The Fisheries Library in R (FLR) is a collection of tools for quantitative fisheries science, developed in the R language, that facilitates the construction of bio-economic simulation models of fisheries systems as well as the application of a wide range of quantitative analysis.

FLR builds on the powerful R environment and syntax to create a domain-specific language for the quantitative analysis of the expected risks and impacts of fisheries management decisions. The classes and methods in FLR consider uncertainty an integral part of our knowledge of fisheries systems.

Required packages

To follow this tutorial you should have installed the following packages:

CRAN: latticeExtra, gridExtra, ggplot2
FLR: FLCore, FLa4a, ggplotFL, FLBRP, FLasher

You can do so as follows:

install.packages(c("latticeExtra", "gridExtra", "ggplot2"))
install.packages(c("FLCore", "ggplotFL", "FLa4a", "FLBRP", "FLasher"),
  repos=c(FLR="https://flr.r-universe.dev", CRAN="https://cloud.r-project.org"))

Classes and methods

The FLCore package defines a series of data structures that represent different elements of the fishery system. These structures are defined using R’s S4 class system, while functions to operate on those classes are defined as methods. In this way function call (e.g. plot()) can be used on different classes and their behaviour is consistent with the class structure and content. For further information on the S4 class system, please look at the introductory text on the methods package.

FLQuant

The FLQuant class is the basic data structure for FLR, and it is in essence a 6D array on which data and outputs can be stored, structured along dimensions related to age or length, year, unit (e.g. sex), season, area and iteractions. It is also able to keep track of the units of measurement employed, as a simple character vector, although various operations on these objects are aware of the units of measurements contained in them.

We will be creating and altering FLQuant objects as we go along, but we can look briefly at some basic operations, as the syntax will then be used in other classes in FLR.

Let’s first load the necessary packages in our session. The FLCore package

library(FLCore)
library(ggplotFL)

As it is the case for R classes, for example data.frame, classes in FLR can be constructed using a constructor method with the same name as the class, in this case FLQuant().

FLQuant(1:10)

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

     year
quant 1  2  3  4  5  6  7  8  9  10
  all  1  2  3  4  5  6  7  8  9 10

units:  NA

The FLQuant constructor can take a variety of inputs, like a vector, a matrix or an array, and also allows you to specify a number of arguments. We will only look now at three of them, units for the units of measurement, quant for the name of the first dimension, and dimnames for the dimension names of the object. For example, to construct an object holding (random) data in tonnes on catches for ages 1 to 4, over a period of 6 years, one could call

flq <- FLQuant(rlnorm(60), dimnames=list(age=1:4, year=2012:2017), units="t")

flq

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2012  2013  2014  2015  2016  2017 
  1 0.604 0.974 0.991 1.685 1.364 5.057
  2 1.938 2.099 3.139 0.850 0.534 0.700
  3 0.248 1.517 5.280 0.612 0.595 1.195
  4 3.147 6.164 1.062 0.936 1.432 0.815

units:  t

The object has six dimensions, although some of them (unit, season, area and iter) are not used in this case. We can inspect the object in various ways

# A summary of structure and data
summary(flq)

An object of class "FLQuant" with:

dim:    age year unit season area iter 
        4   6    1    1      1    1 
units:  t 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    %NAs 
   0.25    0.79    1.13    1.79    1.98    6.16    0.00

# dimnames
dimnames(flq)

$age
[1] "1" "2" "3" "4"

$year
[1] "2012" "2013" "2014" "2015" "2016" "2017"

$unit
[1] "unique"

$season
[1] "all"

$area
[1] "unique"

$iter
[1] "1"

# dims
dim(flq)

[1] 4 6 1 1 1 1

# units
units(flq)

[1] "t"

It can be also subset and modified. Note that contrary to arrays in R one doesn’t need to specify all dimension within square brackets, the FLR method understands there are more dimensions after the last one the user wants to use

