Package 'FLBEIA'

Description

There are several HCRs available in FLBEIA which are used within the main function FLBEIA to generate the management advice in each step. But they can also be used independently.

Usage

annualTAC(stocks, advice, advice.ctrl, year, stknm, ...)

F2CatchHCR(stocks, advice, advice.ctrl, year, stknm, ...)

FroeseHCR(stocks, advice, advice.ctrl, year, stknm, ...)

IcesHCR(stocks, advice, advice.ctrl, year, stknm, ...)

neaMAC_ltmp(stocks, advice, advice.ctrl, year, stknm, ...)

aneHCRE(stocks, advice, advice.ctrl, year, stknm, ...)

annexIVHCR(indices, advice, advice.ctrl, year, stknm, ...)

ghlHCR(indices, advice, advice.ctrl, year, stknm, ...)

IcesCat3HCR(indices, advice, advice.ctrl, year, stknm, ...)

IcesCat3HCR_bsafe_hrcap(indices, advice, advice.ctrl, year, stknm, ...)

MAPHCR(stocks, advice, advice.ctrl, year, stknm, ...)

CFPMSYHCR(stocks, advice, advice.ctrl, year, stknm, ...)

MultiStockHCR(stocks, indices, advice, advice.ctrl, year, stknm, ...)

pidHCR(indices, advice, advice.ctrl, year, stknm, ...)

pidHCRItarg(indices, advice, advice.ctrl, year, stknm, ...)

little2011HCR(indices, advice, advice.ctrl, year, stknm, ...)

Arguments

Details

There are two types of HCRs model-free HCRs and model-Based HCRs. Model-free HCRs use abundance indices to generate the advice and hence it use FLIndices object as input data. Model-based HCRs use estimates of stock abundance and stock exploitation level to generate the advice.

• Model-Free HCRs: annexIVHCR, ghlHCR, little2011HCR, pidHCR, pidHCRItarg, IcesCat3HCR and IcesCat3HCR_bsafe_hrcap.

• Model-Based HCRs: aneHCRE, annualTAC, CFPMSYHCR, F2CatchHCR, FroeseHCR, IcesHCR, MAPHCR, neaMAC_ltmp.

Rules' description:

• aneHCRE: The HCR used in the bay of biscay anchovy long term management plan.

• aneHCRs: The HCRs (escapement biomass) tested for the bay of biscay anchovy with different calendars. For details see Sanchez et al. 2019. MEPS 617-618: 245-263.

• annexIVHCR: The HCR used by EC and ICES to generate the TAC advice for data poor stocks.

• annualTAC: A HCR that generates annual TAC advice. The HCR provides the whole flexibility of fwd.

• CFPMSYHCR: HCR adapting the MAP HCR to allow flexibility in the year Fmsy is achieved. The user can specify the year in which you aim to reach Fmsy, with a linear transition between Fsq to Fmsy in the intervening years

• F2CatchHCR: This HCR transforms the fishing mortality advice given as input data to catch advice without any other restriction.

• FroeseHCR: The HCR defined in the paper by Froese, Branch et al. in Fish and Fisheries 2011.

• ghlHCR: The model-free HCR used in the management of greenland-halibut

• IcesHCR: The HCR used by ICES to generate TAC advice in the MSY framework.

• IcesCat3HCR: HCR that implements the ICES HCR for Category 3 DLS stocks (see ICES CM 2012/ACOM 68: Category 3 - Method 3.2)

• IcesCat3HCR_bsafe_hrcap: Alternative approach for IcesCat3HCR with a biomass safeguard and harvest rate caps (from ICES WKDLSSLS2019)

• little2011HCR: The HCR defined in the paper by Little et al. in ICES Journal of Marine Science 2011.

• MAPHRC: The HCR proposed by the EC in the evaluation on multi-annual management plans in 2015.

• MultiStockHRC: A HCR that produces TAC advice for several stocks simultaneously. It uses a fishing mortality target and an upper bound to conciliate the TAC advices. In the case of stocks without exploitation rate estimates it uses the catch.

• neaMAC_ltmp: The HCR used in the north-east atlantic mackerel long term management plan. It is a particular case of the IcesHCR.

• pidHCR, pidHCRItarg: The HCRs defined in the paper by Pomaerede et al. in Aquatic Living Resources 2010.

The HCRs are documented in detail in the manual of the library.

Value

The advice input object updated with the management advice (TAC) generated by the HCR.

Examples

## Not run: 
library(FLBEIA)
library(FLAssess)          # required to use the IcesHCR. Not available for win64
library(FLash)             # required to use the IcesHCR. Not available for win64
library(ggplot2)  

# Load the data to run FLBEIA in a one stock one fleet example using the HCR used by ICES 
# in the MSY framework. 
 data(one) 
 
oneAdv$TAC[,ac(2009:2025)] <- NA # Put NA-s in the projection years to check how the 
                                 # function fills the advice object.
 
res <- IcesHCR(oneSt, oneAdv, oneAdvC, 19, 'stk1')
# The value printed in the screen is the fishing mortality used in the advice.
 
res$TAC[,'2009']    # The resulting management advice.

## End(Not run)

Description

Function to generate an FLStock from an FLBiol and FLFleet

Usage

biolfleets2flstock(biol, fleets)

Arguments

Value

An FLStock object with abundance and biological information from biol argument and exploitation levels from fleets argument

Examples

## Not run: 

data(res_flbeia)

biol   <- oneRes$biols$stk1
fleets <- oneRes$fleets

stk <- biolfleets2flstock( biol=biol, fleets=fleets)


## End(Not run)

Description

Summarize the results of the simulation in data frames.

Usage

bioSum(
  obj,
  stknms = "all",
  years = dimnames(obj$biols[[1]]@n)$year,
  long = FALSE,
  scenario = "bc",
  byyear = TRUE,
  ssb_season = NULL,
  brp = NULL
)

bioSumQ(obj, prob = c(0.95, 0.5, 0.05))

fltSum(
  obj,
  flnms = "all",
  years = dimnames(obj$biols[[1]]@n)$year,
  byyear = TRUE,
  long = FALSE,
  InterestRate = 0.03,
  scenario = "bc"
)

fltSumQ(obj, prob = c(0.95, 0.5, 0.05))

fltStkSum(
  obj,
  flnms = names(obj$fleets),
  stknms = catchNames(obj$fleets),
  years = dimnames(obj$biols[[1]]@n)[[2]],
  byyear = TRUE,
  long = FALSE,
  scenario = "bc",
  verbose = TRUE
)

fltStkSumQ(obj, prob = c(0.95, 0.5, 0.05))

mtStkSum(
  obj,
  flnms = names(obj$fleets),
  stknms = catchNames(obj$fleets),
  years = dimnames(obj$biols[[1]]@n)[[2]],
  byyear = TRUE,
  long = FALSE,
  scenario = "bc"
)

mtStkSumQ(obj, prob = c(0.95, 0.5, 0.05))

mtSum(
  obj,
  flnms = names(obj$fleets),
  years = dimnames(obj$biols[[1]]@n)[[2]],
  byyear = TRUE,
  long = FALSE,
  scenario = "bc"
)

mtSumQ(obj, prob = c(0.95, 0.5, 0.05))

advSum(
  obj,
  stknms = "all",
  years = dimnames(obj$biols[[1]]@n)$year,
  long = FALSE,
  scenario = "bc"
)

advSumQ(obj, prob = c(0.95, 0.5, 0.05))

riskSum(
  obj,
  stknms = names(obj$biols),
  Bpa,
  Blim,
  Prflim,
  flnms = names(obj$fleets),
  years = dimnames(obj$biols[[1]]@n)[[2]],
  scenario = "bc"
)

npv(
  obj,
  discF = 0.05,
  y0,
  flnms = names(obj$fleets),
  years = dimnames(obj$biols[[1]]@n)[[2]],
  scenario = "bc"
)

npvQ(obj, prob = c(0.05, 0.5, 0.95))

vesselSum(
  obj,
  flnms = "all",
  years = dimnames(obj$biols[[1]]@n)$year,
  byyear = TRUE,
  long = FALSE,
  scenario = "bc"
)

vesselSumQ(obj, prob = c(0.95, 0.5, 0.05))

vesselStkSum(
  obj,
  flnms = names(obj$fleets),
  stknms = catchNames(obj$fleets),
  years = dimnames(obj$biols[[1]]@n)[[2]],
  byyear = TRUE,
  long = FALSE,
  scenario = "bc"
)

vesselStkSumQ(obj, prob = c(0.95, 0.5, 0.05))

Arguments

Details

• advSum, advSumQ: Data frame with the indicators related with the management advice (TAC). The indicators are: "catch", "discards", "discRat", "landings", "quotaUpt" and "tac".

• bioSum, bioSumQ: Data frame with the biological indicators. The indicators are: "biomass", "catch", "catch.iyv", "discards", "disc.iyv", "f", "landings", "land.iyv", "rec" and "ssb".

