)

Arguments

Note

modified from https://github.com/tidyverse/ggplot2/wiki/share-a-legend-between-two-ggplot2-graphs

Description

A function that combined a list of data objects into a single data object (same dimensions but can have different numbers of simulations)

Usage

joinData(DataList)

Arguments

Author(s)

T. Carruthers

See Also

joinMSE joinHist

Description

A standard KOBE plot by each method that also shows the percentage of methods that ended up in each quadrant.

Usage

Kplot(
  MSEobj,
  maxsim = 60,
  MPs = NA,
  sims = NULL,
  maxMP = 9,
  nam = NA,
  cex.leg = 1.5
)

Arguments

Note

Apologies for the nauseating shading.

Author(s)

T. Carruthers with some additions from A. Hordyk

Description

Lag the time-series slots in a Data object by a specified number of time-steps

Usage

Lag_Data(Data, Data_Lag = 0, msg = FALSE)

Arguments

Details

By default, all simulated data in the forward projections are provided up to the previous time-step. That is, in projection year t, the simulated data are up to and including t-1. This function will lag the time-series values by the specified value. For example, Data_Lag=5 will mean in projection time-step t the data will be up to and including t-6.

Note: The Data@Year slot is not lagged by this function. Many built-in MPs use the length of Data@Year to determine the number of years of data for smoothing over recent years etc. This may not be appropriate so check the MP is behaving as you expect if you use Lag_Data.

Value

An object of class Data with time-series slots lagged.

Examples

# Lag all time-series slots by 2 time-steps (usually years)
Data <- Example_datafile
Lagged_1 <- Lag_Data(Data, 2)
length(Data@Year)
length(Lagged_1@Year)
length(Data@Cat[1,])
length(Lagged_1@Cat[1,])
length(Data@Ind[1,])
length(Lagged_1@Ind[1,])

# Lag CAA by 5 and Ind by 3 time-steps
Lagged_2 <- Lag_Data(Data, Data_Lag=list(CAA=5, Ind=3))
length(Lagged_2@Year)
length(Lagged_2@Cat[1,])
dim(Data@CAA[1,,])
dim(Lagged_2@CAA[1,,])

length(Data@Ind[1,])
length(Lagged_2@Ind[1,])

Description

Dimensions of a hierarchical list object

Usage

ldim(x)

Arguments

Author(s)

T. Carruthers

Description

Predict missing life-history based on taxonomic information and hierarchical model fitted to FishBase life-history parameters

Usage

LH2OM(
  OM,
  dist = c("unif", "norm"),
  filterMK = FALSE,
  plot = TRUE,
  Class = "predictive",
  Order = "predictive",
  Family = "predictive",
  msg = TRUE,
  db = MSEtool::LHdatabase
)

predictLH(
  inpars = list(),
  Genus = "predictive",
  Species = "predictive",
  nsamp = 100,
  db = MSEtool::LHdatabase,
  dist = c("unif", "norm"),
  filterMK = TRUE,
  plot = TRUE,
  Class = "predictive",
  Order = "predictive",
  Family = "predictive",
  msg = TRUE
)

Arguments

Details

filterMK

If filterMK is logical: Should the predicted M and K parameters be filtered within the range specified in inparsor OM?

Otherwise, filterMK must be numeric vector of length(2) specifying lower and upper percentiles that will be applied to the predicted M or K values

The model predicts missing life-history parameters based on provided parameters and taxonomic information. If both M and K are provided in inpars or OM, K values are predicted and predictions filtered so that resulting K values are within bounds specified in inpars$K or OM@K (see filterMK).

If both Linf and L50 are provided in inpars or OM, L50 values are predicted and values in inpars$L50 or OM@L50 are ignored.

Value

LH2OM: An OM with OM@cpars populated with OM@nsim samples of M, K, Linf and L50

predictLH: A data.frame with nsamp rows with Linf, L50, K, and M values.

Functions

• LH2OM(): Predict missing life-history and populate OM@cpars

• predictLH(): Predict missing life-history based on taxonomic information and hierarchical model fitted to FishBase life-history parameters

Author(s)

A. Hordyk

Source

https://github.com/James-Thorson-NOAA/FishLife

References

Thorson, J. T., S. B. Munch, J. M. Cope, and J. Gao. 2017. Predicting life history parameters for all fishes worldwide. Ecological Applications. 27(8): 2262–2276

Description

Database from the FishLife package with predicted life-history parameters for all species on FishBase

Usage

LHdatabase

Format

An object of class list of length 3.