# Extract (by location) the first year
flq[, 1]

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2012 
  1 0.604
  2 1.938
  3 0.248
  4 3.147

units:  t

# in a R array, to get the same results, we'd need to do
flq[, 1,,,,]

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2012 
  1 0.604
  2 1.938
  3 0.248
  4 3.147

units:  t

# Extract (by name) year 2013
flq[, "2013"]

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2013 
  1 0.974
  2 2.099
  3 1.517
  4 6.164

units:  t

# Set catches on age 1 to zero
flq[1] <- 0
flq

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2012  2013  2014  2015  2016  2017 
  1 0.000 0.000 0.000 0.000 0.000 0.000
  2 1.938 2.099 3.139 0.850 0.534 0.700
  3 0.248 1.517 5.280 0.612 0.595 1.195
  4 3.147 6.164 1.062 0.936 1.432 0.815

units:  t

as it can be used in many operations

# Product with scalar
flq * 10

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2012  2013  2014  2015  2016  2017 
  1  0.00  0.00  0.00  0.00  0.00  0.00
  2 19.38 20.99 31.39  8.50  5.34  7.00
  3  2.48 15.17 52.80  6.12  5.95 11.95
  4 31.47 61.64 10.62  9.36 14.32  8.15

units:  t

# Addition with another FLQuant of the same dimensions
flq + (flq * 0.20)

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2012  2013  2014  2015  2016  2017 
  1 0.000 0.000 0.000 0.000 0.000 0.000
  2 2.326 2.519 3.767 1.020 0.641 0.840
  3 0.298 1.821 6.336 0.735 0.714 1.435
  4 3.777 7.396 1.275 1.123 1.718 0.978

units:  t

# Sum along years
yearSums(flq)

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 1    
  1  0.00
  2  9.26
  3  9.45
  4 13.56

units:  t

More information

To learn more about the class, check the FLQuant help page.

Loading your data

The first step in an analysis is to get the relevant data into the system. We will need to construct objects of certain FLR classes, but first data is loaded into the R session using any of the tools available in the language: read.csv for CSV files, readVPA and others for fisheries legacy file formats, …

In this example we will create an object of class FLStock, a representation of both inputs and outputs involved in fitting a stock assessment model, from a CSV file containing a table with five columns:

slot, the name of the input quantity, in this case either landings at age, landings.n, weight-at-age in the landings, landings.wt, and the same quantities for discards, discards.n and discards.wt.
age and year
data, the numeric values themselves.
units, a character string for the units of measurement employed: kg and 1000 in this case for kilograms and thousands.

The file is downloaded into a temporary folder, and uncompressed. Simply change the value of dir to save the file in another folder.

dir <- tempdir()
download.file("http://www.flrproject.org/doc/src/ple4.csv.zip", file.path(dir, "ple4.csv.zip"))
unzip(file.path(dir, "ple4.csv.zip"), exdir=dir)

The CSV file can now be loaded as a data.frame using read.csv and inspected

dat <- read.csv(file.path(dir, "ple4.csv"))
head(dat)

slot	age	year	data	units
discards.n	1	1957	32356	1000
discards.n	2	1957	45596	1000
discards.n	3	1957	9220	1000
discards.n	4	1957	909	1000
discards.n	5	1957	961	1000
discards.n	6	1957	25	1000

This data.frame contains the time series of landings and discards at age, in thousands, and the corresponding mean weights-at-age, in kg, for North Sea plaice (Pleuronectes platessa, ICES ple.27.420).

We can create an object to store the landings-at-age data by subsetting it from the data.frame

landn <- subset(dat, slot=="landings.n", select=-slot)

and then convert into an FLQuant using

landsn <- as.FLQuant(landn)

The object can now be inspected and plotted using the FLR-defined methods

summary(landsn)

An object of class "FLQuant" with:

dim:    age year unit season area iter 
        10  52   1    1      1    1 
units:  1000 

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    %NAs 
      0    4548   14030   28141   40536  274209       0

plot(landsn)

In a similar way, we can now convert the input data.frame, containing data for four data elements, as specified in the slots column, into an FLStock object, nple4. The FLStock class has the following slots: catch, catch.n, catch.wt, discards, discards.n, discards.wt, landings, landings.n, landings.wt, stock, stock.n, stock.wt, m, mat, harvest, harvest.spwn and m.spwn. The names used in the slot column need to match the FLStock class for this method to work. See ?FLStock for more information.