• fltSum, fltSumQ: Data frame with the indicators at fleet level. The indicators are: "capacity", "catch", "costs", "discards", "discRat", "effort", "fcosts", "gva", "grossValue", "landings", "fep", "nVessels", "price", "grossSurplus", "quotaUpt", "salaries", "vcosts" and "profitability".

• fltStkSum, fltStkSumQ: Data frame with the indicators at fleet and stock level. The indicators are: "landings", "discards", "catch", "price", "quotaUpt", "tacshare", "discRat" and "quota".

• npv: A data frame with the net present value per fleet over the selected range of years.

• mtSum, mtSumQ: Data frame with the indicators at metier level. The indicators are: "effshare", "effort", "grossValue" and "vcost".

• mtStkSum, mtStkSumQ: Data frame with the indicators at fleet and metier level. The indicators are: "catch", "discards", "discRat", "landings" and "price".

• riskSum: A data frame with the risk indicators. The indicators are: "pBlim", "pBpa" and "pPrflim".

• vesselSum, vesselSumQ: Data frame with the indicators at vessel level. The indicators are: "catch", "costs", "discards", "discRat", "effort", "fcosts", "gva", "grossValue", "landings", "fep", "price", "grossSurplus", "quotaUpt", "salaries", "vcosts" and "profitability".

• vesselStkSum, vesselStkSumQ: Data frame with the indicators at vessel and stock level. The indicators are: "landings", "discards", "catch", "price", "quotaUpt", "tacshare", "discRat" and "quota".

• summary_flbeia: An array with four dimensions: stock, year, iteration, indicator. The indicators are: recruitment, ssb, f, biomass, catch, landings and discards.

• ecoSum_damara: ecoSum built in the framework of Damara project.

The data frames

Value

A data frame with columns for scenario, year, stock, iter, indicator, value,...

The data frames can be of wide or long format. In long format all the indicators are in the same column. There is one column, indicator, for the name of the indicator and a second one value for the numeric value of the indicator. In the wide format each of the indicators correspond with one column in the data frame. The long format it is recommendable to work with ggplot2 functions for example while the wide format it is more efficient for memory allocation and speed of computations.

The quantile version of the summaries, fooQ, returns the quantiles of the indicators. In the long format as many columns as elements in prob are created. The name of the columns are the elements in prob preceded by a q. In the wide format for each of the indicators as many columns as elements in prob are created. The names of the colums are the elements in prob preceded by q_name_of_the_indicator.

Examples

## Not run: 

library(FLBEIA)

# Apply the summary functions to the examples runs in FLBEIA help page.
# Test the different arguments in summary function.

data(res_flbeia)


#------------------------------------------------
# Example One: One stock, one fleet, one iter.
#------------------------------------------------

# Wide format (default)

oneRes_bio    <- bioSum(oneRes)
oneRes$fleets[[1]] <- setUnitsNA(oneRes$fleets[[1]]) 
oneRes_flt    <- fltSum(oneRes)
oneRes_fltStk <- fltStkSum(oneRes)
oneRes_mt     <- mtSum(oneRes)
oneRes_mtStk  <- mtStkSum(oneRes)
oneRes_adv    <- advSum(oneRes)

head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)

oneRes_bioQ    <- bioSumQ(oneRes_bio)
oneRes_fltQ    <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ     <- mtSumQ(oneRes_mt)
oneRes_mtStkQ  <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ    <- advSumQ(oneRes_adv)

head(oneRes_bioQ)
head(oneRes_fltQ)
head(oneRes_fltStkQ)
head(oneRes_mtQ)
head(oneRes_mtStkQ)
head(oneRes_advQ)

# Long format for a range of years

oneRes_bio    <- bioSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_flt    <- fltSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_fltStk <- fltStkSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_mt     <- mtSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_mtStk  <- mtStkSum(oneRes, long = TRUE, years = ac(2016:2020))
oneRes_adv    <- advSum(oneRes, long = TRUE, years = ac(2016:2020))

head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)

oneRes_bioQ    <- bioSumQ(oneRes_bio)
oneRes_fltQ    <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ     <- mtSumQ(oneRes_mt)
oneRes_mtStkQ  <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ    <- advSumQ(oneRes_adv)

head(oneRes_bio)
head(oneRes_flt)
head(oneRes_fltStk)
head(oneRes_mt)
head(oneRes_mtStk)
head(oneRes_adv)

# Wide format with seasonal disaggregation 
# (Note: No seasonal disagregation available for adv summaries)

oneRes_bio    <- bioSum(oneRes, byyear = FALSE)
oneRes_flt    <- fltSum(oneRes, byyear = FALSE)
oneRes_fltStk <- fltStkSum(oneRes, byyear = FALSE)
oneRes_mt     <- mtSum(oneRes, byyear = FALSE)
oneRes_mtStk  <- mtStkSum(oneRes, byyear = FALSE)
oneRes_adv    <- advSum(oneRes) # Advice summary is only by year.

oneRes_bioQ    <- bioSumQ(oneRes_bio)
oneRes_fltQ    <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ     <- mtSumQ(oneRes_mt)
oneRes_mtStkQ  <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ    <- advSumQ(oneRes_adv)

# Long format and seasonal

oneRes_bio    <- bioSum(oneRes, long = TRUE)
oneRes_flt    <- fltSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_fltStk <- fltStkSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_mt     <- mtSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_mtStk  <- mtStkSum(oneRes, long = TRUE, byyear = FALSE)
oneRes_adv    <- advSum(oneRes, long = TRUE) # Advice summary is only by year.

oneRes_bioQ    <- bioSumQ(oneRes_bio)
oneRes_fltQ    <- fltSumQ(oneRes_flt)
oneRes_fltStkQ <- fltStkSumQ(oneRes_fltStk)
oneRes_mtQ     <- mtSumQ(oneRes_mt)
oneRes_mtStkQ  <- mtStkSumQ(oneRes_mtStk)
oneRes_advQ    <- advSumQ(oneRes_adv)


#------------------------------------------------
# Example OneIt: As one but with iterations.
#------------------------------------------------

# Wide format (default)

oneItRes_bio    <- bioSum(oneItRes, scenario = 'with_iters')
oneItRes$fleets[[1]] <- setUnitsNA(oneItRes$fleets[[1]])
oneItRes_flt    <- fltSum(oneItRes, scenario = 'with_iters')
oneItRes_fltStk <- fltStkSum(oneItRes, scenario = 'with_iters')
oneItRes_mt     <- mtSum(oneItRes, scenario = 'with_iters')
oneItRes_mtStk  <- mtStkSum(oneItRes, scenario = 'with_iters')
oneItRes_adv    <- advSum(oneItRes, scenario = 'with_iters')

oneItRes_bioQ    <- bioSumQ(oneItRes_bio)
oneItRes_fltQ    <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ     <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ  <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ    <- advSumQ(oneItRes_adv)

# Long format for a range of years

oneItRes_bio    <- bioSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_flt    <- fltSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_fltStk <- fltStkSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_mt     <- mtSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_mtStk  <- mtStkSum(oneItRes, long = TRUE, years = ac(2016:2020))
oneItRes_adv    <- advSum(oneItRes, long = TRUE, years = ac(2016:2020))


oneItRes_bioQ    <- bioSumQ(oneItRes_bio)
oneItRes_fltQ    <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ     <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ  <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ    <- advSumQ(oneItRes_adv)

# Wide format with seasonal disaggregation 
# (Note: No seasonal disagregation available for adv summaries)

oneItRes_bio    <- bioSum(oneItRes, byyear = FALSE)
oneItRes_flt    <- fltSum(oneItRes, byyear = FALSE)
oneItRes_fltStk <- fltStkSum(oneItRes, byyear = FALSE)
oneItRes_mt     <- mtSum(oneItRes, byyear = FALSE)
oneItRes_mtStk  <- mtStkSum(oneItRes, byyear = FALSE)
oneItRes_adv    <- advSum(oneItRes) # Advice summary is only by year.

oneItRes_bioQ    <- bioSumQ(oneItRes_bio)
oneItRes_fltQ    <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ     <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ  <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ    <- advSumQ(oneItRes_adv)

# Long format and seasonal

oneItRes_bio    <- bioSum(oneItRes, long = TRUE) # Biol summary is only by year.
oneItRes_flt    <- fltSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_fltStk <- fltStkSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_mt     <- mtSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_mtStk  <- mtStkSum(oneItRes, long = TRUE, byyear = FALSE)
oneItRes_adv    <- advSum(oneItRes, long = TRUE) # Advice summary is only by year.

oneItRes_bioQ    <- bioSumQ(oneItRes_bio)
oneItRes_fltQ    <- fltSumQ(oneItRes_flt)
oneItRes_fltStkQ <- fltStkSumQ(oneItRes_fltStk)
oneItRes_mtQ     <- mtSumQ(oneItRes_mt)
oneItRes_mtStkQ  <- mtStkSumQ(oneItRes_mtStk)
oneItRes_advQ    <- advSumQ(oneItRes_adv)

oneItRes_risk <- riskSum( oneItRes, Bpa = c(stk1= 900), Blim = c(stk1 = 600),
                          Prflim = c(fl1 = 0), scenario = 'alternative')

oneItRes_npv  <- npv(oneItRes, y0 = '2014')


