Package 'rFAMS'

Description

Simulate yield under minimum length regulations using the Dynamic Pool (DPM) model with (possibly) multiple values for conditional fishing mortality (cf) and conditional natural mortality (cm).

Usage

dpmBH_MinLL(
  minLL,
  cf,
  cm,
  rec,
  lhparms,
  simyears,
  species = NULL,
  group = NULL,
  matchRicker = FALSE
)

Arguments

Details

Details will be filled out later.

Note that the main calculations are in the internal dpmBH_func (use rFAMS:::dpmBH_func to see that source code).

Value

A list with two data.frame object. The first list item named sumbyAge contains a data.frame with the following calculated values in a summary by age:

• year is the year number for the simulation

• ycis the year class number for the simulation

• age is the age of fish from the year class

• length is the length-at-age at the beginning of the year based on parameters supplied for the von Bertlanffy growth model.

• weight is the total weight at the beginning of the year for length-at-age based on the parameters supplied for the weight-length model.

• N_start is the number of fish alive at the start of the year for the given age and year class.

• exploitation is the exploitation rate at age based on the supplied conditional fishing mortality rate.

• expect_nat_death is the expectation of natural death based on the supplied conditional natural mortality rate.

• cf is the supplied conditional fishing mortality rate.

• cm is the supplied conditional natural mortality rate.

• F is the instantaneous rate of fishing mortality.

• M is the instantaneous rate of natural mortality.

• Z is the instantaneous rate of total mortality.

• S is the (total) annual rate of survival.

• biomass is the total biomass of fish at age and year.

• N_harvest is the total number of fish harvested at age and year.

• N_die is the total number of fish that die at age and year.

• yield is the estimated yield (in g).

• minLL is the minimum length limit specified in the simulation.

For convenience the data.frame also contains the model input values (N0, Linf, K, t0, LWalpha, LWbeta, and tmax).

The second list item named sumbyYear contains a data.frame with the following calculated values in a summary by year:

• year is the year number for the simulation

• Age_1plus is the total number of fish age-1 plus per year.

• Yield_Age_1plus is the total year of age-1 plus fish per year.

• Total_biomass is the total biomass of age-1 plus fish per year.

• N_harvest_Age_1plus is the number of age-1 plus fish that are harvested per year.

• N_die_Age_1plus is the number of age-1 plus fish that die per year.

• substock is the number of substock sized fish at age and year at the beginning of the year.

• stock is the number of stock sized fish at age and year at the beginning of the year.

• quality is the number of quality sized fish at age and year at the beginning of the year.

• preferred is the number of preferred sized fish at age and year at the beginning of the year.

• memorable is the number of memorable sized fish at age and year at the beginning of the year.

• trophy is the number of trophy sized fish at age and year at the beginning of the year.

• PSD is the number of quality sized fish divided by the number of stock sized multiplied by 100.

• PSD_P is the number of preferred sized fish divided by the number of stock sized multiplied by 100.

• PSD_M is the number of memorable sized fish divided by the number of stock sized multiplied by 100.

• PSD_T is the number of trophy sized fish divided by the number of stock sized multiplied by 100.

PSD-X are calculated based on the number of fish in each category (stock, quality, preferred, memorable, and trophy) at the beginning of the year. That is, the length-at-age during the start of the year is used to assign PSD-X categories at age. For example, if Quality size is 300mm, an age-1 fish at 275mm at the start of the year would not be counted as a quality-sized fish, but an age-2 fish at 325mm at the start of the year would be counted as a quality-sized fish.

Author(s)

Jason C. Doll, [email protected]

See Also

yprBH_MinLL for estimating yield with a yield-per-recruit model using a minimum length limit and yprBH_SlotLL for estimating yield with the yield-per-recruit model and a slot limit.

See this demonstration page for more examples of this function.

