# Main function to simulate expected yield using the Dynamic Pool model for a range of input parameters, including minimum length limits for harvest Estimate yield using the Beverton-Holt Yield-per-Recruit (YPR) model using ranges of values for conditional fishing mortality (`cf`), conditional natural mortality (`cm`), and minimum length limits for harvest (`minLL`). ```r dpmBH( simyears, minLL, cf, cm, rec, lhparms, matchRicker = FALSE, species = NULL, group = NULL ) ``` ## Arguments - `simyears`: A single numeric for the lower limit of minimum length limit for harvest in mm. - `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 age. Ages are age-0 through maximum age. - `cm`: A matrix of conditional natural mortality where each row represents a year and each column represents age. Ages are age-0 through maximum age. - `rec`: A single numeric representing initial recruitment abundance. - `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. - `matchRicker`: A logical that indicates whether the yield function should match that in Ricker (). Defaults to `TRUE`. The only reason to changed to `FALSE` is to try to match output from FAMS. See the "YPR_FAMSvRICKER" article. - `species`: is a single character to specify the species used in the simulation and will define the length for `stock`, `quality`, `preferred`, `memorable`, and `trophy`. Length categories are obtained from the FSA package, see the `PSDlit` documentation. - `group`: is a single character to specify the sub-group name of a species used in the simulation and will define the length for `stock`, `quality`, `preferred`, `memorable`, and `trophy`. Length categories are obtained from the FSA package, see the `PSDlit` documentation. ## Returns A list with two data.frame object. The first list item contains a data.frame with the following calculated values in a summary by age: * `year` is the year number for the simulation * `yc`is 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. * `explitation` 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 * `N0` is the number of initial For convenience the data.frame also contains the model input values (`minLL`, `N0`, `N0`, `Linf`, `K`, `t0`, `LWalpha`, `LWbeta`, and `tmax`). 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\ 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_FAMS() #Plot Number harvested vs age ggplot(data=plotdat,mapping=aes(x=age,y=N_harvest)) + geom_point() + geom_line() + labs(y="Number harvested",x="Age") + theme_FAMS() #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(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 PSD vs year ggplot(data=out_2[[2]],mapping=aes(x=year,y=PSD)) + geom_point() + geom_line() + labs(y="PSD",x="Year") + theme_FAMS() #Use summary by year data frame to plot yield vs year ggplot(data=out_2[[2]],mapping=aes(x=year,y=Yield_age_1plus)) + geom_point() + geom_line() + labs(y="Total yield (g)",x="Year") + theme_FAMS() #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_FAMS() #Plot Number harvested vs age ggplot(data=out_2[[1]],mapping=aes(x=age,y=N_harvest,group=yc,color=yc)) + geom_point() + geom_line() + labs(y="Number harvested",x="Age") + theme_FAMS() ``` ## See Also `yprBH_func` for estimating yield from single values of `cf`, `cm`, and `minLL`, and `yprBH_minLL_fixed` for simulating yield with multiple values of `cf` and `cm` but a fixed value for `minLL`. ## Author(s) Jason C. Doll, jason.doll@fmarion.edu

dpmBH function