Sampling_Biomass: Sampling Biomass at each year

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

Description

Returns a determined number of biomass samples.

Usage

Sampling_Biomass(Pop.Mod, CV, niter_sampling)

Arguments

Pop.Mod	A list containing the components returned by Population.Modeling function (main function).
CV	The biomass coefficient of variation. Default value 0, which means that the function returns the biomass computed in the main function of the package, NOT a sample.
niter_sampling	The number of samples to be computed if the Pop.Mod object refers to a deterministic framework. If such object is stochastic (niter>1) for each iteration one sample is computed and hence a value of this parameter is not required.

Details

A log-normal distribution is used to compute the biomass samples. More precisely, for each year and iteration the value of the biomass in the sample comes from a log-normal distribution centered in the corresponding value of biomass and variability determined by the CV.

Value

An array containing the samples of the total biomass for each year. The number of samples is equal to niter_sampling in the deterministic framework and to niter in the stochastic one.

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)
# Now,we can extract the biomass.
# Deterministic biomass:
B<-Sampling_Biomass(Pop.Mod,CV=0)
# Stochastic biomass:
B<-Sampling_Biomass(Pop.Mod,CV=0.2)

# If niter=1, then the following lines allow us to obtain the biomass samples.

# B<-Sampling_Biomass(Pop.Mod,CV=0)
# B<-Sampling_Biomass(Pop.Mod,CV=0.2,niter_sampling=1000)

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