Examples

#load required library
library(dplyr)
library(ggplot2)

# Example of simulating yield with the dynamic pool model,

lhparms <- makeLH(N0=100,tmax=30,Linf=1349.5,K=0.111,t0=0.065,
            LWalpha=-5.2147,LWbeta=3.153)
simyears <- 50
minLL <- 400
rec <- genRecruits(method = "fixed", nR = 100, simyears = simyears)
cm <- matrix(rep(c(rep(0,1), rep(0.18,(lhparms$tmax))), simyears),nrow=simyears,byrow=TRUE)
cf <- matrix(rep(c(rep(0,1), rep(0.33,(lhparms$tmax))), simyears),nrow=simyears,byrow=TRUE)

out<-dpmBH_MinLL(simyears = simyears, minLL = minLL, cf = cf,
                 cm = cm, rec = rec, lhparms = lhparms,
                 matchRicker=FALSE,species="Striped Bass",group="landlocked")

#Use summary by year data frame to plot yield vs year
ggplot(data=out[[2]],mapping=aes(x=year,y=Yield_age_1plus)) +
  geom_point() +
  geom_line() +
  labs(y="Total yield (g)",x="Year") +
  theme_bw()

#Plot date using summary by age
#filter for year class = 1
plotdat<- out[[1]] |> filter(yc==1)
#Plot yield vs age
ggplot(data=plotdat,mapping=aes(x=age,y=yield)) +
  geom_point() +
  geom_line() +
  labs(y="Total yield (g)",x="Age") +
  theme_bw()

#Recruitment based on a normal distribution
rec <- genRecruits(method = "normal", simyears = simyears,
                   meanR = 1000, sdR = 500, minR = 100, maxR =2500)
cm <- matrix(rep(c(rep(0,1), rep(0.18,(lhparms$tmax))), simyears),nrow=simyears,byrow=TRUE)
cf <- matrix(rep(c(rep(0,1), rep(0.33,(lhparms$tmax))), simyears),nrow=simyears,byrow=TRUE)

out_2<-dpmBH_MinLL(minLL = minLL, cf = cf, cm = cm,
                   rec = rec, lhparms = lhparms,simyears = simyears,
                   species="Striped Bass",group="landlocked",matchRicker=FALSE)

#Use summary by year data frame to plot yield vs year
ggplot(data=out_2[[2]],mapping=aes(x=year,y=PSD)) +
  geom_point() +
  geom_line() +
  labs(y="PSD",x="Year") +
  theme_bw()

#Plot date using summary by age
#Plot yield vs age for each year class
ggplot(data=out_2[[1]],mapping=aes(x=age,y=yield,group=yc,color=yc)) +
  geom_point() +
  geom_line() +
  labs(y="Total yield (g)",x="Age") +
  theme_bw()

Description

Several methods may be used to estimate instantaneous (M) and conditional natural mortality (cm) from other types of data, especially those saved in the life history parameters vector/list from makeLH.

Usage

est_natmort(lhparms = NULL, method = "rFAMS", incl.avg = FALSE, ...)

Arguments

Details

The default methods to use are all of those listed in Mmethods that use some of the life history parameters required by makeLH. These methods are not all equally useful or robust, so the user may want to select a subset of them for use after learning more about them. See references in metaM.

Other methods that require parameters other than those required by makeLH can be used by providing the name of the method in method and the required parameters as arguments, as defined in metaM. See metaM for more details and the examples below for an example.

Value

A data.frame with the following items:

• method: The name for the method within the function (as given in method).

• M: The estimated instantaneous natural mortality rate (from metaM)

• cm: The estimated conditional natural mortality rate (computed directly from M).

• givens: A string that contains the input values required by the method to estimate M.

Author(s)

Derek Ogle

Examples

# An example lhparm as would be returned from makeLH
tmp <- list(N0=100,tmax=15,Linf=500,K=0.3,t0=-0.5,LWalpha=-5.16,LWbeta=3.1)

# All methods in metaM() that use those life history parameters
est_natmort(tmp)

# Same but including the average in the last row
est_natmort(tmp,incl.avg=TRUE)

# Selecting just one method
est_natmort(tmp,method="HoenigNLS")

# Selecting several methods
est_natmort(tmp,method=c("HoenigNLS","HoenigO","HoenigO2","HoenigLM"))

# A method that uses a parameter not usually in lhparms
est_natmort(tmp,method="QuinnDeriso",PS=0.05)

# Selecting all Hoenig methods using Mmethods from FSA
est_natmort(tmp,method=FSA::Mmethods("Hoenig"))

# Over-riding the Linf param in parameters list, but others from tmp
est_natmort(tmp,method="PaulyLNoT")              # Linf from tmp
est_natmort(tmp,Linf=1000/10,method="PaulyLNoT") # Linf from Linf= arg

Description

This function is used to generate number of recruits across multiple years using different random functions.

