This package contains functions to run the Length-Based Spawning Potential Ratio (LBSPR) method. The LBSPR package can be used in two ways: 1) simulating the expected length composition, growth curve, and SPR and yield curves using the LBSPR model and 2) fitting to empirical length data to provide an estimate of the spawning potential ratio (SPR).
The LBSPR method has been developed for data-limited fisheries, where few data are available other than a representative sample of the size structure of the vulnerable portion of the population (i.e., the catch) and an understanding of the life history of the species. The LBSPR method does not require knowledge of the natural mortality rate (M), but instead uses the ratio of natural mortality and the von Bertalanffy growth coefficient (K) (M/K), which is believed to vary less across stocks and species than M (Prince et al. 2015).
Like any assessment method, the LBSPR model relies on a number of simplifying assumptions. In particular, the LBSPR models are equilibrium based, and assume that the length composition data is representative of the exploited population at steady state. See the publicaitons listed in the reference list for full details of the assumptions of the model, including simulation testing to evauate the effect of violations of these assumptions.
There are two versions of the LBSPR model included in this package.
The LBSPR model described by Hordyk et al. (2015a, b), and tested in a MSE framework (Hordyk et al. 2015c), use a conventional age-structured equilibrium population model. An important assumption of this model structure is that selectivity is age-based not length-based.
Hordyk et al. (2016) describe a length-structured version of the LBSPR model that uses growth-type-groups (GTG) to account for size-based selectivity. The GTG-LBSPR model also has the ability to include variable M at size (by default M is assumed to be constant). The GTG-LBSPR model typically estimates a lower fishing mortality rate for a given size structure compared to the earlier age-structured model. This is because the age-structured model has a ‘regeneration assumption’, where, because of the age-based selectivity assumption, large individuals are expected even at high fishing mortality (large, young fish).
The default setting for the LBSPR package is to use the GTG-LBSPR model for all simulation and estimation. Control options in the simulation and estimation functions can be used to switch to the age-structured LBSPR model.
Please alert me to any bugs or issues by using GitHub.
Comments and suggestions for additional features are welcome. GitHub pull requests with modifications or extensions are even more welcome!
Finally, please make sure you understand the data and the biological parameters (and how the model treats these) and critically evaluate any output of the LBSPR model.
The LBSPR package is now available on CRAN:
install.packages("LBSPR")You can install the development version of the package from GitHub using the devtools package:
install.packages("devtools")
devtools::install_github("AdrianHordyk/LBSPR")library(LBSPR)The LBSPR package can be used to generate the expected size composition, the SPR, and relative yield for a given set of biological and exploitation pattern parameters.
LB_pars ObjectThe first thing to do is to create a LB_pars object that contains all of the required parameters for the simulation model. LB_pars is an S4 class object.
The S4 system is different to the S3 system that is commonly used in R, and that R users are familiar with. Don’t worry if you’ve never used S4 objects before. The main thing to know is that elements in a S4 object are called slots and you access them using the @ symbol (rather than the $ symbol that is used with data.frames).
You can read more about S4 objects here.
LB_pars ObjectTo create a new LB_pars object you use the new function:
MyPars <- new("LB_pars")## A blank LB_pars object created## Default values have been set for some parametersYou can see the elements or slots of the LB_pars object using the slotNames function:
slotNames(MyPars)## [1] "Species" "MK" "M" "Linf"
## [5] "L_units" "CVLinf" "L50" "L95"
## [9] "Walpha" "Walpha_units" "Wbeta" "FecB"
## [13] "Steepness" "Mpow" "R0" "SL50"
## [17] "SL95" "MLL" "sdLegal" "fDisc"
## [21] "FM" "SPR" "BinMin" "BinMax"
## [25] "BinWidth"MyPars is an object of class LB_pars. You can access the help file for classes by using the ? symbol (similar to how you find the help file for functions):
class?CLASSNAME
class?LB_parsLB_pars ObjectThe LB_pars object has 25 slots. However, not all parameters need to be specified for the simulation model.
Some parameters are essential, and a warning message should appear if you attempt to progress without values (please let me know if there are issues).
Default values will be used for some of the other parameters if no value is specified. For example, the first slot (Species) is a character object that can be used for the species name. If this slot is left empty, the simulation model will populate it with a default value.
A message should alert you any time a default value is being used. Again, please let me know if you find something strange going on.
