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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="author" content="Marta Cousido Rocha, Santiago Cerviño López, Maria Grazia Pennino" />
<title>Rfishpop (Third tutorial)</title>
<script>/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
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) 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<h1 class="title toc-ignore">Rfishpop (Third tutorial)</h1>
<h4 class="author">Marta Cousido Rocha, Santiago Cerviño López, Maria Grazia Pennino</h4>
<h4 class="date">29/7/2020</h4>
</div>
<div id="population-dynamic-tools-in-support-of-fisheries-managment" class="section level2">
<h2>Population dynamic tools in support of fisheries managment</h2>
<p>In the first tutorial, we have explained how to simulate the real dynamics of the fishery system using the <strong>generic operating model (OM)</strong> implemented in Population.Modeling function. Keeping in mind that the aim of the package is to implement a full <strong>Management Strategy Evaluation (MSE)</strong> cycle the next step is to <strong>sampling</strong> data from the operating model to mimic collection of fishery dependent data and research surveys.</p>
<p>Rfishpop package contains relevant functions at this point:</p>
<ul>
<li><p><span style="color:blue">Sampling_Survey</span></p></li>
<li><p><span style="color:blue">Sampling_Catch</span></p></li>
</ul>
<p>Through this tutorial explanations of the performance and use of both functions are provided.</p>
<div id="sampling_survey" class="section level3">
<h3><span style="color:blue">Sampling_Survey</span></h3>
<p>This function returns the indices of abundance for each year, age and iteration, the indices of biomass for each year and iteration, and a sample of the stock length for each year and iteration derived from the corresponding length distribution.</p>
<p>The <strong>arguments</strong> of this function are described above:</p>
<ul>
<li><p><em>Pop.Mod</em> is a list containing the components returned by Population.Modeling function (main function).</p></li>
<li><p><em>type</em> is an indicator of which index/sample must be computed: index of biomass (type=“biomass”), index of abundance (type=“abundance”), or stock length sample (type=“LengthS”).</p></li>
<li><p><em>q_A</em> is the matrix of annual and age specific catchability coefficients.</p></li>
<li><p><em>gamma</em> is the density dependent parameter.</p></li>
<li><p><em>CV_A</em> is the coefficient of variation associated to the abundance index. Default value 0.</p></li>
<li><p><em>par</em> is a vector containing the required parameters if type=“LengthS”.</p>
<ul>
<li><p><em>CV</em> is the coefficient of variation associated to the log-normal distribution used to compute the stock length distribution from which the length sample is obtained (below a deep explanation is given).</p></li>