#------------------------------------------------
# Example Multi: Two stock, two fleet, four iters.
#------------------------------------------------

# Wide format (default)

multiRes_bio    <- bioSum(multiRes)
multiRes$fleets <- FLFleetsExt(lapply(multiRes$fleets, function(x) setUnitsNA(x)))
multiRes_flt    <- fltSum(multiRes)
multiRes_fltStk <- fltStkSum(multiRes)
multiRes_mt     <- mtSum(multiRes)
multiRes_mtStk  <- mtStkSum(multiRes)
multiRes_adv    <- advSum(multiRes)

multiRes_bioQ    <- bioSumQ(multiRes_bio)
multiRes_fltQ    <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ     <- mtSumQ(multiRes_mt)
multiRes_mtStkQ  <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ    <- advSumQ(multiRes_adv)

# Long format for a range of years

multiRes_bio    <- bioSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_flt    <- fltSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_fltStk <- fltStkSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_mt     <- mtSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_mtStk  <- mtStkSum(multiRes, long = TRUE, years = ac(2016:2020))
multiRes_adv    <- advSum(multiRes, long = TRUE, years = ac(2016:2020))

multiRes_bioQ    <- bioSumQ(multiRes_bio)
multiRes_fltQ    <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ     <- mtSumQ(multiRes_mt)
multiRes_mtStkQ  <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ    <- advSumQ(multiRes_adv)

# Wide format with seasonal disaggregation 
# (Note: No seasonal disagregation available for adv summaries)

multiRes_bio    <- bioSum(multiRes, byyear = FALSE)
multiRes_flt    <- fltSum(multiRes, byyear = FALSE)
multiRes_fltStk <- fltStkSum(multiRes, byyear = FALSE)
multiRes_mt     <- mtSum(multiRes, byyear = FALSE)
multiRes_mtStk  <- mtStkSum(multiRes, byyear = FALSE)
multiRes_adv    <- advSum(multiRes) # Advice summary is only by year.

multiRes_bioQ    <- bioSumQ(multiRes_bio)
multiRes_fltQ    <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ     <- mtSumQ(multiRes_mt)
multiRes_mtStkQ  <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ    <- advSumQ(multiRes_adv)

# Long format and seasonal

multiRes_bio    <- bioSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_flt    <- fltSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_fltStk <- fltStkSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_mt     <- mtSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_mtStk  <- mtStkSum(multiRes, long = TRUE, byyear = FALSE)
multiRes_adv    <- advSum(multiRes, long = TRUE) # Advice summary is only by year.

multiRes_bioQ    <- bioSumQ(multiRes_bio)
multiRes_fltQ    <- fltSumQ(multiRes_flt)
multiRes_fltStkQ <- fltStkSumQ(multiRes_fltStk)
multiRes_mtQ     <- mtSumQ(multiRes_mt)
multiRes_mtStkQ  <- mtStkSumQ(multiRes_mtStk)
multiRes_advQ    <- advSumQ(multiRes_adv)

multiRes_npv  <- npv(multiRes, y0 = '2014')
risk_multiRes <- riskSum( multiRes, Bpa = c(stk1= 135000, stk2 = 124000),
                          Blim = c(stk1= 96000, stk2 = 89000), Prflim = c(fl1 = 0, fl2 = 0),
                          scenario = 'alternative')


## End(Not run)

Description

Function to estimate reference points when having a seasonal model

Usage

brpsson(
  stk,
  B0,
  R0,
  rec.ss = 1,
  ssb.ss = 1,
  sr_model,
  sr_params,
  Fprop = rep(1/dim(stk)[4], dim(stk)[4]),
  Fscan = seq(0, 4, by = 0.01),
  oldest = 100,
  nrun = 200,
  tol = 0.01
)

Arguments

See Also

plotBRPsson

Examples

library(FLBEIA)
data(multistk) # object with 2 seasons and 3 iterations

stk <- trim( multistk, year=1, iter = 1) 

# loop for different catch proportions by season
for (p in seq(0.1,0.9,0.1)) {
  fruns <- brpsson( stk, B0=1e+05, R0=27489766, rec.ss=2, ssb.ss=2, 
                    sr_model="bevholt", sr_params=c( a = 29988835.109, b = 9090.909), 
                    Fprop = c(p, 1-p))
} 

plotBRPsson( fruns, pdfnm="stk_Fbar_vs_SPR.pdf")

Description

Given stock abundances and catches (i.e. landings and discards), this function estimates values for fleets[[fl]]@metiers[[mt]]@catches[[st]]@catch.q, fleets[[fl]]@metiers[[mt]]@catches[[st]]@landings.sel, and fleets[[fl]]@metiers[[mt]]@catches[[st]]@discards.sel for all years except the simulation years. For the simulation years, the parameter values are set as the mean of the parameters along mean.yrs.

Usage

calculate.q.sel.flrObjs(biols, fleets, BDs, fleets.ctrl, mean.yrs, sim.yrs)

Arguments

Value

A FLFleetsExt object.

Description

This functions checks wether some conditions are met for the inputs in the different arguments of the FLBEIA function. There are also checking functions for specific inputs: biols, fleets, SRs, advice, and obs.ctrl arguments.

Functions available:

• checkFLBEIAData: for checking inputs for differents arguments of FLBEIA function (of class FLBiols).

• checkBiols: for checking biols argument of FLBEIA function (of class FLBiols).

• checkFleets: for checking fleets argument of FLBEIA function (of class FLFleetsExt).

• checkSRs: for checking SRs argument of FLBEIA function (list of FLSRsim objects).

• checkAdvice: for checking advice argument of FLBEIA function (of class list with two FLQuant elements, TAC and quota.share).

• checkObsctrl: for checking obs.ctrl argument of FLBEIA function (of class list).

Usage

checkFLBEIAData(
  biols,
  SRs = NULL,
  BDs = NULL,
  fleets,
  covars = NULL,
  indices = NULL,
  advice = NULL,
  main.ctrl,
  biols.ctrl,
  fleets.ctrl,
  covars.ctrl,
  obs.ctrl,
  assess.ctrl,
  advice.ctrl
)

checkBiols(object)

checkFleets(object, ctrl = NULL)

checkSRs(object)

checkBDs(object)

checkAdvice(object)

checkObsctrl(object)

Arguments

Value

An error message if there is any error detected.

Description

It creates the advice.ctrl object to be used in the call to the main function FLBEIA.

Usage

create.advice.ctrl(stksnames, HCR.models = NULL, ...)

Arguments

Value

A list of lists with the basic structure of the advice.ctrl object.

Description

create.advice.data function creates a list (elements: TAC, TAE and quota.share)

Usage

create.advice.data(yrs, ns, ni, stks.data, fleets)

Arguments

Value

A list with TAC, TAE and quota.share elements.

Description

It creates the assess.ctrl object to be used in the call to the main function FLBEIA.

Usage

create.assess.ctrl(stksnames, assess.models = NULL, assess.ctrls = NULL, ...)

Arguments

Value

A list of lists with the basic structure of the assess.ctrl object.

Description

create.BDs.data function creates a list of FLBDsim objects.

Usage

create.BDs.data(yrs, ns, ni, stks.data)

Arguments

Value

A list of FLBDsim objects.

Description

This function generates an FLBiol object, given the data inputs as arrays. Supported formats are Excel (xls and xlsx) and R format (RData).

Usage

create.biol.arrays(
  filename = NULL,
  data = NULL,
  name = NA,
  ages,
  hist.yrs,
  sim.yrs,
  fbar = NULL,
  mean.yrs,
  source = "rdata",
  unit = list()
)

Arguments

Value

An FLBiol.

Author(s)

Dorleta Garcia & Sonia Sanchez.

See Also

FLBiol, create.fleets.arrays

Description

It creates the biols.ctrl object to be used in the call to the main function FLBEIA.

Usage

create.biols.ctrl(stksnames, growth.models = NULL, immediate = FALSE, ...)

Arguments

Value

A list of lists with the basic structure of the biols.ctrl object.

Description

create.biols.data function creates an FLBiols object.

Usage

create.biols.data(yrs, ns, ni, stks.data)

Arguments

Value

An FLBiol object

Description

It creates the covars.ctrl object to be used in the call to the main function FLBEIA.

Usage

create.covars.ctrl(
  cvrsnames,
  process.models = NULL,
  flq,
  immediate = FALSE,
  ...
)

Arguments

Value

A list of lists with the basic structure of the covars.ctrl object.

Description

This function generates an covar object with the economic indicators and fills in vcost, fcost, capacity and crewshare slots in FLFleets object. The data is given in excel File (xls and xlsx).