nple4 <- as.FLStock(dat)

summary(nple4)

An object of class "FLStock"

Name:  
Description:  
Quant: age 
Dims:  age  year    unit    season  area    iter
    10  52  1   1   1   1   

Range:  min max pgroup  minyear maxyear minfbar maxfbar 
    1   10  10  1957    2008    1   10  

Metrics: 
  rec: NA - NA  (NA) 
  ssb: NA - NA  (NA) 
  catch: NA - NA  (NA) 
  fbar: NA - NA  (NA)

To complete this FLStock object we will need to specify the natural mortality, m, in this case as a constant value of 0.1 for all ages and years,

m(nple4) <- 0.1

the proportion of natural and fishing mortality that takes place before spawning, assumed to be zero in both cases,

m.spwn(nple4) <- harvest.spwn(nple4) <- 0

and the maturity at age, as the proportion mature, as a vector repeated for all years.

mat(nple4) <- c(0, 0.5, 0.5, rep(1, 7))

We now must compute the overall landings and discards, in biomass, from the age-disaggregated values,

landings(nple4) <- computeLandings(nple4)
discards(nple4) <- computeDiscards(nple4)

and then the catch slots from both landings and discards

catch(nple4) <- computeCatch(nple4, slot="all")

The mean weight-at-age in the stock needs to be provided. In this case we will assume it is the same as the one computed from the catch sampling programme

stock.wt(nple4) <- catch.wt(nple4)

We finalize by specifying the fully selected age range, used in the calculation of an overall index of fishing mortality, for example as the mean, using fbar(), or as the maximum value, using fapex() across those ages. This information is part of the object’s range.

range(nple4, c("minfbar", "maxfbar")) <- c(2, 6)

If we now inspect the resulting object we can see that all calculated slots have been assigned the corresponding units of measurement, except for those that will hold the estimates coming from a stock assessment model: stock, stock.n and stock.wt for the estimates of abundance, and harvest for the estimates of fishing mortality at age.

summary(nple4)

An object of class "FLStock"

Name:  
Description:  
Quant: age 
Dims:  age  year    unit    season  area    iter
    10  52  1   1   1   1   

Range:  min max pgroup  minyear maxyear minfbar maxfbar 
    1   10  10  1957    2008    2   6   

Metrics: 
  rec: NA - NA  (NA) 
  ssb: NA - NA  (NA) 
  catch: 78423 - 342985  (t) 
  fbar: NA - NA  (NA)

#plot(metrics(nple4, Catch=catch, Landings=landings))

plot(nple4)

data(ple4)

More information

Please see the Loading your data into FLR tutorial for more examples on how to get your data into into FLR.

Visualizing and plotting

As already shown, FLR classes always have a plot method defined that provides a basic visual exploration of the object contents. But you are not limited to those plots, and other methods are available to allow you to build the plots you need.

The FLCore package provides a set of those plot methods based on R’s lattice package, as well as versions of lattice’s methods that work directly on FLR objects, for example xyplot

xyplot(data~year|age, harvest(ple4), xlab="", ylab="", type="b", cex=0.5, pch=19)

In alternative the ggplotFL package, which we have already loaded, can also be used, which provides a new set of plot methods based on ggplot2, from which all the plots shown so far in this tutorial have been generated.

One important advantage of ggplot2 is that plots are returned as objects that can be modified and extended. For example, the basic plot for the abundances at age FLQuant using ggplotFL would be this one

plot(stock.n(ple4))

in which each age is shown in a different horizontal panel (facet). Now we can modify this plot in various ways by adding extra ggplot2 commands to it, for example by adding a label to the y axis, shading a certain period of interest

plot(stock.n(ple4)) +
  # Add y label
  ylab("Biomass (t)") +
  # Draw rectangle between years 1990 and 2000
  annotate("rect", xmin = 1990, xmax = 2000, ymin = 0, ymax = Inf,
    # in semi-transparent red
    alpha = .2, fill='red')