The minimum parameters that are needed for the simulation model are:
Biology
Linf)MK)L50)L95)Exploitation - Length at 50% selectivity (SL50) - Length at 95% selectivity (SL95) - F/M ratio (FM) or SPR (SPR). If you specify both, the F/M value will be ignored.
Size Classes - Width of the length classes (BinWidth)
Remember, you can find the help documentation for the LB_pars object by typing: class?LB_pars in the console.
To create an example parameter object:
MyPars@Linf <- 100
MyPars@L50 <- 66
MyPars@L95 <- 70
MyPars@MK <- 1.5
MyPars@SL50 <- 50
MyPars@SL95 <- 65
MyPars@SPR <- 0.4
MyPars@BinWidth <- 5Now we are ready to run the LBSPR simulation model. To do this we use the LBSPRsim function:
MySim <- LBSPRsim(MyPars)## BinMax not set. Using default of 1.3 Linf## BinMin not set. Using default value of 0You will notice some messages in the console alerting you that default values have been used. You can change these by specifying values in MyPars and re-running the LBSPRsim function.
We’ll manually set those values here so we don’t keep seeing the messages throughout the vignette.
MyPars@BinMax <- 150
MyPars@BinMin <- 0We can also choose to set the units for the length parameters:
MyPars@L_units <- "mm"LB_obj ObjectThe output of the LBSPRsim function is an object of class LB_obj. This is another S4 object, and contains all of the information from the LB_pars object and the output of the LBSPRsim function.
Many of the functions in the LBSPR package return an object of class LB_obj. You should not modify the LB_obj object directly. Rather, make changes to the LB_pars object (MyPars in this case), and re-run the simulation model (or other functions, covered later in the vignette).
Let’s take a look at some of the simulated output.
MySim@SPR ## [1] 0.4The simulated SPR is the same as our input value (MyPars@SPR).
What is the ratio of fishing mortality to natural mortality in this scenario?
MySim@FM ## [1] 0.65It is important to note that the F/M ratio reported in the LBSPR model refers to the apical F over the adult natural mortality rate. That is, the value for fishing mortality refers to the highest level of F experienced by any single size class.
If the selectivity pattern excludes all but the largest individuals from being exploited, it is possible to have a very high F/M ratio in a sustainable fishery (high SPR).
A couple of more simulations with alternative values:
Specify F/M instead of SPR:
MyPars@SPR <- numeric() # remove value for SPR
MyPars@FM <- 1 # set value for FM
MySim <- LBSPRsim(MyPars)
round(MySim@SPR, 2) # SPR at F/M = 1 ## [1] 0.27Change the life history parameters:
MyPars@MK <- 2.0
MySim <- LBSPRsim(MyPars)
round(MySim@SPR, 2) # SPR ## [1] 0.24MyPars@MK <- 0.5
MySim <- LBSPRsim(MyPars)
round(MySim@SPR, 2) # SPR ## [1] 0.36MyPars@Linf <- 120
MySim <- LBSPRsim(MyPars)
round(MySim@SPR, 2) # SPR ## [1] 0.35Change selectivity parameters:
MyPars@MK <- 1.5
MyPars@SL50 <- 10
MyPars@SL95 <- 15
MyPars@FM <- 1
MySim <- LBSPRsim(MyPars)
round(MySim@SPR, 2) # SPR ## [1] 0.13MyPars@SL50 <- 80
MyPars@SL95 <- 85
MySim <- LBSPRsim(MyPars)
round(MySim@SPR, 2) # SPR ## [1] 0.57There are a number of additional parameters that can be modified to control other aspects of the simulation model.
For example, by default the LBSPR model using the Growth-Type-Group model (Hordyk et at. 2016). The Control argument can be used to switch to the Age-Structured model (Hordyk et al. 2015a, b):
MyPars@Linf <- 100
MyPars@SL50 <- 50
MyPars@SL95 <- 55
MyPars@FM <- numeric()
MyPars@SPR <- 0.4
MySim <- LBSPRsim(MyPars, Control=list(modtype="absel"))
MySim@FM ## [1] 0.67MySim <- LBSPRsim(MyPars, Control=list(modtype="GTG"))
MySim@FM # lower F/M for the GTG model## [1] 0.64See the help file for the LBSPRsim function for additional parameters for the Control argument.
The plotSim function can be used to plot MySim:
plotSim(MySim)