Usage

create.ecoData(file, fltObj, hist.yrs, mean.yrs, sim.yrs)

Arguments

Value

An /codeFLFleetsExt.

Author(s)

Dorleta Garcia & Sonia Sanchez.

See Also

FLFleetsExt, create.biol.arrays

Description

This function generates an FLFleetsExt object, given the data inputs as arrays. Supported formats are Excel (xls and xlsx) and R format (RData).

Usage

create.fleets.arrays(
  stk_objs,
  caa_objs,
  caa_objs_path,
  price_objs,
  price_objs_path,
  catch_obj,
  effort_obj,
  flt_obj = NULL,
  stk_nms = NA,
  flt_nms,
  flt_mt_nms,
  flt_mt_stk_nms,
  ages = NULL,
  hist.yrs,
  sim.yrs,
  mean.yrs,
  new_hist.yrs = hist.yrs,
  update_catch_effort = TRUE,
  update_price = TRUE,
  update_weight = TRUE,
  caa_flt_mt_correspondences = NULL,
  paa_flt_mt_correspondences = NULL,
  caaOpt,
  priceOpt,
  excel = TRUE
)

Arguments

Value

An FLFleetsExt.

Author(s)

Dorleta Garcia & Sonia Sanchez.

See Also

FLFleetsExt, create.biol.arrays

Description

It creates the fleets.ctrl object to be used in the call to the main function FLBEIA.

Usage

create.fleets.ctrl(
  fls,
  n.fls.stks,
  fls.stksnames,
  catch.threshold = NULL,
  seasonal.share = NULL,
  effort.models = NULL,
  capital.models = NULL,
  catch.models = NULL,
  price.models = NULL,
  flq,
  ...
)

Arguments

Value

A list of lists with the basic structure of the fleets.ctrl object.

Description

create.fleets.data function creates an FLFleetsExt object

Usage

create.fleets.data(yrs, ns, ni, fls.data, stks.data)

Arguments

Value

An FLFleetsExt object.

Description

create.indices.data function creates an FLIndices object

Usage

create.indices.data(yrs, ns, ni, stks.data)

Arguments

Value

An FLIndices object.

Description

It creates the obs.ctrl object to be used in the call to the main function FLBEIA.

Usage

create.obs.ctrl(
  stksnames,
  n.stks.inds = NULL,
  stks.indsnames = NULL,
  stkObs.models = NULL,
  indObs.models = NULL,
  immediate = FALSE,
  ...
)

Arguments

Value

A list of lists with the basic structure of the obs.ctrl object.

Description

create.BDs.data function creates a list of FLBDsim objects

Usage

create.SRs.data(yrs, ns, ni, stks.data)

Arguments

Value

A list of FLSRsim objects.

Description

Example datasets for the classes defined in FLBEIA.

Details

• one: A dataset for running FLBEIA. Example with one stock (age-structured), one fleet, annual steps (one season) and one iteration.

  • oneBio (FLBiols) : Biological information on the stock. In this case, the stock is age-structured.

  • oneSR (FLSRsim) : Stock-recruitment model for the stock in oneBio object.

  • oneFl (FLFleetsExt) : Information on the fleet and the metier considered.

  • oneCv (list of FLQuants) : Covariates information. In this case all are economic indicators.

  • oneIndAge and oneIndBio (list of FLIndices) : Indices, if avalable, for the different stocks in oneBio. Where oneIndAge and oneIndBio are indices with estimates in numbers at age and total biomass, respectively.

  • oneAdv (list) : Information on TAC and quota share.

  • oneMainC (list) : Settings to control the main function FLBEIA. The simulation years.

  • oneBioC (list) : Settings to control the biological operating model for the stock in oneBio object.

  • oneFlC (list) : Settings to control the fleet operating model for the fleet in oneFl object.

  • oneCvC (list) : Settings to control the covar operating model for each covariate in covars object. In this case all the covariates are fixed.

  • oneObsC, oneObsCIndAge and oneObsCIndBio (list) : Settings to control the observation model for the stock in oneBio object. In oneObsC the stock is observed without error and there are no indices available. Alternative control settings are available for cases when indices are observed, oneObsCIndAge and oneObsCIndBio.

  • oneAssC (list) : Settings to control the assessment model for each stock in oneBio object. In this case, no assessment is carried out.

  • oneAdvC (list) : Settings to control the advice model for the stock in oneBio object.

• oneIt: A dataset for running FLBEIA. Same as one dataset, but with three iterations.

• multi: A dataset for running FLBEIA. Example with two stocks (one age-structured and the other in biomass), two fleets (with 2 metiers each), four seasons and one iteration.

  • multiBio (FLBiols) : Biological information on the stocks (one is age-structured and the other one in total biomass).

  • multiSR (FLSRsim) : Stock-recruitment models for the age-structured stock in multiBio object. In this case a Beverton-Holt (bevholt) is selected.

  • multiBD (FLSRsim) : Biomass dynamic model for the stock in biomass in multiBio object. In this case Pella-Tomlinson model (PellaTom) is selected.

  • multiFlC (FLFleetsExt) : Information on the fleets and metiers. In this case there are two fleets, each one with two metiers, all of them capturing both stocks in multiBio object.

  • multiCv (list of FLQuants) : Covariates information. In this case all are economic indicators.

  • multiAdv (list) : Information on TAC and quota share.

  • multiMainC (list) : Settings to control the main function FLBEIA.

  • multiBioC (list) : Settings to control the biological operating model for each stock in multiBio object.

  • multiFlC (list) : Settings to control the fleet operating model for each fleet in fleets object.

  • multiCvC (list) : Settings to control the covar operating model for each covariate in covars object. In this case all the covariates are fixed.

  • multiObsC (list) : Settings to control the observation model for each stock in multiBio object. In this case the stock is observed without error and there are no indices available.

  • multiAssC (list) : Settings to control the assessment model for each stock in multiBio object. In this case, no assessment is carried out.

  • multiAdvC (list) : Settings to control the advice model for each stock in multiBio object.

• res_flbeia: A dataset with the outputs of FLBEIA runs given as input the different datasets (one, oneIt and multi).

  • oneRes (list) : Output of the FLBEIA function, given as input the data in the one dataset.

  • onIteRes (list) : Output of the FLBEIA function, given as input the data in the oneIt dataset.

  • multiRes (list) : Output of the FLBEIA function, given as input the data in the multi dataset.

• mur: A dataset for Stripped Red Mullet in the Bay of Biscay. Information on catch and abundance indices from Evohe survey.

  • catch (data.frame) : The total catch time series data by area from WGBIE report (ICES, 2017). Total catch data is available since 1975. In 1999 France did not report any data. As France is the main contributor to the total catch, the 1999 catch data was not included in the analysis.

  • evhoe (data.frame) : EVHOE abundance index time series, provided by Ifremer. The abundance index is available since 1997 and provides an estimation of the biomass together with a coefficient of variation.

• multistk: A dataset with an FLStock with multiple dimensions.

  • multistk (FLStock) : FLStock object with 2 seasons and 3 iterations.

Datasets can be loaded by issuing the data command, like in: data(one).

All available datasets can be checked by: data(package='FLBEIA').

References

ICES, 2017

See Also

FLBEIA, FLBiols, FLFleetsExt, FLSRsim, FLIndices, FLQuant

Examples

data(one)
data(res_flbeia)

Description

These functions return the biomass (B), fishing mortality (F), spawning stock biomass (SSB), recruitment (R), catches (C), landings (L) and discards (D) indicators. Also indicators comparing the reference points with the actual values, Bpa, Blim, Btarget, Fpa, Flim and Ftarget, so the biomass indicators are TRUE if the biomass is above them, and the fishing mortality indicators are TRUE if the fishing mortality is below them. ssb2Btarget and f2Ftarget return the ratio between SSB and F and the target reference point.

Usage

ecoSum_damara(fleets, flnms = "all", years, covars = NULL)

F_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

SSB_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

B_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

R_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

C_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

L_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

D_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

summary_flbeia(obj, years = dimnames(obj$biols[[1]]@n)$year)

Arguments

Details

• B_flbeia this function computes SSB.

• F_flbeia this function computes fishing mortiality.

• SSB_flbeia this function computes spawning stock biomass by species.

• R_flbeia this function computes recruitment by stock. If the stock is defined by age this function the recruiment is computed. ; If the stock is follows a biomass dynamics, this function gives the growth.

• C_flbeia this function computes catches by fleets and stock.

• L_flbeia this function computes landings by fleets and stock.

• D_flbeia this function computes the discards by fleets and stock.

• summary_flbeia this function computes recruitment, SSB, fishing mortality, total biomass for all stocks; and catch and landings for all fleets by year and stock.

Value

B_flbeia, F_flbeia... return an array with three dimensions (stock, year and iter). The summary_flbeia function returns an array with 4 dimensions (stock, year, iter, indicator) with the value of all the indicators.