The basic method in ggplot2 taking a data input, ggplot, has also been defined for some FLCore class, which simplifies constructing plots based on objects of those classes from scratch. For example, we can plot our catch-at-age matrix from ple4 to show the signal of strong cohorts in the data using

ggplot(data=catch.n(ple4), aes(x=year, y=data, group=age)) +
  geom_line(aes(colour=as.factor(age))) +
  ylab("Total catch (t)") + xlab("") + theme(legend.position="none")

ggplotFL deals with multiple iterations in FLR objects by calculating quantiles along the iter dimension and plotting them as areas and lines. For example, an object with random lognormal noise around the catch time series would look like this

plot(rlnorm(250, log(catch(ple4)), 0.5))

where the 50% and 95% intervals are shown as a red region and a dotted line respectively, while the median value is shown as a black line.

Note that plot methods on both FLCore and ggplotFL work by converting various FLR objects to a class that lattice and ggplot2 can understand, data.frames.

Coercing a 6D array like an FLQuant to a 2D data.frame requires generating one column per dimension, named as those, plus an extra column for the values stored in the object. This column is named data, thus the use of data in various plot formulas above. For classes with multiple slots, like FLStock, an extra column is necessary, which will be called slot. Please check this help page to know more about the coercion methods for FLR objects.

More information

For further explanations on the use of the two plotting platforms available for FLR classes, please refer to the ggplotFL plotting FLR objects with ggplot2 and Plotting FLR objects using lattice tutorials.

Running a stock assessment

An important step in providing management advice for a fish stock is the estimation of current status, past history and stock productivity through a stock assessment (SA) model fit. The FLStock object we have created contains some of the inputs required for most SA models: catches, natural mortality, and maturity. What is now needed is some indication of changes in abundance over the period of exploitation, an index of abundance, derived from either research surveys or catch-per-unit-effort of commercial fleets.

Index of abundance

FLR has specific classes for this information, FLIndex or when multiple indices are available FLIndices. An index of abundance for the North Sea plaice stock is available as an example dataset in FLCore, and can be loaded using the data command

data(ple4.index)

This time series of abundances at age, covering from 1996 to 2017, was obtained during the ICES International Bottom Trawl Survey, as is one of the indices used by the Working Group on the Assessment of Demersal Stocks in the North Sea and Skagerrak (WGNSSK).

The FLIndex class is able to contain not only the index of abundance, in this case as an age-structured FLQuant, but also other information on variance, associated catches, effort, selectivity and catchability, which might be used by certain methods.

summary(ple4.index)

An object of class "FLIndex"

Name: BTS-Combined (all) 
Description: Plaice in IV                                            . Imported from VPA file. 
Type :  numbers 
Distribution :   
Quant: age 
Dims:  age  year    unit    season  area    iter
    10  22  1   1   1   1   

Range:  min max pgroup  minyear maxyear startf  endf 
    1   10  1   1996    2017    0.6453376   0.6453376

plot(ple4.index)

The range slot in this class contains information about the time period over which the survey takes place, as proportions of the year, in this case from the beginning of August to the end of September.

range(ple4.index)[c("startf", "endf")]

   startf      endf 
0.6453376 0.6453376

a4a statistical catch-at-age

The FLa4a package provides an FLR implementation of the a4a model, a statistical catch-at-age model of medium complexity that has been designed to make use of the power and flexibility of R’s formula-based model notation (as used for example in lm), while giving access to a solid and fast minimization algorithm implemented using AD Model Builder.

library(FLa4a)

A first go at fitting this model to the ple4 and ple4.index datasets requires a single line of code

fit <- sca(ple4, ple4.index)

This returns an object of class a4aFitSA that contains the results of the model fit, including the estimated catch-at-age, and the derived abundances and fishing mortality at age

summary(fit)

An object of class "a4aFitSA"

Name: PLE 
Description: Plaice in IV. ICES WGNSSK 2018. FLAAP 
Quant: age 
Dims:  age  year    unit    season  area    iter
    10  61  1   1   1   1   

Range:  min max pgroup  minyear maxyear minfbar maxfbar 
    1   10  10  1957    2017    2   6

which we can use to update the FLStock object

stk <- ple4 + fit
plot(stk)

Among other methods, FLa4a provides a range of tools for inspecting the quality of the model fit.