<li><p><em>RF.value</em> is the number of values generated for each age (given a year and an iteration) from the log-normal distribution used to obtain the stock length distribution from which the length sample is obtained (below a deep explanation is given). By default RF.value=1000.</p></li>
<li><p><em>sample.size</em> is the size of the desired sample.</p></li>
</ul></li>
<li><p><em>tsampling</em> is the time of the year at which the sampling is carried out. This parameter takes a value between 0 and 1, since the year is considered as a unit. By default tsampling=0.</p></li>
</ul>
<p>The <strong>result</strong> of the function is: an array containing the indices of abundance for each year, age, and iteration. If type=“biomass” an array containing the indices of biomass for each year and iteration is also reported. If type=“LengthS” an array containing in each column (year) the corresponding length sample for each iteration (third dimension) is also provided.</p>
<p>The important point is: <strong>How the indices and samples are obtained?</strong> Below the corresponding details are provided.</p>
<p>Let’s start explaining the abundance index.</p>
<p><strong>ABUNDANCE INDEX</strong></p>
<p>The abundance index for year t and age i is</p>
<p><span class="math display">\[{IA}_{it}={q_A}_{it}*N_{it}^{\gamma},\]</span></p>
<p>where <span class="math inline">\({q_A}_{it}\)</span> is the catchability coefficient, <span class="math inline">\(\gamma=gamma\)</span> is the density dependent parameter, and <span class="math inline">\(N_{it}\)</span> is the abundance for year t and age i (stock numbers matrix) when <span class="math inline">\({CV}_A=0\)</span>. If <span class="math inline">\({CV}_A\)</span> is different than 0 the abundance index for year t and age i is generated from a log normal distribution center in <span class="math inline">\({IA}_{it}\)</span> (defined above) and variability determined from the corresponding coefficient of variation <span class="math inline">\({CV}_A\)</span>. Note that the <strong>matrix N (stock numbers)</strong> in this function is computed <strong>at the time instant tsampling</strong>, i.e., it is not the matrix N computed in the main function Population.Modeling function, instead we update such matrix to time instant tsampling.</p>
<p><strong>BIOMASS INDEX</strong></p>
<p>Once the abundance index (IA) is computed, we can continue computing the biomass index.</p>
<p>Firstly, we compute the W (third dimensional array containing the weight corresponding to the stock length for each age, year and iteration) at time tsampling. Then, we define WS (third dimensional array containing the weight for each age, year and iteration) multiplying the abundance index IA by the weight W. Finally, the index of biomass for each year is the sum by columns of matrix WS.</p>
<p><strong>SAMPLE LENGTH</strong></p>
<p>The procedure to obtain a stock sample length needs a detailed explanation.</p>