Usage

genRecruits(
  simyears,
  method = c("fixed", "uniform", "normal", "StrYC_Nth", "StrYC_randInt"),
  nR = NULL,
  minR = NULL,
  maxR = NULL,
  meanR = NULL,
  sdR = NULL,
  nStr = NULL,
  sizeStr = NULL,
  avgFreq = NULL
)

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

Arguments

Value

A vector that contains the number of recruits for each simulation that can be used directly in the dynamic pool model (e.g., dpmBH_MinLL).

Author(s)

Jason C. Doll, [email protected]

Examples

# Generate recruits for 20 years based on a fixed number
rec <- genRecruits(simyears=20,method="fixed",nR=50)
rec

# Generate recruits for 20 years from a uniform distribution bound
# by 25 and 75
rec <- genRecruits(simyears=20,method="uniform",minR=25,maxR=75)
rec

# Generate recruits for 20 years based on a normal distribution with a mean
# of 50, standard deviation of 10, and trucated to be between 25 and 75
rec <- genRecruits(simyears=20,method="normal",minR=25,maxR=75,meanR=50,sdR=10)
rec

# Geneate recruits for 20 years based on a fixed number of recruits at 50 and
# a strong year class every 5 years with recruits 2 times the mean recruits
rec <- genRecruits(simyears=20,method="StrYC_Nth",nR=50,sizeStr=2,nStr=5)
rec

# Generate recruits for 20 years based on a fixed number of recruits at 50
# and a strong year class at random intervals of size 2 times the mean
# recruitswith the random interval averaging every 5 years.
rec <- genRecruits(simyears=20,method="StrYC_randInt",nR=50,sizeStr=2,avgFreq=5)
rec

Description

Efficiently construct either a vector or list that contains the seven life history parameters required for Beverton-Holt yield-per-recruit analyses. The parameters can be given by the user through function arguments. Alternativvely, the von Bertalanffy parameters (Linf, K, and t0) may be extracted from an nls object created from fitting the von Bertalanffy equation to length-at-age data (object created outside this function). Similarly the log10-transformed weight-length model coefficients may be extracted from an lm object created from fitting the model to transformed weight-length data (object created outside this function). All parameter values are checked for sanity (e.g., Linf>0).

Usage

makeLH(N0, tmax, Linf, K, t0, LWalpha, LWbeta, restype = c("list", "vector"))

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

Arguments

Details

Use of this function for putting life history parameters into a list or vector is recommended as (i) values for Linf, K, t0, LWalpha, and LWbeta can be extracted from objects from appropriate model fitting and (ii) checks for impossible or improbable values for each parameter are performed; i.e.,

# Best practice for entering life history parameter values
LH <- makeLH(N0=100,tmax=15,Linf=600,K=0.30,t0=-0.6,
             LWalpha=-5.453,LWbeta=3.10)

# Works but no checks on the values
LH <- list(N0=100,tmax=15,Linf=600,K=0.30,t0=-0.6,
           LWalpha=-5.453,LWbeta=3.10)

If a list is returned then values will be displayed with the number of decimals provided by the user. If a vector is returned then the number of decimals displayed will be the same for each value and will match the value supplied by the user with the most decimals. Thus, a list is preferred as it will be easier to match what was given to what was expected to be given.

Value

A named list or vector (depending on restype) that contains the given (or extracted) life history parameters values that can be used directly in the yield-per-recruit calculation functions (e.g., yprBH_SlotLL).

Author(s)

Derek Ogle

See Also

This demonstration page for further examples.