Source

https://github.com/James-Thorson-NOAA/FishLife/

References

Thorson, J. T., S. B. Munch, J. M. Cope, and J. Gao. 2017. Predicting life history parameters for all fishes worldwide. Ecological Applications. 27(8): 2262–2276

Description

Converts an OM to a single stock, single fleet MOM.

Usage

makeMOM(..., silent = FALSE)

Arguments

Value

A class MOM object.

Author(s)

Q. Huynh

Examples

MOM <- makeMOM(testOM)

Description

A function for calculating a movement matrix from user specified unfished stock biomass fraction in each area. Used by simmov to generate movement matrices for an operating model.

Usage

makemov(fracs = c(0.1, 0.2, 0.3, 0.4), prob = c(0.5, 0.8, 0.9, 0.95))

Arguments

Author(s)

T. Carruthers

See Also

simmov

Description

A function for calculating a movement matrix from user specified distribution among areas (v) and relative movement to other areas (solves for positive diagonal - vector of prob staying). Used by simmov2 to generate movement matrices for an operating model. There must be a prior on the positive diagonal of the movement matrix or these will tend to 1 and hence perfectly satisfy the requirement V = MV.

Usage

makemov2(
  dist = c(0.05, 0.6, 0.35),
  prob = 0.5,
  probE = 1,
  frac_other = matrix(c(NA, 2, 1, 2, NA, 1, 1, 2, NA), nrow = 3, byrow = T),
  plot = F
)

Arguments

Author(s)

T. Carruthers

See Also

simmov2

Description

Generate a MICE Rel object, with predict and simulate methods, for multiMSE. Currently implements intra-stock dynamics via density-dependent processes.

Usage

makeRel(type = "DDM", stock = 1, CV = 0, ...)

## S3 method for class 'Rel'
print(x, ...)

## S3 method for class 'Rel'
predict(object, newdata, ...)

## S3 method for class 'Rel'
simulate(object, nsim = 1, seed = 1, ...)

Arguments

Value

A class "Rel" object to pass to MOM@Rel.

Density-dependent M ("DDM")

Natural mortality (M) is a linear function of stock depletion in terms to total biomass (B) in year y (Forrest et al. 2018):

$M_y = M_0 + (M_1 + M_0) (1 - B_y/B_0)$

with a constraint that $M_y = M_0$ if $B_y > B_0$

Provide the following arguments:

• M0: Natural mortality as B approaches B0. Vector ⁠[nsim]⁠

• M1: Natural mortality as B approaches zero. Vector ⁠[nsim]⁠

• Optional B0: Unfished biomass. Calculated from stock-recruit alpha and beta and unfished biomass per recruit at M = M0. Vector ⁠[nsim]⁠

Author(s)

Q. Huynh

References

Forrest, R., Holt, K., and Kronlund, A. 2018. Performance of alternative harvest control rules for two Pacific groundfish stocks with uncertain natural mortality: Bias, robustness and trade-offs. Fisheries Research 206: 259–286. doi:10.1016/j.fishres.2018.04.007

Examples

# Depensatory natural mortality
Rel <- makeRel(type = "DDM", M0 = 0.8, M1 = 0.2, CV = 0.1)

# Predict M when B/B0 = 0.1
pred <- predict(Rel, newdata = data.frame(B_1 = 0.1, B0_1 = 1))

# Simulate values of M with CV = 0.1
Rel$fitted.values <- pred
simulate(Rel, nsim = 10, seed = 1)

# Add Rel to MOM
MOM <- makeMOM(testOM)
MOM@Rel <- list(Rel)

Description

Make colors transparent

Usage

makeTransparent(someColor, alpha = 100)

Arguments

Author(s)

T. Carruthers

Description

A highly dubious means of getting very uncertain estimates of current stock biomass and (equilibrium) fishing mortality rate from growth, natural mortality rate, recruitment and fishing selectivity.

Usage

ML2D(OM, ML, nsim = 100, ploty = T, Dlim = c(0.05, 0.6))

Arguments

Value

An object of class 'OM' with 'D' slot populated

Author(s)

T. Carruthers

Description

A Multi Management Strategy Evaluation object that contains information about simulation conditions and performance of MPs for a multi-stock, multi-fleet operating model.