<p>A stock length sample is obtained generating random values from the computed <strong>stock length distribution</strong>.</p>
<p>Hence, the question is: <strong>How the stock distribution length is computed?</strong></p>
<p>The stock distribution length from which the sampling procedure is carried out is computed as follows. For each age, year and iteration <em>RF.value</em> random values are generating from a log-normal distribution centered in the corresponding stock length and whose variability comes from the given CV. The distribution obtained for each age (given a year and an iteration) is scaled using the corresponding abundance index matrix (IA).</p>
<p>Remember that the matrix LS (stock length) is computed at the time instant tsampling.</p>
<p>After the above description of the function, the next step is to use it in an example to fix some ideas. Hence, we need to use the main function (Population.Modeling) to simulate a population.</p>
<p>Taking into account that in previous tutorials we have explained the functions <strong>BYR.eq</strong> (Total Yield, Biomass and Recruitment in Equilibrium) and <strong>RF</strong> (Reference Points) we have used such functions to define a population corresponding to a short life species at certain level of explotation.</p>
<p>First point is to create the population, as we explained in the first tutorial, using the values of the parameters for a short life species. The recruitment model is Beverton-Holt and the selectivity type is constant.</p>
<pre class="r"><code>library(Rfishpop)
ctrPop<-list(years=seq(1980,2020,by=1),niter=1,N0=15000,ages=0:15,minFage=2,
maxFage=5,tc=0.5,seed=NULL)
number_ages<-length(ctrPop$ages);number_years<-length(ctrPop$years)
Mvec=c(1,0.6,0.5,0.4,0.35,0.35,0.3,rep(0.3,9))
M<-matrix(rep(Mvec,number_years),ncol = number_years)
colnames(M)<-ctrPop$years
rownames(M)<-ctrPop$ages
ctrBio<-list(M=M,CV_M=0, L_inf=20, t0=-0.25, k=0.3, CV_L=0, CV_LC=0, a=6*10^(-6), b=3,
a50_Mat=1, ad_Mat=-0.5,CV_Mat=0)
ctrSEL<-list(type="cte", par=list(cte=0.5),CV_SEL=0)
f=matrix(rep(0.5,number_years),ncol=number_years,nrow=1,byrow=TRUE)
ctrFish<-list(f=f,ctrSEL=ctrSEL)
a_BH=15000; b_BH=50; CV_REC_BH=0
SR<-list(type="BH",par=c(a_BH,b_BH,CV_REC_BH))
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)</code></pre>
<p>Now, applying the <strong>BYR.eq</strong> function to the above Pop.Mod object we can obtain the <strong>population size in equilibrium for each age</strong>.</p>
<pre class="r"><code>RE<-BYR.eq(Pop.Mod,0,3,3,c(FALSE,1),Method="mean",par=NULL)
N_eq<-RE$N
N_eq</code></pre>
<pre><code>## , , 1
##
## [,1]
## 0 10574.55798
## 1 3890.16248
## 2 2134.96644
## 3 1294.92260
## 4 868.01258
## 5 611.67812
## 6 431.04229
## 7 319.32398
## 8 236.56102
## 9 175.24872
## 10 129.82744
## 11 96.17854
## 12 71.25081
## 13 52.78390
## 14 39.10327
## 15 111.76873</code></pre>
<p>Below, we use this vector <strong>N_eq</strong> as a value for the parameter <em>N0</em> in the main function for starting the population at the equilibrium stock numbers.</p>