Examples

library(FSA)
library(FSAdata)
library(dplyr)

# ----- Simple examples with explicit arguments for each --------------------
makeLH(N0=100,tmax=15,Linf=500,K=0.3,t0=-0.5,LWalpha=-5.613,LWbeta=3.1)
makeLH(N0=100,tmax=15,Linf=500,K=0.3,t0=-0.5,LWalpha=-5.613,LWbeta=3.1,
       restype="vector")

# ----- Example of extracting values from model fits ------------------------
# N0 and tmax provided as arguments ... Linf, K, and t0 extracted from nls
#   output and LWalpha and LWbeta extracted from lm output. Note that nls
#   and lm output here are just examples of the function, they should be
#   calculated for the same species from the same waterbody, etc.

# Load data from FSAdata package, restrict to one location and year,
# create log10 values of weight and length
data(WalleyeErie2,package="FSAdata")
tmp <- WalleyeErie2 |>
  dplyr::filter(loc==2,year==2010) |>
  dplyr::mutate(logW=log10(w),
                logL=log10(tl))

# Generate LVB results
vb1 <- FSA::makeGrowthFun(type="von Bertalanffy")
fit1 <- nls(tl~vb1(age,Linf,K,t0),data=tmp,
            start=FSA::findGrowthStarts(tl~age,data=tmp))

# Generate length-weight regression results
fit2 <- lm(logW~logL,data=tmp)

# Make life-history list with those results
waeLH <- makeLH(N0=100,tmax=15,Linf=fit1,LWalpha=fit2)
waeLH

Description

Convert vectors of conditional fishing (cf) and natural (cm) mortality rates to instantaneous total (Z), fishing (F), and natural (M) mortality rates, total annual mortality rate (A), the annual exploitation rate (u), and the expectation of natural death (v). The primary purpose of this function is to provide a data.frame from which the user can explore the relationships between these rates and understand how choices of cf and cm effect the other rates, especially A and u.

Usage

seeMorts(cf, cm, type = 2, verbose = TRUE)

## S3 method for class 'SEEMORTS'
summary(object, verbose = TRUE, ...)

Arguments

Details

Numeric values in the cf and cm vectors can be entered as a single value (e.g., cf=0.3), a sequence of values created with seq (e.g., cf=seq(0.1,0.5,0.05), or as unique values with c (e.g., cf=c(0.1,0.4,0.5) depending on the user's needs. Values of cf and cm will be repeated as necessary (via expand.grid) to form all combinations of the two sets of given values. Thus, neither cf and cm should contain repeated values.

Equations for computing the other mortality rates (F, M, Z, A, u, and v) from cf and cm are in Ricker (1975). Note that n and m in Ricker (1975) are cf and cm here.

The formulae for u and v differ depending on whether a Type-1 or a Type-2 fishery is being considered (see type). A Type-1 fishery is where fishing mortality occurs in a very narrow part of the annual period such that it is reasonable to assume that fishing and natural mortality do not both occur (or overlap) in that portion (e.g., a fishery where the open harvest season is only a few days). A Type-2 fishery is where natural and fishing mortality substantially overlap throughout the annual period (e.g., a fishery where the open harvest season is much of the annual period).

Value

The main function returns a data.frame with the following values:

• cm is the given conditional natural mortality rates.

• cf is the given conditional fishing mortality rates.

• M is the calculated instantaneous rate of natural mortality.

• F is the calculated instantaneous rate of fishing mortality.

• Z is the calculated instantaneous rate of total mortality.

• A is the calculated total annual rate of mortality.

• u is the calculated annual exploitation rate.

• v is the calculated expectation of natural death.

The summary function returns a data.frame with the following values for each of the mortality rates:

• type is the "type" of mortality rate (cm, cf, M, F, Z, A, u, or v).

• unique is the number of unique values.

• min is the minimum value (rounded to 3 decimal places).

• max is the maximum value (rounded to 3 decimal places).

References

Ricker, W.E. 1975. Computation and interpretation of biological statistics of fish populations. Technical Report Bulletin 191, Bulletin of the Fisheries Research Board of Canada. Was (is?) from https://waves-vagues.dfo-mpo.gc.ca/library-bibliotheque/1485.pdf.

See Also

yprBH_MinLL, yprBH_SlotLL, and dpmBH_MinLL for functions that require the user to provide reasonable values of cf and cm.