Description

The output can be used for the brp argument of bioSum function.

Usage

extractBRP(advice.ctrl, stkn, Btarget = NULL, Ftarget = NULL)

Arguments

Value

A data frame with columns stock, iter and one colum per reference point with the value of the biological reference points per stock and iteration. The used reference points are Bpa, Blim, Bmsy, Fpa, Flim and Fmsy.

See Also

bioSum

Examples

## Not run: 

library(FLBEIA)

data(one)
extractBRP(oneAdvC, stkn = names(oneBio))

data(oneIt)
extractBRP(oneItAdvC, stkn = names(oneItBio))

data(multi)
extractBRP(multiAdvC, stkn = names(multiBio))
                          
# setting targets different to Bmsy and Fmsy

extractBRP(oneAdvC, stkn = names(oneBio),
           Btarget=setNames("Btrigger","stk1"), Ftarget=setNames("Ftrigger","stk1"))

Btarget <- setNames(c("Btrigger","Bmsy"),names(oneBio))
Ftarget <- setNames(c("Ftrigger","Fmsy"),names(oneBio))

extractBRP(multiAdvC, stkn = names(multiBio))


## End(Not run)

Description

A class to simulate the growth of populations aggregated in biomass.

Arguments

Details

The FLBDsim has the following slots:

Value

An object of class FLBDsim.

Slots

name

The name of the stock.

desc

A description of the object.

range

The range of the object.

biomass

An FLQuant to store the biomass of the stock.

gB

An FLQuant to store the surplus production of the stock.

catch

An FLQuant to store the catch of the stock.

uncertainty

An FLQuant to store the uncertainty that is multiplied to the biomass in every step of the simulation.

covar

An FLQuants to store the covariates that are part of the growth model.

model

A character with the name of the model to simulate the recruitment process.

params

An array with dimension [numb.params, numb.year, numd.season, numb.iteration] with year and season and iteration dependent parameters of the growth model.

alpha

An array with dimension [numb.year, numd.season, numb.iteration] with year, season and iteration dependent value bigger than one which indicates, in percentage, how big can be the biomass in comparison with the carrying capacity.

name

The name of the object.

desc

Character with the description of the object.

range

A numeric vector with the range of the object as in other FLR objects.

Description

FLBEIA is a simulation model that describes a fishery system under a Management Strategy Evaluation framework.The objective of the model is to facilitate the Bio-Economic evaluation of Management strategies. The model is multistock, multifleet and seasonal. The simulation is divided in 2 main blocks, the Operating Model (OM) and the Management Procedure (MP). In turn, the OM is divided in 3 components, the biological, the fleets and the covariables component. The MP is also divided in 3 components, the observation, the assessment and the advice.

Usage

FLBEIA(
  biols,
  SRs = NULL,
  BDs = NULL,
  fleets,
  covars = NULL,
  indices = NULL,
  advice = NULL,
  main.ctrl,
  biols.ctrl,
  fleets.ctrl,
  covars.ctrl,
  obs.ctrl,
  assess.ctrl,
  advice.ctrl
)

Arguments

Value

A list with 8 elements biols, fleets, covars, advice, stocks, indices, fleets.ctrl, pkgs.versions. All the elements except stocks and pkgs.versions correspond with the the updated versions of the objects used in the call to FLBEIA. stocks is a list of FLStocks object containing the perceived stocks used in the management procedure to produce the management advice. pkgs.versions is a matrix indicating the packages and package version used along the simulation.

Examples

## Not run: 
library(FLBEIA)
library(FLAssess)          # required to use the IcesHCR. Not available for win64
library(FLash)             # required to use the IcesHCR. Not available for win64
library(ggplot2)  


#---------------------------------------------------------------- 
# Example with 1 stock, 1 Fleets, 1 seasons and 1 iteration: one
#----------------------------------------------------------------

# Load the data to run FLBEIA in a one stock one fleet example using the HCR used by ICES 
# in the MSY framework. 
 data(one) 

# The names and the class of the objects needed to run FLBEIA.
# sapply(ls(), function(x) class(get(x)))

# In this scenario a single, age-structured, stock is exploited by a single fleet with a 
# unique metier. 
# The fleet catches yearly exactly the adviced TAC and there is no exit-entry of vessels 
# in the fishery.  
# The stock abundance and exploitation level is observed without error in the observation 
# model.
# There is no assessment model and the TAC advice is used through the HCR used by ICES 
# in the MSY framework.  
 

  
    
s0 <- FLBEIA(biols = oneBio,   # FLBiols object with one FLBiol element for stk1.
               SRs = oneSR,    # A list with one FLSRsim object for stk1.
               BDs = NULL,     # No Biomass Dynamic populations in this case.
            fleets = oneFl,    # FLFleets object with on fleet.
            covars = oneCv,    # covars not used
           indices = NULL,     # indices not used 
            advice = oneAdv,   # A list with two elements 'TAC' and 'quota.share'
         main.ctrl = oneMainC, # A list with one element to define the start and end of 
                               # the simulation.
        biols.ctrl = oneBioC,  # A list with one element to select the model to simulate 
                               # the stock dynamics.
       fleets.ctrl = oneFlC,   # A list with several elements to select fleet dynamic models 
                               # and store additional parameters.
       covars.ctrl = oneCvC,   # covars control not used 
          obs.ctrl = oneObsC,  # A list with one element to define how the stock observed 
                               # ("PerfectObs").
       assess.ctrl = oneAssC,  # A list with one element to define how the stock assessment 
                               # model used ("NoAssessment").
       advice.ctrl = oneAdvC)  # A list with one element to define how the TAC advice is 
                               # obtained ("IcesHCR").

# Names of the object returned by FLBEIA
names(s0)

# The default plot for FLBiol defined in FLCore
plot(s0$biols[[1]])

# Extract reference points for summaries
s0_brps <- extractBRP(oneAdvC, stkn = names(oneBio)) 

# Create summary data frames (biological, economic, and catch)
proj.yr     <- 2013 
s0_sum      <- bioSum(s0, brp = s0_brps)
s0$fleets$fl1 <- setUnitsNA(s0$fleets$fl1) # set units to NA to avoid errors in fltSum
s0_flt      <- fltSum(s0)
s0_fltStk   <- fltStkSum(s0)


# Create several plots and save them in the working directory using 'pdf' format and 
# 's0' suffix in the name.


plotFLBiols(s0$biols, pdfnm='s0', ss = 'all')
plotFLFleets(s0$fleets, pdfnm='s0', ss = 'all')
plotEco(s0, pdfnm='s0')
plotfltStkSum(s0, pdfnm='s0')

#------------------------------------------------------------ 
# Example with several iterations: oneIters
#------------------------------------------------------------
 
 # Load the same data set as before but with 3 iterations.
 # Run FLBEIA and plot the results
 
data(oneIt)
 
s1 <- FLBEIA(biols = oneItBio,   # FLBiols object with one FLBiol element for stk1.
               SRs = oneItSR,    # A list with one FLSRsim object for stk1.
               BDs = NULL,       # No Biomass Dynamic populations in this case.
            fleets = oneItFl,    # FLFleets object with on fleet.
            covars = oneItCv,    # covars not used
           indices = NULL,       # indices not used 
            advice = oneItAdv,   # A list with two elements 'TAC' and 'quota.share'
         main.ctrl = oneItMainC, # A list with one element to define the start and end of 
                                 # the simulation.
        biols.ctrl = oneItBioC,  # A list with one element to select the model to simulate 
                                 # the stock dynamics.
       fleets.ctrl = oneItFlC,   # A list with several elements to select fleet dynamic 
                                 # models and store additional parameters.
       covars.ctrl = oneItCvC,   # covars control not used 
          obs.ctrl = oneItObsC,  # A list with one element to define how the stock observed 
                                 # ("PerfectObs").
       assess.ctrl = oneItAssC,  # A list with one element to define how the stock 
                                 # assessment model used ("NoAssessment").
       advice.ctrl = oneItAdvC)  # A list with one element to define how the TAC advice is 
                                 # obtained ("IcesHCR").