Exploring the stock-recruitment relationship

Fitting a model for the relationship between stock abundance and expected recruitment is necessary for forecasting the future dynamics of the stock under various levels of fishing mortality, the central element of fisheries management advice. Certain stock assessment models, for example most statistical catch-at-age models, include the stock-recruitment model in the estimated parameters, but for others, like Virtual Population Analysis, this is a setup carried out once the stock and population abundance have been obtained.

Both inputs and outputs for stock-recruitment models are stored in an object of the FLSR class which we can create directly from the stock assessment results by coercion using

plsr <- as.FLSR(stk)

The resulting FLSR object contains the series of recruitment rec, the abundance of the first age, and the stock reproductive potential, in this case the spawning stock biomass ssb. Both series are already displaced by the age of recruitment, 1 in this case.

summary(plsr)

An object of class "FLSR"

Name: PLE 
Description: 'rec' and 'ssb' slots obtained from a 'FLStock' object 
Quant: age 
Dims:  age  year    unit    season  area    iter
    1   60  1   1   1   1   

Range:  min minyear max maxyear 
    1   1958    1   2017    


Model:  list()
An object of class "FLPar"
character(0)
units:  NA 
Log-likelihood:  NA(NA) 
Variance-covariance: <0 x 0 matrix>

We now need to choose a model to fit, and FLCore provides a number of them already defined in terms of:

the model formula, kept in the model slot
a function returning the log-likelihood, to be used for minimization, in the logl slot
a function to obtain initial values for the minimization procedure, in the initial slot

See the SR Models help page for further details of the available models.

We can now assign the output of the chosen model function, in this case ricker(), to the existing object using

model(plsr) <- ricker()

The model object is ready to fit, through Maximum Likelihood Estimation (MLE), using the fmle method defined in FLCore. This makes use of R’s optim, but defaults to the Melder-Mead algorithm.

plsr <- fmle(plsr)

Another look at the object will show us that a number of slots now contain the results of the model fit, among them:

fitted for the estimated recruitment, as an FLQuant
residuals for the residuals in log space, FLQuant
params for the estimated parameters, as an object of class FLPar
vcov for the variance-covariance matrix, as an array

The standard plot for the class shows the model fit and a series of useful diagnostics that will help us evaluating its quality and usefulness for future projections. This can be further explored by likelihood profiling over a range of values of the estimated parameters by simply calling the profile method of the object

profile(plsr)

Finally, the model can be used to predict future recruitments if provided with a set of new values of SSB, as in

predict(plsr, ssb=FLQuant(rnorm(10, 25e4, sd(ssb(plsr))), dimnames=list(age=1, year=2008:2017)))

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

   year
age 2008      2009      2010      2011      2012      2013      2014     
  1 -10008692   1322964     45880    957730    893855    872758  -1209982
   year
age 2015      2016      2017     
  1    805894  -1329512   1032422

units:  1000

More information

Please see the Modelling stock recruitment with FLSR tutorial for more examples and information on the stock-recruitment model fitting capabilities of FLR.

Estimating reference points

To assess current and future status of a stock we need reference points which we hold to represent desirable states for the stock, usually either in terms of stock abundance, like \(B_{MSY}\), the biomass expected to produce, on average and under the current exploitation pattern, the Maximum Sustainable Yield (\(MSY\)), or of fishing mortality, such as the fishing mortality expected to produce \(MSY\), \(F_{MSY}\).

We can use the FLBRP package to calculate a large set of reference points, once we have an estimate of stock status, our FLStock object, and of the stock-recruitment relationship, FLSR. We can use them both to create the necessary object, of class FLBRP, after loading the package

library(FLBRP)
plrp <- FLBRP(stk, sr=plsr)
summary(plrp)

An object of class "FLBRP"

Name:  
Description:  
Quant: age 
Dims:  age  year    unit    season  area    iter
    10  101 1   1   1   1   

Range:  min max pgroup  minfbar maxfbar 
    1   10  10  2   6   


Model:  rec ~ a * ssb * exp(-b * ssb)
    params
iter    a        b
   1 4.81 1.33e-06

refpts:  NA

An object of the FLBRP class contains a series of times series of observations on dynamics of both stock and fishery, as well as average quantities computed along a specified time period. A series of slots are designed to contain economic variables, such as price, variable costs (vcost) and fixed costs (fcost), which will need to be provided. When those data are available, economic reference points can also be calculated. Finally, a vector of fishing mortality values, fbar, has been created, by default to be of length 101 and with values going from \(F=0\) to \(F=4\), to be used in the calculation procedure.