Examples

# == Simple examples ========================================================
seeMorts(cf=0.3,cm=0.2)
seeMorts(cf=0.3,cm=0.2,type=1)

# == More realistic example =================================================
test <- seeMorts(cf=seq(0,0.5,0.05),cm=c(0.2,0.3,0.4,0.5))
head(test)
tail(test)
summary(test)

#-- Optional plotting examples ----------------------------------------------
if (require(ggplot2)) {
  ggplot(data=test,mapping=aes(x=cf,y=u,color=as.factor(cm))) +
    geom_line(linewidth=1) +
    theme_bw()

  ggplot(data=test,mapping=aes(x=Z,y=A)) +
    geom_line(linewidth=1) +
    theme_bw()

  ggplot(data=test,mapping=aes(x=cf,y=cm,z=A)) +
    geom_contour_filled(bins=9) +
    scale_fill_discrete(name="A",palette="OrRd") +
    theme_bw()
}

Description

Simulate yield under minimum length regulations using the Beverton-Holt Yield-per-Recruit (YPR) model with (possibly) multiple values for minimum length limits for harvest (minLL), conditional fishing mortality (cf), and conditional natural mortality (cm).

Usage

yprBH_MinLL(minLL, cf, cm, lhparms, loi = NULL, matchRicker = FALSE)

Arguments

Details

Details will be filled out later.

Note that the main calculations are in the internal yprBH_func (use rFAMS:::yprBH_func to see that source code).

Value

A data.frame with the following calculated values:

• yield is the estimated yield (in g).

• exploitation is the exploitation rate.

• Nharvest is the number of harvested fish.

• Ndie is the number of fish that die of natural deaths.

• Nt is the number of fish at time tr (time they become harvestable size).

• avgwt is the average weight of fish harvested.

• avglen is the average length of fish harvested.

• tr is the time for a fish to recruit to a minimum length limit (i.e., time to enter fishery).

• nAtxxx is the number that reach the length of interest supplied. There will be one column for each length of interest.

• F is the instantaneous rate of fishing mortality.

• M is the instantaneous rate of natural mortality.

• Z is the instantaneous rate of total mortality.

• S is the (total) annual rate of survival.

For convenience the data.frame also contains the model input values (minLL, cf, andcm from input vectors; N0; Linf; K; t0; LWalpha; LWbeta; and tmax from lhparms).

The data.frame also contains a notes value which may contain abbreviations for "issues" that occurred when computing the results and were adjusted for. The possible abbreviates are as follows:

• minLL>=Linf: The minimum length limit (minLL) being explored was greater than the given asymptotic mean length (Linf). For the purpose (only) of computing the time at recruitment to the fishery (tr) the Linf was set to minLL+0.1.

• tr<t0: The age at recruitment to the fishery (tr) was less than the hypothetical time when the mean length is zero (t0). The fish can't recruit to the fishery prior to having length 0 so tr was set to t0. This also assures that the time it takes to recruit to the fishery is greater than 0.

• Nt<0: The number of fish recruiting to the fishery was less than 0. There cannot be negative fish, so Nt was then set to 0.

• Nt>N0: The number of fish recruiting to the fishery was more than the number of fish recruited to the populations. Fish cannot be added to the cohort, so Nt was set to N0.

• Y=Infinite: The calculated yield (Y) was inifinity, which is impossible and suggests some other problem. Yield was set to NA.

• Y<0: The calculated yield (Y) was negative, which is impossible. Yield was set to 0.

• Nharv<0: The calculated number of fish harvested (Nharv) was negative, which is not possible. Number harvested was set to 0.

• Nharv>Nt: The calculated number of fish harvested (Nharv) was greater than the number of fish recruiting to the fishery, which is impossible. The number harvested was set to the number recruiting to the fishery.

• Ndie<0: The calculated number of fish recruiting to the fishery that died naturally (Ndie) was negative, which is not possible. Number that died was set to 0.

• Ndie>Nt: The calculated number of fish recruiting to the fishery that died naturally (Ndie) was greater than the number of fish recruiting to the fishery, which is impossible. The number that died was set to the number recruiting to the fishery.