Slots

Name

Name of the MMSE object. Single value. Character string

nyears

The number of years for the historical simulation. Single value. Positive integer

proyears

The number of years for the projections - closed loop simulations. Single value. Positive integer

nMPs

Number of management procedures simulation tested. Single value. Positive integer.

MPs

The names of the MPs that were tested. Vector of length nMPs. Character strings.

MPcond

The MP condition. Character ('bystock': an MP per stock, 'byfleet' and MP per stock and fleet, 'MMP' an MP for all stocks and fleets)

MPrefs

The names of the MPs applied for each stock (row) and fleet (column). An array.

nsim

Number of simulations. Single value. Positive integer

nstocks

Number of stocks. Single value. Positive integer

nfleets

Number of fleets. Single value. Positive integer

Snames

Names of the stocks

Fnames

Names of the fleets (matrix nstocks x nfleets)

Stocks

The stock operating model objects. List of Stocks

Fleets

The fleet operating model objects. Hierarchical list, fleets nested in stocks.

Obss

The fleet specific observation error operating model objects. Hierarchical list, fleets nested in stocks.

Imps

The fleet specific implementation error operating model objects. Hierarchical list, fleets nested in stocks.

OM

A table of sampled parameters of the operating model. Data frame of nsim rows.

Obs

A table of sampled parameters of the observation model. Data frame of nsim rows.

SB_SBMSY

Simulated spawning biomass relative to SBMSY over the projection. An array with dimensions: nsim, nStocks, nMPs, proyears. Non-negative real numbers

F_FMSY

Simulated fishing mortality rate relative to FMSY over the projection. An array with dimensions: nsim, nStocks, nFleets, nMPs, proyears. Non-negative real numbers

N

Simulated stock numbers over the projection. An array with dimensions: nsim, nStocks, maxage+1, nMPs, proyears, nareas. Non-negative real numbers

B

Simulated stock biomass over the projection. An array with dimensions: nsim, nStocks, nMPs, proyears. Non-negative real numbers

SSB

Simulated spawning stock biomass over the projection. An array with dimensions: nsim, nStocks, nMPs, proyears. Non-negative real numbers

VB

Simulated vulnerable biomass over the projection. An array with dimensions: nsim, nStocks, nMPs, proyears. Non-negative real numbers

FM

Simulated fishing mortality rate over the projection. An array with dimensions: nsim, nStocks, nFleets, nMPs, proyears. Non-negative real numbers

SPR

A list of SPR values. Currently not used.

Catch

Simulated catches (landings) over the projection. An array with dimensions: nsim, nStocks, nFleets, nMPs, proyears. Non-negative real numbers

Removals

Simulated removals (landings+discards) over the projection. An array with dimensions: nsim, nStocks, nFleets, nMPs, proyears. Non-negative real numbers

Effort

Simulated relative fishing effort in the projection years. An array with dimensions: nsim, nStocks, nFleets, nMPs, proyears. Non-negative real numbers

TAC

Simulated Total Allowable Catch (prescribed) over the projection (this is NA for input controls). An array with dimensions: nsim, nStocks, nFleets, nMPs, proyears. Non-negative real numbers

TAE

Simulated Total Allowable Effort (prescribed) over the projection (this is NA for output controls). An array with dimensions: nsim, nStocks, nFleets, nMPs, proyears. Non-negative real numbers

BioEco

A named list of bio-economic output. Not currently used.

RefPoint

Named list of annual MSY reference points MSY, FMSY, and SBMSY. Array with dimensions: nsim, nstocks, nMPs, nyears+proyears. Will be the same as multiHist@Ref$ByYear unless selectivity is changed by MP

multiHist

The object of class multiHist containing information from the spool-up period.

PPD

Posterior predictive data. List of Data objects at the end of the projection period (length nMPs)

Misc

Miscellaneous output such as posterior predictive data

Objects from the Class

Objects can be created by calls of the form new('MMSE', Name, nyears, proyears, nMPs, MPs, nsim, OMtable, Obs, B_BMSYa, F_FMSYa, Ba, FMa, Ca, OFLa, Effort, PAA, CAA, CAL, CALbins)

Author(s)

T. Carruthers

Description

An object containing all the parameters needed to control a multi-stock, multi-fleet MSE which can be build from component Stock, Fleet, Obs, and Imp objects.

Details

Almost all of these inputs are a vector of length 2 which describes the upper and lower bounds of a uniform distribution from which to sample the parameter.