<p>On the other hand, we can use the function <strong>RF</strong> to obtain the reference fishery mortality at which the MSY is attained. This point allows to control the level of explotation that we want to introduce in the population.</p>
<pre class="r"><code>rf=RF(Pop.Mod, 3,3,Method="mean",par=NULL,FM_type="F_msy",iters=1,plot=FALSE)
rf</code></pre>
<pre><code>## $F_msy
## , , 1
##
## f F YPR BPR R Y B
## [1,] 0.1894689 0.09473445 0.0006686766 0.006496303 7303.314 4.883555 47.44454
##
##
## $INFO
## $INFO$Method
## [1] "mean"
##
## $INFO$Fish.years
## [1] 3
##
## $INFO$Bio.years
## [1] 3</code></pre>
<pre class="r"><code>fmsy=rf$F_msy[,1,1];fmsy</code></pre>
<pre><code>## [1] 0.1894689</code></pre>
<p>Then, now we are going to exploit the population at two times the MSY level during the first 25 years, and then the population has the chance to recover being exploited at MSY level.</p>
<pre class="r"><code>ctrPop<-list(years=seq(1980,2020,by=1),niter=2,N0=N_eq,ages=0:15,minFage=2,
maxFage=5,tc=0.5,tseed=NULL)
f=matrix(c(rep(fmsy*2,25),rep(fmsy,number_years-25)),ncol=number_years,nrow=2,byrow=TRUE)
ctrFish<-list(f=f,ctrSEL=ctrSEL)
ctrBio<-list(M=M,CV_M=0.2, L_inf=20, t0=-0.25, k=0.3, CV_L=0, CV_LC=0, a=6*10^(-6), b=3,
a50_Mat=1, ad_Mat=-0.5,CV_Mat=0)
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)</code></pre>
<p>THE PROCEDURE TO CREATE THIS EXAMPLE IS ALSO A TUTORIAL OF HOW TO USE THE RF AND BYR.eq FUNCTIONS TO CREATE A POPULATION UNDER A CERTAIN SITUATION.</p>
</div>
<div id="practical-use-of-sampling_survey" class="section level3">
<h3>PRACTICAL USE OF Sampling_Survey</h3>
<div id="abundance" class="section level4">
<h4>ABUNDANCE</h4>
<p>The first checking is to verify, as an example, that the abundance index matches the N matrix when <span class="math inline">\(q_A=1\)</span>, <span class="math inline">\(\gamma=1\)</span>, <span class="math inline">\({CV}_A=0\)</span> and tsampling=0. Remember that in Population.Modeling function the stock is always simulated at time 0.</p>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1;CV_A<-0
IA=Sampling_Survey(Pop.Mod=Pop.Mod,type="abundance",q_A=q_A,gamma=gamma,CV_A=CV_A,tsampling=0)</code></pre>
<p>We check the correspondence between N and IA at last year and first iteration, as an example.</p>
<pre class="r"><code>cbind(Pop.Mod$Matrices$N[,41,1],IA[,41,1])</code></pre>
<pre><code>## [,1] [,2]
## 0 6373.862308 6373.862308
## 1 2099.719272 2099.719272
## 2 1030.111293 1030.111293
## 3 557.452366 557.452366
## 4 332.698929 332.698929
## 5 208.264151 208.264151
## 6 130.044401 130.044401
## 7 85.133873 85.133873
## 8 55.569094 55.569094
## 9 36.156412 36.156412
## 10 23.446032 23.446032
## 11 15.148753 15.148753
## 12 9.750857 9.750857
## 13 6.252871 6.252871
## 14 3.997420 3.997420
## 15 6.803958 6.803958</code></pre>
<p>Now, taking <span class="math inline">\({CV}_A=0.2\)</span> we can compare using the following code the abundance index for the first iteration and last year obtained above using <span class="math inline">\({CV}_A=0\)</span> (black color) with 100 indices computed using <span class="math inline">\({CV}_A=0.2\)</span> (red color).</p>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1;CV_A<-0.2