• agvglen<minLL: The average length of harvested fish was less than the given minimum length limit being explored, which is not possible (with only legal harvest). The average length was set to the minimum length limit.

Author(s)

Jason C. Doll, [email protected]

See Also

yprBH_SlotLL for estimating yield with the yield-per-recruit model and a slot limit, or dpmBH_MinLL for estimating yield with a dynamic pool model using a minimum length limit.

See this demonstration page for more examples of this function.

Examples

# Load other required packages for organizing output and plotting
library(dplyr)    ## for filter
library(ggplot2)  ## for ggplot et al.
library(metR)     ## geom_contour2

# Life history parameters to be used below
LH <- makeLH(N0=100,tmax=15,Linf=592,K=0.20,t0=-0.3,LWalpha=-5.528,LWbeta=3.273)

# Estimate yield for multiple values of minLL, cf, and cm
# # This is a minimal example, increments for minLL, cf, and cm would likely be smaller
# #   to produce finer-scaled results.

minLL <- seq(from = 200, to = 550, by = 50)
cf <- seq(from = 0.1, to = 0.9, by = 0.1)
cm <- seq(from = 0.1, to = 0.9, by = 0.1)
loi <- c(400,450,500,550)

Res_1 <- yprBH_MinLL(minLL = minLL, cf = cf, cm = cm,
                     lhparms=LH, loi=loi)

# Yield curves (yield vs exploitation) by varying minimum lengths,
# using cm=40
plot_dat <- Res_1 |> filter(cm==0.40)

ggplot(data=plot_dat,mapping=aes(y=yield,x=exploitation,
                                 group=minLL,color=minLL)) +
  geom_line(linewidth=1) +
  scale_color_gradient2(high="black") +
  xlab("Exploitation (u)")+
  ylab("Yield (g)")+
  labs(color="Min Length Limit") +
  theme_bw()

# Yield isopleths for varying minLL and exploitation with cm=0.40
# # Using same data as previous example
ggplot(data=plot_dat,mapping=aes(x=exploitation,y=minLL,z=yield)) +
  geom_contour2(aes(label = after_stat(level))) +
  xlab("Exploitation (u)") +
  ylab("Minimum length limit (mm)") +
  theme_bw()

Description

Simulate yield below slot length regulations using the Beverton-Holt Yield-per-Recruit (YPR) model with (possibly) multiple values for conditional natural mortality (cm) and chosen values for the lower and upper lengths of the slot (i.e,. lowerSL and upperSL); conditional fishing mortality below (cfBelow), in (cfIn), and above (cfAbove) the slot; and length when fish recruit to the fishery (recruitmentTL).

Usage

yprBH_SlotLL(
  lowerSL,
  upperSL,
  cfBelow,
  cfIn,
  cfAbove,
  cm,
  lhparms,
  recruitmentTL = NULL,
  loi = NULL,
  matchRicker = FALSE,
  label = NULL
)

Arguments

Details

Details will be filled out later.

Note that the main calculations are in the internal yprBH_slot_func (use rFAMS:::yprBH_slot_func to see that source code).

Value

A data.frame with the following calculated values:

• yieldTotal is the calculated total yield

• yieldBelow is the calculated yield below the slot limit

• yieldIn is the calculated yield within the slot limit

• yieldAbove is the calculated yield above the slot limit

• nharvTotal is the calculated total number of harvested fish

• ndieTotal is the calculated total number of fish that die of natural death

• nharvestBelow is the number of harvested fish below the slot limit

• nharvestIn is the number of harvested fish within the slot limit

• nharvestAbove is the number of harvested fish above the slot limit

• n0die is the number of fish that die of natural death before entering the fishery at a minimum length

• ndieBelow is the number of fish that die of natural death between entering the fishery and the lower slot limit

• ndieIn is the number of fish that die of natural deaths within the slot limit

• ndieAbove is the number of fish that die of natural deaths above the slot limit

• nrBelow is the number of fish at time trBelow (time they become harvestable size below the slot limit)

• nrIn is the number of fish at time trIn (time they reach the lower slot limit size)

• nrAbove is the number of fish at time trAbove (time they reach the upper slot limit size)