We can now proceed with the calculation of reference points, through the brp method

plrp <- brp(plrp)

and the extraction of the values obtained, stored in the refpts slot of the object

refpts(plrp)

An object of class "FLPar"
        quant
refpt    harvest  yield    rec      ssb      biomass  revenue  cost    
  virgin 0.00e+00 0.00e+00 6.14e+05 2.10e+06 2.16e+06       NA       NA
  msy    2.63e-01 1.03e+05 1.31e+06 8.89e+05 1.01e+06       NA       NA
  crash  5.10e-01 9.18e-07 1.71e-05 3.56e-06 4.87e-06       NA       NA
  f0.1   1.52e-01 8.28e+04 1.06e+06 1.37e+06 1.47e+06       NA       NA
  fmax   2.05e-01 9.73e+04 1.20e+06 1.13e+06 1.25e+06       NA       NA
  spr.30 1.91e-01 9.43e+04 1.17e+06 1.20e+06 1.31e+06       NA       NA
  mey          NA       NA       NA       NA       NA       NA       NA
        quant
refpt    profit  
  virgin       NA
  msy          NA
  crash        NA
  f0.1         NA
  fmax         NA
  spr.30       NA
  mey          NA
units:  NA

This slot contains the estimates for a series of reference points and for a number of quantities. For example, the value stored at the intersection of msy and harvest corresponds to \(F_{MSY}\), while that of virgin and ssb is the estimate of virgin stock biomass. We can, for example, extract the two standard MSY-related reference points as a named vector using

pmsy <- refpts(plrp)["msy", c("harvest", "ssb"), drop=TRUE]

to then use them to visualize the status of the stock in relation to these points, for example

plot(ssb(stk) / pmsy["ssb"]) + geom_hline(aes(yintercept=1), linetype=2) +
  ylab(expression(SSB / SSB[MSY]))

More information

For more information on FLBRP, please see the Reference points for fisheries management with FLBRP tutorial.

Forecasting under different scenarios

Our final step in this quick tour takes us to the tools in FLR for forecasting the future responses of the stock to alternative management decisions. This example concerns a Short Term Forecast, in which the effect of the current management, for example a maximum catch limit or TAC, on the present year is forecast, and then a series of alternative increases or decreases in catch are applied to the object representing our current understanding of the stock status and dynamics.

The FLasher package contains the current implementation of the forecasting mechanism in FLR.

library(FLasher)

The key element in the FLasher interface is the fwdControl class, which allows us to specify the targets and limits that we want the stock to achieve or remain within. But first we need to make some assumptions about the future values of certain quantities, both on the biology (natural mortality, maturity) and the fishery (selectivity, proportion of discards). A common approach for this kind of forecast is to use averages over the last few years (typically three) for certain quantities. This can be done in FLR using the fwdWindow method in the FLash package.

proj <- fwdWindow(stk, end=2020)

Our new object extends up to 2020, and the values for certain stocks have been extended using the mean of the last three years, for example for the weight-at-age in the stock

stock.wt(proj)[, ac(2011:2020)]