IA=list()
for (i in 1:100){
IA[[i]]=Sampling_Survey(Pop.Mod=Pop.Mod,type="abundance",q_A=q_A,gamma=gamma,CV_A=CV_A,tsampling=0)
}
plot(1:16,Pop.Mod$Matrices$N[,41,1],type="l",xlab = "Ages",ylab="Abundance index")
for (i in 1:100){
lines(1:16,IA[[i]][,41,1],col="red")
}
lines(1:16,Pop.Mod$Matrices$N[,41,1])</code></pre>
<p><img 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" width="672" /></p>
<p>We can also compare the abundance index using <span class="math inline">\({CV}_A=0\)</span> (black color) with the mean of the above 100 indices using <span class="math inline">\({CV}_A=0.2\)</span> (red color).</p>
<pre class="r"><code>a=matrix(0,ncol=100,nrow=16)
for (i in 1:100){
a[,i]=IA[[i]][,41,1]
}
amean=rowMeans(a)
plot(1:16,Pop.Mod$Matrices$N[,41,1],type="l",xlab = "Ages",ylab="Abundance index")
lines(1:16,amean,col="red")</code></pre>
<p><img 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" width="672" /></p>
<p>The above plot focus on the deterministic iteration (iter=1) but the plot can be done also for the second iteration (stochastic).</p>
<pre class="r"><code>plot(1:16,Pop.Mod$Matrices$N[,41,2],type="l",xlab = "Ages",ylab="Abundance index")
for (i in 1:100){
lines(1:16,IA[[i]][,41,2],col="red")
}
lines(1:16,Pop.Mod$Matrices$N[,41,2])</code></pre>
<p><img 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" width="672" /></p>
<p>All the abundance indices obtained above can be computed at other time of the year changing tsampling=0 by other value between 0 and 1, for example, at tsampling=0.4.</p>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1;CV_A<-0
IA=Sampling_Survey(Pop.Mod=Pop.Mod,type="abundance",q_A=q_A,gamma=gamma,CV_A=CV_A,tsampling=0.4)
#IA</code></pre>
<p>The same comment applies to <span class="math inline">\(q_A\)</span> and <span class="math inline">\(\gamma\)</span>, the user easily can define this arguments taking other values.</p>
</div>
<div id="biomass" class="section level4">
<h4>BIOMASS</h4>
<p>The first checking is to verify, as an example, that the biomass index matches the biomass of the stock when <span class="math inline">\(q_A=1\)</span>, <span class="math inline">\(\gamma=1\)</span>, <span class="math inline">\({CV}_A=0\)</span> and tsampling=0.</p>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1;CV_A<-0
IA=Sampling_Survey(Pop.Mod=Pop.Mod,type="biomass",q_A=q_A,gamma=gamma,CV_A=CV_A,tsampling=0)</code></pre>
<p>The above comparison is done for the first iteration, as you can see both biomass coincide.</p>