• trBelow is the time for a fish to recruit to a minimum length limit (i.e., time to enter fishery)

• trIn is the time for a fish to recruit to a lower length limit of the slot limit

• trOver is the time for a fish to recruit to a upper length limit of the slot limit

• avglenBelow is the average length of fish harvested below the slot limit

• avglenIn is the average length of fish harvested within the slot limit

• avglenAbove is the average length of fish harvested above the slot limit

• avgwtBelow is the average weight of fish harvested below the slot limit

• avgwtIn is the average weight of fish harvested within the slot limit

• avgwtAbove is the average weight of fish harvested above the slot limit

• nAtxxx is the number that reach the length of interest supplied. There will be one column for each length of interest.

• cm A numeric representing conditional natural mortality

• expBelow is the exploitation rate below the slot limit

• expIn is the exploitation rate within the slot limit

• expAbove is the exploitation rate above the slot limit

• FBelow is the estimated instantaneous rate of fishing mortality below the slot limit

• FIn is the estimated instantaneous rate of fishing mortality within the slot limit

• FAbove is the estimated instantaneous rate of fishing mortality above the slot limit

• MBelow is the estimated instantaneous rate of natural mortality below the slot limit

• MIn is the estimated instantaneous rate of natural mortality within the slot limit

• MAbove is the estimated instantaneous rate of natural mortality above the slot limit

• ZBelow is the estimated instantaneous rate of total mortality below the slot limit

• ZIn is the estimated instantaneous rate of total mortality within the slot limit

• ZAbove is the estimated instantaneous rate of total mortality above the slot limit

• SBelow is the estimated total survival below the slot limit

• SIn is the estimated total survival within the slot limit

• SAbove is the estimated total survival above the slot limit

For convenience the data.frame also contains the model input values (lowerSL, upperSL, cfBelow, cfIn, cfOver, cm from input vectors; N0; Linf; K; t0; LWalpha; LWbeta; and tmax from lhparms) and, optionally, the string provided in label.

Author(s)

Jason C. Doll, [email protected]

See Also

yprBH_MinLL for estimating yield with the yield-per-recruit model using a minimum length limits, or dpmBH_MinLL for estimating yield with a dynamic pool model using a minimum length limit.

See this demonstration page for more examples of this function.

Examples

#Load other required packages for organizing output and plotting
library(ggplot2)  #for plotting
library(dplyr)    #for select
library(tidyr)    #for pivot_longer

# Life history parameters to be used below
LH <- makeLH(N0=100,tmax=15,Linf=592,K=0.20,t0=-0.3,LWalpha=-5.528,LWbeta=3.273)
# conditional natural mortality vector
cm <- seq(from = 0.1, to = 0.9, by = 0.1)

# Estimate yield based on a protected slot limit
Res_1 <- yprBH_SlotLL(lowerSL=250,upperSL=325,
                      cfBelow=0.25,cfIn=0.0,cfAbove=0.15,cm=cm,
                      lhparms=LH,recruitmentTL=200,
                      loi=c(200,250,300,325,350),label="250-325")

Res_1

# Plot results
# Total Yield vs Conditional Natural Mortality (cm)
ggplot(data=Res_1,mapping=aes(x=cm,y=yieldTotal)) +
  geom_point() +
  geom_line() +
  labs(y="Total Yield (g)",x="Conditional Natural Mortality (cm)") +
  theme_bw()


# Yield below, in, and above the slot limit vs Conditional Natural Mortality (cm)
# Select columns for plotting
plot_data <- Res_1 |>
  select(cm, yieldBelow, yieldIn, yieldAbove) |>
  pivot_longer(!cm, names_to="YieldCat",values_to="Yield")

# Generate plot
ggplot(data=plot_data,mapping=aes(x=cm,y=Yield,group=YieldCat,color=YieldCat)) +
  geom_point() +
  scale_color_discrete(name="Yield",labels=c("Above SL","In SL","Below SL"))+
  geom_line() +
  labs(y="Total Yield (g)",x="Conditional Natural Mortality (cm)") +
  theme_bw() +
  theme(legend.position = "top")+
  guides(color=guide_legend(title="Yield"))