An object of class "FLQuant"
, , unit = unique, season = all, area = unique

    year
age  2011   2012   2013   2014   2015   2016   2017   2018   2019   2020  
  1  0.0390 0.0520 0.0430 0.0480 0.0240 0.0300 0.0320 0.0287 0.0287 0.0287
  2  0.1000 0.0930 0.1070 0.1040 0.0650 0.0660 0.0690 0.0667 0.0667 0.0667
  3  0.1870 0.1420 0.1530 0.1580 0.1200 0.1170 0.1320 0.1230 0.1230 0.1230
  4  0.2090 0.1880 0.2080 0.2020 0.2070 0.1980 0.1810 0.1953 0.1953 0.1953
  5  0.3550 0.3310 0.3200 0.3120 0.2790 0.2600 0.2700 0.2697 0.2697 0.2697
  6  0.4830 0.3930 0.3540 0.3800 0.3230 0.3290 0.3330 0.3283 0.3283 0.3283
  7  0.4380 0.4840 0.4340 0.4390 0.3790 0.3800 0.3590 0.3727 0.3727 0.3727
  8  0.4220 0.4790 0.4930 0.4840 0.4350 0.4340 0.4580 0.4423 0.4423 0.4423
  9  0.5300 0.4800 0.6620 0.4580 0.4650 0.4790 0.4760 0.4733 0.4733 0.4733
  10 0.5800 0.5180 0.4680 0.6150 0.4570 0.5140 0.5570 0.5093 0.5093 0.5093

units:  kg

We can now construct a fwdControl object that projects the stock as if catches in the current year, 2018 in this case, were those of the agreed TAC, 85,000 t, and F was then kept in 2019 and 2020 at the same levels as in 2017. The main information to be passed to fwdControl object is

year
quant, the quantity to be projected, for example ssb, f or catch
value, the actual value to be obtained

TAC <- 85000
Flevel <- fbar(stk)[,"2017"]
ctrl <- fwdControl(year=2018:2020, quant=c("catch", "f", "f"), value=c(TAC, Flevel, Flevel))

Now the fwd method will carry out the projection using those three object: stock, stock-recruitment and control

proj <- fwd(proj, control=ctrl, sr=plsr)

and the results can be visualized

plot(proj) + geom_vline(aes(xintercept=2017.5), linetype=2)

More information

Please see the tutorial Forecasting on the Medium Term for advice using FLasher for more examples on how to use the capabilities of FLasher to forecast population dynamics under various scenarios.

More on FLR

Please visit our website for more information on the FLR packages, tutorials and examples of use, and instructions for getting in touch if you want to report a bug, ask a question or get involved in the development of the FLR toolset. The project is run by a team of fisheries scientists that decided to share the R code they were writing and realised that using a common platform for Quantitative Fisheries Science in R could break the Babel tower spell that J. Schnute first identified

“The cosmic plan for confounding software languages seems to be working remarkably well among the community of quantitative fishery scientists!”

Schnute et al. 2007

References

L. T. Kell, I. Mosqueira, P. Grosjean, J-M. Fromentin, D. Garcia, R. Hillary, E. Jardim, S. Mardle, M. A. Pastoors, J. J. Poos, F. Scott, R. D. Scott; FLR: an open-source framework for the evaluation and development of management strategies. ICES J Mar Sci 2007; 64 (4): 640-646. doi: 10.1093/icesjms/fsm012.

More information

You can submit bug reports, questions or suggestions on this tutorial at https://github.com/flr/doc/issues.
Alternatively, send a pull request to https://github.com/flr/doc/.
For more information on the FLR Project for Quantitative Fisheries Science in R, visit the FLR webpage: http://flrproject.org.

Software Versions

R version 4.5.1 (2025-06-13)
FLCore: 2.6.20.9342
ggplotFL: 2.7.0.9139
FLa4a: 1.9.5
FLBRP: 2.5.9.9051
FLash: 2.5.11
Compiled: Wed Oct 29 18:02:23 2025

License

This document is licensed under the Creative Commons Attribution-ShareAlike 4.0 International license.

Author information

Iago MOSQUEIRA. Wageningen Marine Research (WMR), IJmuiden, The Netherlands.

Ernesto Jardim. Instituto Portugues do Mar e Atmosfera (IPMA), Lisbon, Portugal
National Institute of Aquatic Resources, Technical University of Denmark (DTU-Aqua), Copenhagen, Denmark.

A quick introduction to FLR

29 October, 2025

Required packages

Classes and methods

FLQuant

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Loading your data

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Visualizing and plotting

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Running a stock assessment

Index of abundance

a4a statistical catch-at-age

Exploring the stock-recruitment relationship

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Estimating reference points

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Forecasting under different scenarios

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More on FLR

References

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Software Versions

License

Author information