<pre class="r"><code>cbind(IA$biomass[,,1],Sum.Pop.Mod(Pop.Mod,c("BIO"))$BIO[,,1])</code></pre>
<pre><code>## [,1] [,2]
## 1980 124.27987 124.27987
## 1981 102.85092 102.85092
## 1982 85.65976 85.65976
## 1983 72.32550 72.32550
## 1984 62.11968 62.11968
## 1985 54.30359 54.30359
## 1986 48.25385 48.25385
## 1987 43.48176 43.48176
## 1988 39.65245 39.65245
## 1989 36.52494 36.52494
## 1990 33.92870 33.92870
## 1991 31.74265 31.74265
## 1992 29.87942 29.87942
## 1993 28.27474 28.27474
## 1994 26.88023 26.88023
## 1995 25.65882 25.65882
## 1996 24.58156 24.58156
## 1997 23.62551 23.62551
## 1998 22.77233 22.77233
## 1999 22.00715 22.00715
## 2000 21.31780 21.31780
## 2001 20.69423 20.69423
## 2002 20.12805 20.12805
## 2003 19.61223 19.61223
## 2004 19.14083 19.14083
## 2005 18.70878 18.70878
## 2006 20.12947 20.12947
## 2007 21.63525 21.63525
## 2008 23.18049 23.18049
## 2009 24.73916 24.73916
## 2010 26.29304 26.29304
## 2011 27.82802 27.82802
## 2012 29.33418 29.33418
## 2013 30.80096 30.80096
## 2014 32.21986 32.21986
## 2015 33.58390 33.58390
## 2016 34.88748 34.88748
## 2017 36.12635 36.12635
## 2018 37.29758 37.29758
## 2019 38.39946 38.39946
## 2020 39.43138 39.43138</code></pre>
<pre class="r"><code>bio0=IA$biomass</code></pre>
<p>Now, taking <span class="math inline">\({CV}_A=0.2\)</span> we can compare using the following code the biomass index for the first iteration obtained above using <span class="math inline">\({CV}_A=0\)</span> (black color) with 100 indices computed using <span class="math inline">\({CV}_A=0.2\)</span> (red color).</p>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1;CV_A<-0.2
IA=list()
for (i in 1:100){
IA[[i]]=Sampling_Survey(Pop.Mod=Pop.Mod,type="biomass",q_A=q_A,gamma=gamma,CV_A=CV_A,tsampling=0)
}
plot(1:41,bio0[,,1],type="l",xlab = "Years",ylab="Biomass index")
for (i in 1:100){
lines(1:41,IA[[i]]$biomass[,,1],col="red")
}
lines(1:41,bio0[,,1])</code></pre>
<p><img 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VhJAgrky9uJ9N8fb2P318M7v7l+RQNuAlqNV0FlO0EpKLBxngL66nAV0iGgxdlL1y+pz11AJ1dBCSiQBT8Bva4vRPrf5/uA7p4WxXfpnkjfK+jMNnxFQYGN8xLQ+vqjJ/uVz/r0pbqgv7h+TW2uAjr+dM7xNjwBBbbOS0Avi+JudQpodXv4JhCXAR0Yb8NTUGDjfAR0v9JZ7/c8BXS/OhpuG95ZQMcFJaBAdjxdylmn8xTQ03dhuAvoqKD9T0YioEAGCKgZoYlTAW2P0y98OQAxYxPejDqg4214AgpsnK+DSA/agF5v4iDS8eu5naAUFNg0LwG9LY7n0NcB3X+9hdOYTt/M7AQloMCmeQlofe7n2Ys6oLt/FJs4kb79biagFBTYMj9XItX3pG+lfCmn2Sro8NcBbIuna+F3T9t+Jn0zEQIKoOPtdnZfn9X3Yzq7/8716xlZNaAUFMgMN1Q2REABNAiooYmjSNKAUlBguwiooamAjgo6/gMAG7JuQHcfr2TepnslkllAKSiwaesGtHf6Uifha+HHAa0IKJArAmpocg2UbXggK5424Z/VZzA9v7r6X+fF2eMNbcLPrYLK/gTAVnj7TKT/8rn98oHrl9TnIqCTBgU9/cni4QYQI183E+luwHSZ9M1E5graBlT8bHgKCmyTx/uBniy+H+jupt4x8PbG5kmcj+5gE55teCAnHu9IL/3O1KdnwsEo8+tC1wgo2/BArtIK6NdHg+P5d14aDtwaAZWvglZdQCkosE2eNuGF3Z639jcEvT2vo3nv4uj7w73xzPanuh/dmW345pdcvyyACHg5iHRddCudXx9aH4avzyo9eyE88OG8MFybXT+gbMMD+fAS0Lp8dw7l2703bl7nevSnf5nWeJWAshMUyJSf80Bvj3ss7y26I31/T0D7xEb7AwIFlIICm+TpbkxfuqM/1neklx19Mj0i5SGgg4I2v+X6dQGE5+12