Slots

Name

Name of the operating model

Agency

Name of the agency responsible for the management of the fishery. Character string

Region

Name of the general geographic region of the fishery. Character string

Sponsor

Name of the organization who sponsored the OM. Character string

Latitude

Latitude (decimal degrees). Negative values represent the South of the Equator. Numeric. Single value

Longitude

Longitude (decimal degrees). Negative values represent the West of the Prime Meridian. Numeric. Single value

nsim

The number of simulations

proyears

The number of projected years

interval

The assessment interval - how often would you like to update the management system?

pstar

The percentile of the sample of the management recommendation for each method

maxF

Maximum instantaneous fishing mortality rate that may be simulated for any given age class

reps

Number of samples of the management recommendation for each method. Note that when this is set to 1, the mean value of the data inputs is used.

cpars

A hierarchical list nstock then nfleet long of custom parameters. Time series are a matrix nsim rows by nyears columns. Single parameters are a vector nsim long. See validcpars()

seed

A random seed to ensure users can reproduce results exactly

Source

A reference to a website or article from which parameters were taken to define the operating model

Stocks

List of stock objects

Fleets

List of Fleet objects

Obs

Hierarchical List of Observation model objects Level 1 is stock, level 2 is fleet

Imps

Hierarchical List of Implementation model objects Level 1 is stock, level 2 is fleet

CatchFrac

A list nstock long, of matrices nsim x nfleet representing the fraction of current catches of the various fleets to each stock (each matrix is nsim by nfleet long and rows sum to 1 for each stock)

Allocation

A list nstock long, of matrices nsim x nfleet representing the fraction of future TACs of the various fleets to each stock (each matrix is nsim by nfleet long and rows sum to 1 for each stock).

Efactor

A list nstock long, of current effort factors by fleet (default is 1 - same as current effort)

Complexes

A list of stock complexes. Each position is a vector of stock numbers (as they appear in StockPars) for which data should be aggregated and TAC recommendations split among stocks according to vulnerable biomass

SexPars

A list of slots that control sex-specific dynamics, i.e., sex-specific spawning and hermaphroditism. More generally, controls spawning and moving abundance between stocks. See details.

Rel

A list of biological / ecological relationships among stocks over-ridden if an MP of class 'MP_F" is supplied that is a multi-fleet MP.

Objects from the Class

Objects can be created by calls of the form new('MOM', Stock_list, Fleet_list, Obs_list, Imp_list).

SexPars

• SSBfrom A nstock x nstock matrix that specifies the proportion of the spawning output of the row p stock for the column p' stock. A diagonal matrix means each stock is responsible for its own recruitment.

• Herm A list with each entry containing a matrix (nsim x maxage + 1) that specifies the proportion at age that moves from stock p to p' (sequential hermaphroditism). The names of the list should be of the form "H_p'_p" where p and p' are integers that identify the stock. Arrays can also be used (nsim x maxage + 1 x nyears + proyears) for time-varying values.

• share_par Optional. Logical to indicate whether stock-recruit, depletion, and observation/implementation parameters are mirrored between stocks. By default, TRUE.

Author(s)

T. Carruthers and A. Hordyk

See Also

Article on MOM and multiMSE: https://openmse.com/features-multimse/

Description

Apply the movement model to the stock for one time-step

Usage

movestockCPP(nareas, maxage, mov, Number)

Arguments

Author(s)

A. Hordyk

Description

The user specifies the probability of staying in the same area and spatial heterogeneity (both in the unfished state). This function returns the squared difference between these values and those produced by the three logit movement model.

Usage

movfit_Rcpp(par, prb, frac)

Arguments

Details

This is paired with getmov to find the correct movement model.

Author(s)

T. Carruthers with an amateur attempt at converting to Rcpp by A. Hordyk (but it works!)

Description

Fill any NAs arising from MPCalcs (hermaphroditism mode)

Usage

MPCalcsNAs(MPCalcs)

Arguments

Author(s)

T. Carruthers

Description

Management Procedure Type

Usage

MPtype(MPs = NA)

Arguments

Value

A data.frame with MP names, management type (e.g "Input", "Output") and management recommendations returned by the MP (e.g, TAC (total allowable catch), TAE (total allowable effort), SL (size-selectivity), and/or or Spatial)

See Also

Required

Examples

MPtype(c("AvC", "curE", "matlenlim", "MRreal", "FMSYref"))

Description

A Management Strategy Evaluation object that contains information about simulation conditions and performance of data-limited methods

Slots

Name

Name of the MSE object. Single value. Character string

nyears

The number of years for the historical simulation. Single value. Positive integer

proyears

The number of years for the projections - closed loop simulations. Single value. Positive integer

nMPs

Number of management procedures simulation tested. Single value. Positive integer.