# Names of the object returned by FLBEIA
names(s1)

# The default plot for FLBiol defined in FLCore
plot(s1$biols[[1]])

# Extract reference points for summaries
s1_brps <- extractBRP(oneItAdvC, stkn = names(oneItBio)) 

# Create summary data frames (biological, economic, and catch)
proj.yr     <- 2013 
s1_bio     <- bioSum(s1, brp = s1_brps)
s1$fleets$fl1 <- setUnitsNA(s1$fleets$fl1) # set units to NA to avoid errors in fltSum
s1_flt     <- fltSum(s1)
s1_fltStk  <- fltStkSum(s1)

s1_bioQ    <- bioSumQ(s1_bio)
s1_fltQ    <- fltSumQ(s1_flt)
s1_fltStkQ <- fltStkSumQ(s1_fltStk)

s1b_bio     <- bioSum(s1, long = FALSE)
s1b_flt     <- fltSum(s1, long = FALSE)
s1b_fltStk  <- fltStkSum(s1, long = FALSE)

s1b_fltQ    <- bioSumQ(s1b_bio)
s1b_fltQ    <- fltSumQ(s1b_flt)
s1b_fltStkQ <- fltStkSumQ(s1b_fltStk)

# Create several plots and save them in the working directory using 'pdf' format and 
# 's1' suffix in the name.

plotFLBiols(s1$biols, pdfnm='s1', ss = 'all')
plotFLFleets(s1$fleets, pdfnm='s1', ss = 'all')
plotEco(s1, pdfnm='s1')
plotfltStkSum(s1, pdfnm='s1') 

 
#------------------------------------------------------------------ 
# Example with 2 stock, 2 Fleets, 4 seasons and 1 iteration: multi
#------------------------------------------------------------------
 
 # Load the multi data set. This dataset has 2 stocks, one stk1 is 
 # age structured and the second one stk2 is aggregated in biomass.
 
data(multi)
 
 # Run FLBEIA.
 
s2 <- FLBEIA(biols = multiBio,   # FLBiols object with 2 FLBiol element for stk1.
               SRs = multiSR,    # A list with 1 FLSRsim object for stk1.
               BDs = multiBD,    # A list with 1 FLBDSim object for stk2.
            fleets = multiFl,    # FLFleets object with on fleet.
            covars = multiCv,    # covars not used
           indices = NULL,       # indices not used 
            advice = multiAdv,   # A list with two elements 'TAC' and 'quota.share'
         main.ctrl = multiMainC, # A list with one element to define the start and end 
                                 # of the simulation.
        biols.ctrl = multiBioC,  # A list with one element to select the model to simulate 
                                 # the stock dynamics.
       fleets.ctrl = multiFlC,   # A list with several elements to select fleet dynamic 
                                 # models and store additional parameters.
       covars.ctrl = multiCvC,   # covars control not used 
          obs.ctrl = multiObsC,  # A list with one element to define how the stock observed 
                                 # ("PerfectObs").
       assess.ctrl = multiAssC,  # A list with one element to define how the stock 
                                 # assessment model used ("NoAssessment").
       advice.ctrl = multiAdvC)  # A list with one element to define how the TAC advice is 
                                 # obtained ("IcesHCR").

# Names of the object returned by FLBEIA
names(s2)

# The default plot for FLBiol defined in FLCore
plot(s2$biols[[1]])

# Extract reference points for summaries
s2_brps <- extractBRP(multiAdvC, stkn = names(multiBio)) 

# Create summary data frames (biological, economic, and catch)

s2_sum      <- bioSum(s2, brp = s2_brps)
for (fl in names(s2$fleets))  # set units to NA to avoid errors in fltSum
  s2$fleets[[fl]] <- setUnitsNA(s2$fleets[[fl]])
s2_flt      <- fltSum(s2)

s2b_flt     <- fltSum(s2, byyear = FALSE)

s2_fltStk   <- fltStkSum(s2)

# Create several plots and save them in the working directory using 'pdf' format and 
# 's2' suffix in the name.

plotFLBiols(s2$biols, pdfnm='s2', ss = 2)
plotFLFleets(s2$fleets, pdfnm='s2', ss = 2)
plotEco(s2, pdfnm='s2')
plotfltStkSum(s2, pdfnm='s2')

## End(Not run)

Description

A list of FLCatchExt objects.

Usage

vFLCs(object)

## S4 method for signature 'ANY'
FLCatchesExt(object, ...)

## S4 method for signature 'missing'
FLCatchesExt(object, ...)

## S4 method for signature 'list'
FLCatchesExt(object)

is.FLCatchesExt(object, ...)

## S4 method for signature 'ANY'
is.FLCatchesExt(object, ...)

## S4 method for signature 'FLCatchesExt'
catchNames(object)

## S4 method for signature 'FLMetierExt'
catchNames(object)

## S4 method for signature 'FLMetiersExt'
catchNames(object)

## S4 method for signature 'FLFleetExt'
catchNames(object)

## S4 method for signature 'FLFleetsExt'
catchNames(object)

Arguments

Slots

.Data

Internal S4 data representation, of class list.

desc

As textual description of the object contents

lock

Can the object be extended/trimmed? TRUE or FALSE.

names

A character vector for the element names

Author(s)

The FLBEIA Team

See Also

FLlst, list, vector, FLCatchExt

Description

It extends the FLCatch class defined in FLFleet package. The FLCatch class includes two extra slots alpha and beta used in the Cobb-Douglas production functions.

Usage

## S4 method for signature 'FLQuant'
FLCatchExt(object, range = "missing", name = "NA", desc = character(0), ...)

## S4 method for signature 'missing'
FLCatchExt(object, ...)

## S4 method for signature 'FLCatchExt,ANY,ANY,ANY'
x[i, j, k, l, m, n, ..., drop = FALSE]

## S4 replacement method for signature 'FLCatchExt,ANY,ANY,FLCatchExt'
x[i, j, k, l, m, n, ...] <- value

catchNames(object, ...) <- value

## S4 method for signature 'FLCatchExt,FLCatchExt'
addFLCatch(e1, e2)

## S4 method for signature 'FLCatchExt'
catchNames(object)

## S4 replacement method for signature 'FLCatchExt,character'
catchNames(object) <- value

Arguments

Details

The FLCatchExt object contains a representation of the catch of a fish stock as constructed for the purposes of fleet dynamic modelling. This includes information on removals (i.e. landings and discards), selectivity, weight, price and catch production parameters (catchability and elasticities).

Value

The constructors return an object of class FLCatchExt.

Slots

landings

An FLQuant with the total landings in weight of the stock.

landings.n

An FLQuant with the landings in numbers at age of the stock.

landings.wt

An FLQuant with the weight at age of the landings.

discards

An FLQuant with the total discards in weight of the stock.

discards.n

An FLQuant with the discards in numbers at age of the stock.

discards.wt

An FLQuant with the weight at age of the discards.

landings.sel,discards.sel

An FLQuant with the landing/discard ogive of the metier for this stock (i.e. landings.sel corresponds to the proportion of catches landed). Elements must be between 0 and 1, with discards.sel = 1-landings.sel.

catch.q

An FLQuant with the catchability at age of the stock for the corresponding metier. This is the catchability used in the catch production model.

price

An FLQuant with the price at age of the stock.

alpha

An FLQuant with the elasticity parameter at age of the stock for the corresponding metier. This is one of the parameters used in the catch production model.

beta

An FLQuant with the elasticity parameter at age of the stock for the corresponding metier. This is one of the parameters used in the catch production model.

name

The name of the stock.

desc

A description of the object.

range

The range as in other FLR objects: c("min","max","plusgroup","minyear","maxyear").

Description

It extends the FLFleetExt class defined in FLFleet package. The only difference is that that the metiers slot is a FLMetiersExt object.

Usage

## S4 method for signature 'FLMetiersExt'
FLFleetExt(object, ...)

## S4 method for signature 'FLMetierExt'
FLFleetExt(object, ...)

## S4 method for signature 'FLCatchesExt'
FLFleetExt(object, ...)

## S4 method for signature 'FLCatchExt'
FLFleetExt(object, ...)

## S4 method for signature 'FLFleetExt'
FLFleetExt(object, metier, catch, ...)

## S4 method for signature 'missing'
FLFleetExt(object, ...)

## S4 method for signature 'FLFleetExt,ANY,missing,ANY'
x[i, drop = FALSE]

## S4 method for signature 'FLFleetExt,ANY,ANY,ANY'
x[i, j, drop = FALSE]

## S4 method for signature 'FLFleetExt,ANY,missing'
x[[i, drop = FALSE]]

Arguments

Details

The FLFleetExt object contains a representation of a fishing fleet as constructed for the purposes of fleet dynamic modelling. This includes information on effort, fixed-cost, capacity, crew-share, metiers and variable costs.

Value

The constructors return an object of class FLFleetExt.

Slots

effort

An FLQuant with the effort of the fleet. The effort can have any units (e.g. number of fishing days, trips, hooks,...)

fcost

An FLQuant with the fixed costs of the fleet. These costs should be given by vessel and the number of vessels by fleet must be included in the covars object.

capacity

An FLQuant with the capacity of the fleet. Same units as in slot effort must be used.

crewshare

An FLQuant with the crewshare of the fleet. Where crewshare is the percentage of revenues that goes to the crew.

metiers

A FLMetiersExt with information on the fleet's metiers.

name

The name of the stock.

desc

A description of the object.

range

The range as in other FLR objects: c("min","max","plusgroup","minyear","maxyear").

Description

A list of FLFleetExt objects.

Usage

vFLFs(object)

## S4 method for signature 'ANY'
FLFleetsExt(object, ...)

## S4 method for signature 'missing'
FLFleetsExt(object, ...)

## S4 method for signature 'list'
FLFleetsExt(object)

is.FLFleetsExt(object, ...)