dp8Od6T/dsEd6eMMKNvwQL4Suh9o/3z8o/Cb8OOjSLJVUAIKbFFCAa0vAh3Ust4telfx21I+1kBlq6AEFNiilAL65XxfUHEP6tenpoekQgSUj0YCtspXQD/+etH52fJE+uvDQfyL54c75L05nklvdk6pl4DKCkpAgQ3yE9Av525uqPy+/zz7mj4xHLgAAT3tJXX+wgBC8xLQQT8X3Exk91p8qrPHpquyKwRU40QmAgpsk69LOYsfnt+0/ljy/LtPV68vLi4eX72z2BHgJ6D9gp5+xf0LAwjM181EjI6VuyL7PCb3r6IVUFZBgQ3ydDs728s3l4kjoM2OUPevDCAs//cDdWX36a35rgBPAZUUlIAC2+NpE95ZQD9eXR3OBD3eWvns74Z/vkZA9VZBCSiwPb4OIrn5HLnjaUzffT7dWrkwvjfzugGtJgrKNjywPb7uB+pkFfT6VM27h1srH+9Jb3Z0ytcaaC+gFWeCAtvk7UT6s8c3S5+5Pp30zvM3+433fz1dgVQX1WjdNkhAex9xDGA7PB1EcnIi/elmIvVZUc2K52X4m4mcTlKaXQUloMDmJBTQ9o70t90tREw/ZJ6AAnDHS0B/utf3g1VA2z2pwi7VCG6oXGnuBKWgwOYkdDu7+AOq+nxjAgpsU0IBbe9IXx+Senl8LIpN+Gpcz1J6GGmN1wYQTkIBbY8YXXa3Ab2O4SDS6VL3iVXQdk0UwJakFND6w5F/vLl5VX8+crsuGsFpTMJRpIqAAhlZN6DHXZSuTmM6rHoeLj/6z304719dPTO+FGmdgEqPIsku51zlxQGEklRAd6+O91F+2V6TZPpUPgPKKiiwdSsH9Kf6lCVHpzHVPv1674fDbej/ebwY/r7hTZVXC2jV3jiZgALZSGkfaM/u468Xz43vZ0dAAbiTbEDtrBxQCgpkhYA60dSR4/BATgioE11AJ28KSkCBTSGgTgjb8JOroOu8OoAwCKgT4k7QyfvSA9gQAuqEIqDjC+LXeXkAQRBQJ7qP7OivcRJQYMsIqBvtBvpEQNmGBzaGgLqhtQpKQIFtIaBuEFAgQwTUDd2AUlBgQ/wG9H19G7rfXL+igXUDyiookBdPAf3wqL7v3HVzN7pgVguo8JkdrIICufAT0H059wH9cr7sdqAOrBxQSUG5nBPYLi8Brcu5r+Yho/Wnuz9w/Zra/AeUVVBgu7wE9PrwyRt1Ou8ePtnI6HPgnFozoFVTx5mAUlBgK3wE9PR5xKdPgDP9KHenVg2o5mEkCgpshY+AnpJ5fTx+tOGANgWdCygFBbbBY0AvDztCtxrQpqDHL2cCSkGBTfAX0NMu0HpL3uyjiF1aP6AaBeWmIsBGeNoHWvzS7AKtV0S3eBBJuJ3d/HF4Cgpsgq+j8Hfe/Y/DFvzuH8Wxo2GsHlBZQSUnMlFQYAu8BHS/DX/w4PhVuBVQPwEtNS7npKDABvi5Eul4DdJ3nw8B/THYHtBVA9pbBVUGtKKgwHZ4uhZ+9+Hi53f7//71L/ffuX5BE34COiyo9GIkCgokj9vZOdMEsVvRVK6CNr9GQ4GkEVBnxICWyoAOrTc8ANbG/UCdaWOoWdCqXRkFkCbuB+pMabIKOvgTACnifqDuCF0cRVR+Y3oCCiSN+4E6JGzEDwuqOhBPQYGEcT9QlwxXQQkokDbuB+rSxEa89HpOtuGBpHE/UKeaII6vRlIeiF93gACsiPuBumWwCsoVnUDquB+oYwaroMffI6BAsrgfqGPCRjwBBTaO+4G61pZx9kB883sAEsX9QJ2TfjQnq6DABnE/UPdUq6DSzzcmoEC6uB+oe926pcYqKAEF0sXt7FbQppFVUGDTCOgaWAUFshAkoDcbPg/0SG8VtDlb1M8wAXDN1z7QNxeNe99v+UqkE+2NeO6qDCTMT0BvT3cCLULfENRXQFUFZRUU2BJv9wMV3d/8Jrz0041ZBQU2xteVSMX9Nw+Ls//2+lFRnIW7EMljQBW7QQkosCG+roV/cLgI/pfm5sqheAyo7H4io4I2p9v7GyoA7ni6G1N9I9DrQ0aPdxYJxW9Ax5fDy1ZBSwoKJMrj/UBPn+Wx5Y/06BvXU1ZQtuGBZHkM6OlGoPvvtnw/UJHWKigBBZLlaR/o6Vb0p4BmcBrTkc4aKNvwQLK8HIW/POwD7TqadUDHq6AEFEiUr9OY6t2ex8PwtyEPw3sOaNWPJNvwwLZ4u6HyPpr7dH7z7j8fZnMQqaa5CsqN6YEUebqUs2g+Vq5Wb88H4j2gsoKyDQ9shKebiXx4dNj9+ejQzyeuX1JfFAGVXBFPQIEUeb6d3YeLi8d/uH5FA3EGlG14IE3cUHltequgBBRIEAFdm7iZPrENT0GB9BDQtcnWQMe3FCGgQIK8fSrnrxedn7M5D7TSO5mebXggSX4C+j67O9IL9FZBKwoKJMdLQG/7N6QnoGzDA5vg6Vr4orj/9qYV7kSmSAParoUCSImn29nVt1KOQcCAVjMFZRseSI7H+4HGIOQa6OSZTAQUSJDH+4HGIFRANT5ejoACyfF4P9AYhAio8OFIU3e1o6BAcnzdzi7cHex6QgVUthnPKiiQOj/ngX45L34M91nGgtABZRse2BJPVyJ9fVQU395r/LBoj+ju5uPV1dXbG5sixxTQ8U2ZKCiQFm9roI5OpP/0THie+++MBy7igA4EGFAAZrwEdNBP+4B+fTS4punOS8OBCxdQjU84JqBAWnxdiXT2+Gb5lUi3hxDfO92S5PvD7e1/MRu4IPeKMlkF7f4mxJACMOHrSiSzzsnVz3P2Qnjgw7np2myogHbXI1UzBRX/BkDcUroS6XqUS+OLRMMFtJKtiE6sghJQIH4JXYlUf6jncEXW9EPmgwa0GjW0YBUUSJmXfaDXTjbhZSuypiu3oQM6LiiroEC6vAR0v+5otJ4ol3JAJ9ZBxXSKBSWgQPT8