MPs

The names of the MPs that were tested. Vector of length nMPs. Character strings.

nsim

Number of simulations. Single value. Positive integer

OM

Operating model parameters (last historical year used for time-varying parameters). Data.frame with nsim rows

Obs

Observation parameters (last historical year used for time-varying parameters). Data.frame with nsim rows

SB_SBMSY

Simulated spawning biomass relative to spawning BMSY over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

F_FMSY

Simulated fishing mortality rate relative to FMSY over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

N

Simulated total numbers over the projection. An array with dimensions: nsim, maxage+1, nMPs, proyears, nareas. Non-negative real numbers.

B

Simulated stock biomass over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

SSB

Simulated spawning stock biomass over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

VB

Simulated vulnerable biomass over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

FM

Simulated fishing mortality rate over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

SPR

Named list with equilibrium and dynamic SPR. Each element is an array with dimensions: nsim, nMPs, proyears. Non-negative real numbers.

Catch

Simulated catches (landings) over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

Removals

Simulated removals (catch + discards) over the projection. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

Effort

Simulated relative fishing effort in the projection years. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

TAC

Simulated Total Allowable Catch prescribed by MPs. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

TAE

Simulated Total Allowable Effort prescribed by MPs. An array with dimensions: nsim, nMPs, proyears. Non-negative real numbers

BioEco

Named list with bio-economic output Only used if bio-economic parameters are included in OM

RefPoint

Named list of annual MSY reference points MSY, FMSY, and SBMSY. Array with dimensions: nsim, nMPs, nyears+proyears. Will be the same as Hist@Ref$ByYear unless selectivity is changed by MP

CB_hist

Simulated catches (landings) from the spool-up period. An array with dimensions: nsim, nyears. Non-negative real numbers

FM_hist

Simulated fishing mortality rate from the spool-up period. An array with dimensions: nsim, nyears Non-negative real numbers

SSB_hist

Simulated spawning stock biomass from the spool-up period. An array with dimensions: nsim, nyears. Non-negative real numbers

Hist

Information from the historical spool-up period. Object of class Hist. Only contains slots AtAge and TSdata unless extended=TRUE in runMSE

PPD

Posterior predictive data. List of Data objects at the end of the projection period (length nMPs)

Misc

Miscellaneous output

Author(s)

T. Carruthers and A. Hordyk

Description

A data.frame with description of slots for class MSE

Usage

MSEDescription

Format

An object of class data.frame with 29 rows and 2 columns.

Description

Downloads the MSEextra package from GitHub

Usage

MSEextra(silent = FALSE, force = FALSE)

Arguments

Description

Internal function to calculate MSY Reference Points

Usage

MSYCalcs(
  logF,
  M_at_Age,
  Wt_at_Age,
  Mat_at_Age,
  Fec_at_Age,
  V_at_Age,
  maxage,
  relRfun,
  SRRpars,
  R0x = 1,
  SRrelx = 3L,
  hx = 1,
  SSBpR = 0,
  opt = 1L,
  plusgroup = 1L,
  spawn_time_frac = 0
)

Arguments

Value

See opt

Description

Catches, CAA, CAL are summed. LFC and LFS are weighted averages. ML, Lc and Lbar are recalculated from summed CAL. All other observations are for fleet 1 (indicative)

Usage

multiData(MSElist, StockPars, p, mm, nf)

Arguments

Author(s)

T. Carruthers

Description

Catches, CAA, CAL are summed. Indices, LFC and LFS are weighted averages. ML, Lc and Lbar are recalculated from summed CAL. All other observations are for fleet 1 and weighted average across stocks

Usage

multiDataS(MSElist, Real.Data.Map, np, mm, nf, realVB)

Arguments

Author(s)

T. Carruthers

Description

A basic comparison of runMSE output (MSE) and multiMSE (MMSE)

Usage

multidebug(MSEsingle, MSEmulti, p = 1, f = 1, MPno = 1, maxsims = 4)

Arguments

Author(s)

T.Carruthers