## S4 method for signature 'ANY'
is.FLFleetsExt(object, ...)

Arguments

Slots

.Data

Internal S4 data representation, of class list.

desc

As textual description of the object contents

lock

Can the object be extended/trimmed? TRUE or FALSE.

names

A character vector for the element names

Author(s)

The FLBEIA Team

See Also

FLlst, list, vector, FLFleetExt

Description

It extends the FLMetier class defined in FLFleet package. The only difference is that that the catches slot is a FLCatchesExt object.

Usage

## S4 method for signature 'FLCatchExt'
FLMetierExt(catches, gear = "NA", ...)

## S4 method for signature 'FLCatchesExt'
FLMetierExt(catches, gear = "NA", ...)

## S4 method for signature 'FLQuant'
FLMetierExt(catches, gear = "NA", ...)

## S4 method for signature 'missing'
FLMetierExt(catches, gear = "NA", ...)

## S4 method for signature 'FLMetierExt,ANY,missing,ANY'
x[i, drop = FALSE]

## S4 method for signature 'FLMetierExt,ANY,missing'
x[[i, drop = FALSE]]

Arguments

Details

The FLMetierExt object contains a representation of the metier of a fishing fleet as constructed for the purposes of fleet dynamic modelling. This includes information on effortshare and variable costs.

Value

The constructors return an object of class FLMetierExt.

Slots

gear

A character with the gear name of a fleet's metier.

effshare

An FLQuant with the effort share of a fleet's metier relative to fleet's total effort (the sum of all metiers effshares for a fleet must sum 1).

vcost

An FLQuant with the varible costs of a fleet's metier. These costs should be given by vessel and based on the effort units used for the fleet's effort (within the FLFleetExt object). The number of vessels by fleet must be included in the covars object.

catches

A FLCatchesExt with information on the fleet's metier catches.

name

The name of the stock.

desc

A description of the object.

range

The range as in other FLR objects: c("min","max","plusgroup","minyear","maxyear").

Description

A list of FLMetierExt objects.

Usage

vFLMs(object)

## S4 method for signature 'ANY'
FLMetiersExt(object, ...)

## S4 method for signature 'missing'
FLMetiersExt(object, ...)

## S4 method for signature 'list'
FLMetiersExt(object)

is.FLMetiersExt(object, ...)

## S4 method for signature 'ANY'
is.FLMetiersExt(object, ...)

Arguments

Slots

.Data

Internal S4 data representation, of class list.

desc

As textual description of the object contents

lock

Can the object be extended/trimmed? TRUE or FALSE.

names

A character vector for the element names

Author(s)

The FLBEIA Team

See Also

FLlst, list, vector, FLMetierExt

Description

This class is used to store the necessary information to simulate the recruitment process within FLBEIA.

Arguments

Details

The FLSRsim class contains a representation of the recruitment process of a fish stock. This includes information on recruitment, ssb, recruitment model, uncertainty and distribution of the recruitment along seasons. The slots in this class:

Value

An object of class FLSRsim.

Slots

rec

An FLQuant with to store the recruitment.

ssb

An FLQuant with to store the ssb.

covar

An FLQuants to store the covariates considered in the stock-recruitment model, one FQuant per covariate.

uncertainty

An FLQuant with to store the uncertainty that is multiplied to the recruitment point estimate in each step of the simulation.

proportion

An FLQuant with values between 0 and 1 to indicate how the recruitment in each of the time steps is distributed along season.

model

A character with the name of the model to simulate the recruitment process.

params

An array with dimensions [number of params,number of years,number of seasons,number of iteration].

timelag

A matrix [2, number of seasons]. The element (1,j) indicates the time lag between the spawning and recruitment year and the element (2,j) the season in which the recruitment was spawn.

name

A character with the name of the stock.

desc

A description of the object.

range

A numeric vector with c(min, max,plusgroup, minyear, maxyear) as in the rest of the FLR objects.

Description

joinIter function allows to join different outputs of FBEIA simulations. For example when simulations are run iteration by iteration separately, with this function we can merged all the iterations into one FLBEIA output object.

Usage

joinIter(
  objnam,
  files,
  directory = NULL,
  Niters = 1,
  elements = "all",
  advice.ext = "TAC",
  fleets.ctrl.ext = "seasonal.share"
)

Arguments

Value

A new FLBEIA output object with all the iterations joined.

Note

The files must contain a single object (named as objnam value).

Description

Summarize the results in a plot.

Usage

plotbioSum(obj, stk.nam, Blim = NA, Bpa = NA, proj.yr = NA)

plotfltSum(obj, flt.nam, proj.yr = NA)

Arguments

Details

• plotbioSum: Plot summarising information on stock. With one plot for each of the following indicators: "catch", "rec", "f" and "ssb". Input object should have the same format as the output of bioSum function.

• plotfltSum: Plot summarising information on fleet's economic indicators. With one plot for each of the following indicators: "catch", "effort", "grossValue" and "grossSurplus". Input object should have the same format as the output of fltSum function.

Value

Plot.

Examples

## Not run: 

library(FLBEIA)

# Apply the summary plots to the examples runs in FLBEIA help page.

data(res_flbeia)
#------------------------------------------------
# Example One: One stock, one fleet, one iter.
#------------------------------------------------

# Biological indicators.
plotbioSum( bioSum(oneRes), Blim=800, Bpa=1200, proj.yr=2010) # bioSum output in wide format
plotbioSum( bioSum(oneRes, long = FALSE))   # bioSum output in long format
plotbioSum( bioSum(oneRes), stk.nam='stk0') # if incorrect name for the stock

# Indicators at fleet level.
plotfltSum( fltSum(oneRes), proj.yr=2010) # fltSum output in wide format
plotfltSum( fltSum(oneRes, long = FALSE)) # fltSum output in long format
plotfltSum( fltSum(oneRes), flt.nam='stk1') # if incorrect name for the fleet
plotfltSum( fltSum(oneRes, byyear = FALSE)) # although seasonal disagregation, 
                                            # it is summarised by year

#------------------------------------------------
# Example OneIters: As one but with iterations.
#------------------------------------------------

# Biological indicators.
plotbioSum( bioSum(s1), Blim=800, Bpa=1200, proj.yr=2010) # bioSum output in wide format
plotbioSum( bioSum(s1, long = FALSE))   # bioSum output in long format
plotbioSum( bioSum(s1), stk.nam='stk0') # if incorrect name for the stock


# Indicators at fleet level.
plotfltSum( fltSum(s1), proj.yr=2010) # fltSum output in wide format
plotfltSum( fltSum(s1, long = FALSE)) # fltSum output in long format
plotfltSum( fltSum(s1), flt.nam='stk1') # if incorrect name for the fleet
plotfltSum( fltSum(s1, byyear = FALSE)) # although seasonal disagregation, 
                                        # it is summarised by year

# also possible to plot information on various scenarios
sc11_bio <- bioSum(s1)
sc12_bio <- bioSum(s1, scenario='alt'); sc12_bio$value <- sc12_bio$value*1.05
plotbioSum(rbind(sc11_bio, sc12_bio), Blim=800, Bpa=1200, proj.yr=2010)

#------------------------------------------------
# Example Multi: Two stock, two fleet, four iters.
#------------------------------------------------

for (st in names(s2$stocks)) # one plot for each stock
  plotbioSum( bioSum(s2, scenario='s2'), stk.nam=st, proj.yr=2010)

for (fl in names(s2$fleets)) # one plot for each fleet
  plotfltSum( fltSum(s2, scenario='s2'), flt.nam=fl, proj.yr=2010)
  

## End(Not run)

Description

Function for generating YPR plots for reference points when having a seasonal model

Usage

plotBRPsson(obj, pdfnm = "Fbar_vs_SPR.pdf")

Arguments

Value

A pdf for each stock with plots.

See Also

brpsson

Description

Return a pdf with plots using FLBEIA object (FLFleets and covars).

Usage

plotEco(obj, probs = c(0.95, 0.5, 0.05), pdfnm = "bc")

Arguments

Value

A pdf with capacity, costs, effort, profits by fleet.

Examples

## Not run: 
library(FLBEIA)
library(ggplot2)

data(res_flbeia)
plotEco(oneRes, pdfnm='one')

## End(Not run)

Description

For each stock, return a pdf with plots using FLBiols object.

Usage

plotFLBiols(biols, probs = c(0.95, 0.5, 0.05), pdfnm = "bc", u = 1, ss = 1)

Arguments

Details

• Each pdf contains biomass in numbers at age, mean weight at age, fecundity, natural mortality, maturity, spawning, recruitment and spawning stock biomass

Value

A pdf for each stock with plots.

Examples

## Not run: 
library(FLBEIA)
library(ggplot2)
data(res_flbeia)
plotFLBiols(oneRes$biols, pdfnm='oneRes')

## End(Not run)

Description

For each fleet, return a pdf with plots using FLFleets object.

