Simulate expected yield under minimum length regulations using the Dynamic Pool model for a range of input parameters

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

minLL: A single numeric representing the minimum length limit for harvest in mm.
cf: A matrix of conditional fishing mortality where each row represents a year and each column represents an age (age-0 through maximum age; i.e., tmax in lhparms). All values must be between 0 and 1 (inclusive).
cm: A matrix of conditional natural mortality where each row represents a year and each column represents an age (age-0 through maximum age; i.e., tmax in lhparms). All values must be between 0 and 1 (inclusive).
rec: A numeric vector with length simyears that specifies the number of recruits each year. This vector is best generated using the genRecruits. All values must be greater than 0.
lhparms: A named vector or list that contains values for each N0, tmax, Linf, K, t0, LWalpha, and LWbeta. See makeLH for definitions of these life history parameters. Also see details.
simyears: A single numeric for the number of years to simulate. Value must be a whole number greater than 1.
species: A single character to specify the species used in the simulation. This will define the length for stock, quality, preferred, memorable, and trophy lengths from the FSA package. See the PSDlit documentation.
group: A single character to specify the sub-group name for species which may be required when defining the stock, quality, preferred, memorable, and trophy length categories from the FSA package. See the PSDlit documentation.
matchRicker: A logical that indicates whether the yield function should match that in Ricker (1975). Defaults to FALSE. See the FAMS vs Ricker article.

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.

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).

Author

Jason C. Doll, jason.doll@fmarion.edu

Examples

#load required library
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
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()