Sum.Pop.Mod: Information of the Exploited Population (Structured by Age)...

In IMPRESSPROJECT/ModelingPopulation: Modeling of an exploited population (structured by age)

Description

This function allows us to extract additional information obtained in the simulation process of Population.Modeling (main function). The specified information that can be extracted is explained above.

Usage

Sum.Pop.Mod(Pop.Mod, Elements)

Arguments

Pop.Mod

A list containing the components returned by Population.Modeling function (main function).

Elements

A vector specifing which of the following elements must be reported by the function. "Z": Third dimensional array containing the instantaneous mortality for each age, year and iteration. "LS":Third dimensional arraycontaining the (stock) length for each age, year and iteration (at ts). "LC":Third dimensional array containing the length of the captures for each age, year and iteration (at tc). "WS":Third dimensional array containing the population weight for each age, year and iteration. "WSSB":Third dimensional array containing the weight of the mature population for each age, year and iteration. "C":Weight of the captures for each year and iteration. "SEL":Selectivity by age, for each iteration. "BIO":Total biomass for each year and iteration. "SSB":Maturity biomass for each year (spawning stock) and iteration. "REC":Population numbers at first age. "F":Mean fishing mortality (only takes the values between the minFage and maxFage. "WC":Third dimensional array containing the weight of captures for each age (at tc), year and iteration.

Value

A list containing the the objects specified before using argument "Elements".

Author(s)

• Marta Cousido-Rocha

• Santiago Cerviño López

Examples

# First we introduce the basic parameters to define the population.
# Note that N0 is equal to 10000 individuals, and hence below we are
# consistent with this unit when we introduce the biological and
# stock-recruitment parameters.
ctrPop<-list(years=seq(1980,2020,by=1),niter=2,N0=10000,ages=0:15,minFage=4,
maxFage=7,ts=0,tc=0.5,tseed=NULL)

# Now, we introduce the biological parameters of the population.
# Note that L_inf is in cm, and a and b parameters allow us to relate
# the length in cm with the weight in Kg.
number_ages<-length(ctrPop$ages);number_years<-length(ctrPop$years)
M<-matrix(rep(0.4,number_ages*number_years),ncol = number_years)
colnames(M)<-ctrPop$years
rownames(M)<-ctrPop$ages
ctrBio<-list(M=M,CV_M=0.2, L_inf=124.5, t0=0, k=0.164, CV_L=0.2, CV_LC=0.2, a=4.5*10^(-6), b=3.1049,
           a50_Mat=3, ad_Mat=-0.5,CV_Mat=0.2)

# We continue introducing the fishing parameters.
# Below, we have different objects ctrSEL depending on which selectivity function is used.
# Constant selectivity
ctrSEL<-list(type="cte", par=list(cte=0.5),CV_SEL=0.2)

# Logistic selectivity
ctrSEL<-list(type="Logistic", par=list(a50_Sel=1.5, ad_Sel=-1),CV_SEL=0.2)

# Gamma selectivity
ctrSEL<-list(type="Gamma", par=list(gamma=10,alpha=15, beta=0.03),CV_SEL=0.05)

# Andersen selectivity
ctrSEL<-list(type="Andersen", par=list(p1=2,p3=0.2,p4=0.2,p5=40),CV_SEL=0.05)

f=rep(0.5,number_years)
ctrFish<-list(f=f,ctrSEL=ctrSEL)

# Finally, we show below the three possible stock recruitment relationship.
# The values of the parameters of Beverton-Holt Recruitment Model and Ricker
# Recruitment Model are ones suitables when the biomass is measured in Kg and
# the recruitment is measured as number of individuals.

a_BH=10000; b_BH=400; CV_REC_BH=0.2; a_RK=10; b_RK=0.0002; CV_REC_RK=0.2
# If the spawning stock recruiment relationship is constant:
SR<-list(type="cte",par=NULL)
# If the spawning stock recruitment relationship is Beverton-Holt Recruitment Model:
SR<-list(type="BH",par=c(a_BH,b_BH,CV_REC_BH))
# If the spawning stock recruitment relationship is Ricker Recruitment Model:
SR<-list(type="RK",par=c(a_RK,b_RK,CV_REC_RK))

# The following lines allow us to use the described function.
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)
# We extract the F and SSB.
Sum.Pop.Mod(Pop.Mod,c("F","SSB"))

IMPRESSPROJECT/ModelingPopulation documentation built on March 21, 2020, 12:14 a.m.