Usage

plotFLFleets(fleets, probs = c(0.95, 0.5, 0.05), pdfnm = "bc", u = 1, ss = 1)

Arguments

Details

For each fleet, the pdf contains plots of:

• Catch, discards, landings, capacity, crewshare, effort, fcost, effshare

• For each metier: landings and discards at age in numbers and mean weight, alpha, beta and catch.q

Value

A pdf for each fleet with plots.

Examples

## Not run: 
library(FLBEIA)
library(ggplot2)
data(res_flbeia)
plotFLFleets(oneRes$fleets,pdfnm='oneFl')

## End(Not run)

Description

Return a pdf with plots with the outputs fltStkSum, usint the output of FLBEIA.

Usage

plotfltStkSum(obj, pdfnm)

Arguments

Value

One pdf with plots on landings, discards,catch,price,quotaUpt,tacshare,discRat,quota by fleet and stock.

Examples

## Not run: 
library(FLBEIA)
library(ggplot2)
data(res_flbeia)
plotfltStkSum(obj=oneRes,pdfnm = "oneRes")

## End(Not run)

Description

These functions provide summary results of costs, prices and revenues. Provided data can be dessagregated by fleet or by metier depending on the selected function.

Usage

revenue_flbeia(fleet)

costs_flbeia(fleet, covars, flnm = NULL)

totvcost_flbeia(fleet)

totfcost_flbeia(fleet, covars, flnm = NULL)

price_flbeia(fleet, stock)

Arguments

Details

• revenue_flbeia computes the revenue by fleet and metier. The revenue is computed as landings (weight) multiplied by the price.

• costs_flbeia computes total costs as the sum of fixed and variable costs.

• totvcost_flbeia computes the variable costs including crew share costs .

• totfcost_flbeia computes the total costs by vessel.

• price_flbeia computes the price by stock.

Description

Model formulation:

Usage

segregmix()

Details

$R = \mathbf{ifelse}(S \leq b, a S, \mathbf{ifelse}(S \leq B, a b, A B uncAdd))$

R = ifelse(S <= b, a*S, ifelse(S <= B, a*b, A*B*uncAdd)) a is the slope of the recruitment for stock levels at or below b, $a b$ is the mean recruitment for stock levels above b and at or below B, and $A B uncAdd$ is the mean recruitment for stock levels above B. Where a, b, A, B > 0.

Additional stock-recruitment (SR) models to the ones provided in FLCore package.

Author(s)

The FLBEIA Team

See Also

SRModels, FLSR, FLModel

Description

Estimates selectivity at age of an stock by a fleet and metier.

Usage

SelAtAge(
  biols,
  fleets,
  flnm = 1,
  mtnm = 1,
  stnm = 1,
  years,
  iter = NULL,
  restriction = 1,
  ntrials = 3
)

Arguments

Details

To calculate selectivity at age, the following formula is used:

$C_{a,f,m} = \frac{C_{f,m}}{sum_{i=a_0,...a+} S_{i,f,m} \cdot B_i} \cdot S_{a,f,m} \cdot B_a$

Where:

• a: age.

• f: fleet.

• m: metier.

• i: susbscript of age.

• $S_{a,f,m}$: selectivity at age 'a' for fleet 'f' and metier 'm'.

• $C_{a,f,m}$: catch (in weight) at age 'a' for fleet 'f' and metier 'm'.

• $C_{f,m}$: total catch for fleet 'f' and metier 'm'.

• $B_a$: biomass (in weight) at age.

Consult FLBEIA manual to see the derivation of the formula.

The equation above is nonlinear and therefore we cannot find an analytical expression for the selectivity. Rewriting the equation above for each 'a' we have de following equation:

$sum_{i=a_0,...a+} S_{i,f,m} \cdot B_i - \frac{C_{f,m}}{C_{a,f,m}} \cdot B_a \cdot S_{a,f,m}$

Thus we have a system of linear equations being Sa0fm,...,Sa+fm the unknown variables. The problem is that the equations in the system are not independent. Therefore, we have to remove one equation to get a system of independent equations. Furthermore, we have to add a constraint (an equation) to be able to solve the system.

To make sure that output is correct, we solve the sistem for each year and season several times (the number is set in ntrials), removing a different equation each time. Alway using the same constraint (restriction=1): $sum_{i=a_0,...a+} S_{i,f,m} = 1$ Finally, we compare the values of $S_{i,f,m}$ and we check if they are the same.

Value

A FLQuant with selectivity at age values in years. The rest of the years have value 0 for all ages.

Description

Function to remove the units from the FLQuants of an object

Usage

setUnitsNA(object)

## S4 method for signature 'FLBiol'
setUnitsNA(object)

## S4 method for signature 'FLBiols'
setUnitsNA(object)

## S4 method for signature 'FLFleetExt'
setUnitsNA(object)

## S4 method for signature 'FLFleetsExt'
setUnitsNA(object)

Arguments

Value

The same object with the units equal to NA in all the FLQuant slots.

Examples

data(one)

st <- setUnitsNA(oneBio)
fl <- setUnitsNA(oneFl)

data(multi)

biols <- setUnitsNA(multiBio)
fleets <- setUnitsNA(multiFl)

data(res_flbeia)

res_biols  <- setUnitsNA(multiRes$biols)
res_fleets <- setUnitsNA(multiRes$fleets)

Description

Indicates which stocks are caugth by each fleet-metier combination.

Usage

stock.fleetInfo(fleets)

Arguments

Value

Return a matrix with rownames equal to the stocks names and colnames equal to names of fleet and metier. If element (i,j) is equal to 0, the stock (i) is not caught by fleet/metier (j).

Examples

## Not run: 
data(multi)
stock.fleetInfo(fl1)

## End(Not run)

Description

Extract landings, discards or catch at age of a stock from a FLFleetExt or FLFleetsExt object.

Usage

tlandStock(obj, stknm)

tdiscStock(obj, stknm)

landStock.f(obj, stock)

discStock.f(obj, stock)

catchStock.f(obj, stock)

landWStock.f(obj, stock)

discWStock.f(obj, stock)

catchWStock.f(obj, stock)

landStock(obj, stock)

discStock(obj, stock)

catchStock(obj, stock)

landWStock(obj, stock)

discWStock(obj, stock)

catchWStock(obj, stock)

Arguments

Details

• landStock.f: Extract total landings at age in numbers of a stock from a FLFleetExt obj.

• discStock.f: Extract total discards at age in numbers of a stock from a FLFleetExt obj.

• catchStock.f: Extract total catch at age in numbers of a stock from a FLFleetExt obj.

• landWStock.f: Extract total landings at age in weight of a stock from a FLFleetExt obj.

• discWStock.f: Extract total discards at age in weight of a stock from a FLFleetExt obj.

• catchWStock.f: Extract total catch at age in weight of a stock from a FLFleetExt obj.

• landStock: Extract total landings at age in numbers of a stock from a FLFleetExts obj.

• discStock: Extract total discards at age in numbers of a stock from a FLFleetExts obj.

• catchStock: Extract total catch at age in numbers of a stock from a FLFleetExts obj.

• landWStock: Extract total landings at age in weight of a stock from a FLFleetExts obj.

• discWStock: Extract total discards at age in weight of a stock from a FLFleetExts obj.

• catchWStock: Extract total catch at age in weight of a stock from a FLFleetExts obj.

• tlandStock: Total landings of a stock across fleets and metiers

• tdiscStock: Total discards of a stock across fleets and metiers

Value

A FLQuant object with landings, discards or catch (total or at age).

Examples

## Not run: 
library(FLBEIA)
data(one)
landWStock.f(oneFl$fl1,"stk1")
landWStock(oneFl,"stk1")

## End(Not run)

Description

This function transforms a FQuant with several 'unit' dimension into an array an unique 'unit' dimension and no 'area' dimension. Moving 'unit' to 'age' dimension. This is usefull when we use the 'unit' dimension to store the different seasonal cohorts.

Usage

unit2age(Q)

Arguments

Value

A 4-dimensional array with length c(d[1]*d[3], d[2], d[4], d[6]), where d is the dimension of the FLQuant.

Note

The files must contain a single object (named as objnam value).

Description

Updates an old FLBiols (where slots fec and mat from each FLBiol are FLQuants), into the new version of the class, where slots fec and mat are of class predictModel.

Usage

updateFLBiols(biols)

Arguments

Value

A new FLBEIA output object with all the iterations joined.

See Also

predictModel

Description

Mean weight at age in discards for a stock across fleets and metiers

Usage

wtadStock(obj, stknm)

Arguments

Value

A FLQuant with mean weight-at-age values.

See Also

wtalStock

Description

Mean weight at age in landings for a stock across fleets and metiers

Usage

wtalStock(obj, stknm)

Arguments

Value

A FLQuant with mean weight-at-age values.

See Also

wtadStock