nAe6L+gd43M2JU8yuqQ+jU34qtfF2VXQtqZBhhWANi8HkX66d+9w1ubCK5EuR7Wsd4saXWUfKKBiQrULSkCB2Pk6jcnFifT1+fjf/SY88HXfT7P7PAULaJfQUUClBe1t9gOIU0oBrQ9G7Yt58fyq9uZ4Jr3Zre4DBrRN6OwqqHDSKICYpfW58INP99zX9InZEwQNaJPQiYJWFBRISFoBrXavxYSePTY9th84oNKrOhUb8QQUiF5iAd3bfbp6fXFx8fjqncWZUcEDqn09EgEF4ucvoH/WOy7fev1E40LC5+ur6BSUVVAgfr4C+v57y72WiwYl0oAanAwKIGKeAvpKSJibD/f486PNNnycAR1/ugcBBVLgJ6D1+Udn96+u3jyrjwEt+IC5Tz8dToFqjiXdeTH7F4OBizWginXQ0AMKYIq3O9Kf1jt3r0zPfRfsnhWHc0jrC5DsVmcjDah6Iz70kAKY4OuO9Hfl3xg5dHMf0MN/zy4uLsxXZyMJqH5BQw8ogAme7gcqrHTuV0ctb810u+/lf/l8/O/hAqTdPxK6lLNHElB5QUMPKIAJ/u9Ib39/+stTN4V1WNPV2WgDyiookKCEArr/w8PqZvPfmunqbLwBVRU09JACUEtoE74pr1jgNG6oLDEdUOGTkEMPKAC1hA4iNbEUP2JpQwFVrIKGHlAAal4CWh/1aS5Ael/YfkBS+6Fyl90KbSJ3pB+TBpSCAmnxcyL9ZX3S+/Obm5s3jxacSN98rPGX8+Yp6qYm8LHGMlqroGzDA1Hz9plIHevPl6tvzHynviP9dXMak/FZ+VEHVL4RH3pAASh5uhZ+114Mf7bgUvjb+sz5H/Zrsv/Yl/Tx8brQhO5I36MdUAoKRMvf7ew+/npxcfHz20XP/WV4R3rDfkYVUN2TQUMPKQCVxG6o3L8jfbI3E6nJV0Fl91YGEKnEArr355uLnw6fjfzz82RvZ1eTBVRjFZSgAvFIL6CLRBXQwzF2jYL2/4qCAtFYN6DH89ydfayxg4GLLaCGG/Fs0wMxIaDBSLfhp1dBy+NHHgccaACClQP6070ffq//t+8HAlqpAjpV0ONXBBSIBftAg5Fvw0/clakpKQUFIkFAwzFYBe2tiRJQIBIENBxlQMcF7R89oqBAHHwG9NOvFxf/9TfXr2ckuoDq3py+uUPo6Q/DDjiAo/UD+uf/uTic8d7cUOROyIRGF9CykiRUcU18l1AKCkRh9YAe7iJy9nfhXKYzu9uBOhFVQCshiLMb8WX7exUBBSKxdkAvT9V8cn24kdKbn4KeBhpdQCuDVdDTn5xKGnK4ARytHND69kl3nl89O6x5vmweCbcKGmFAm/+v9ApasQoKRGPlgDYfgHTZ3Xnu2v6W9MtFF1DFjtCJj+gkoEA01g1o+zFGt92t460/ldOFuALarHcev9QsKNvwQDRWvxa+3XBv9nxafy68C9EFVNw07wd0uqAEFIiAj5uJVL1qEtDOIYlaR+Ir8avjrwcdcgAVAQ2rPO0Fbb5Rb8T3fnz87aBDDqAioDGQB3RY0MGGPAUFwiOgETA4nZ6AAhEhoBFQbsSrCnr67ZDDDICAxkFrI55VUCA265/G9Pxq78356YvDlwR0yLSg4h8BCGX1gMoQ0KF2pVJzI/70R0GHGQABjYNyFXRYUOHkewIKBLbypZwfr2TecinniHwVdHQ+ffs73d8ACIWP9IiEIqDD3aAVAQXiQUBjoVlQNuKBeBDQWIirl9O7QcWV0dBDDWSNgEZDvQpKQYE4EdB4KFZBlWeDVgQUCIuAxqOUf8oxBQViRUAjol4FVRZUCCgtBXwjoDFRr4LOF/S0SgrAHwIaE1VAJwra+0SQsEMPZIeARmWioINsDgI63J4H4AEBjUovjhMFHa6ClhxRAgIgoHGZCOjEzUFLLkwCQiCgkbEpaNntCA078EBmCGhk1I2cKmj7x0GHHcgNAY3NVEFn+klAAb8IaHS6LGoXVPjbgAMOZIeARmdqFXSuoAQU8ImAxmdqFXTqM5IqAgr4RUAjNLUOqv4Zp4ICvhHQCMlXP7UKSkABjwhojJoYygI6fWs7Agp4REBjpA7o9I2ZKCjgFQGNktBF7YKyDQ+4UWovRgQ0SupEzhWUgALLnNZFtBDQOJXKbXgKCqyo3ZjTQkDj1LtPsklBCSiwQFka7QkjoHEqNQsq+3nYIQfS1Sw/BFQumYC2BZWvZ+rc2w6AkXbp0V+GCGisFhQ08JADKRKWHQKqkFBAFxQ08IAD6RGXHINFiIDGq92coKDAqvqLDQFVSSqg9gUNPeBASgYLjckCREBjRkGB1Q2XGAKqlFhAu48rVhV05v6gAKaNlhejxYeAxs2yoKEHG0iDZHEhoGrJBXTyms5SvRkferCBBMiWFbMbkxPQ2NkVNPRQA/GTLintnSG1ENDoDQs6qCkFBezIFl0R+QkAACAASURBVBOzfhLQBDRvKQUFHFKtgJosPQQ0AcOCam3Fhx5oIG7S5cSwnwQ0BaeNeOWeUAoKTJAvC9Ll5PQQ+0Dl0gxos/ppWFASClSqXZrSxaR5gIDKJRrQZmtDjKNOQWkooNrXKVtImu8IqFyqAZUUVGsdlIgie9KPaegtUOKjvf/OI6CJUOZRo6A0FBmT7tNsDhb1lo/2KwKqkG5ALQtadbt1gBw1C8HwwXbpED7HQ/gcHc1nJ6DJWLQOGnrggZWpZvNBHJvfPa6YCucHdk9gssAQ0HRQUEBJMZufOlkNAtqe2dK/RuX0RPqvmmBAdzcfr66u3t58tvjbTQd04vZ2FBQb1+zQlDxeDs9MEs6rFv6y+a6UHnVSSC2gn54VnfvvTP982wGloMhWW7/xw8dHux+V3eH3YUGbRWWjAf36qOi789LsCRIPaNXLoSKhBBT5qReO43+HZ8a3B9rbn7Qb8F1aB4uK/uKSVEBvz+to3rs4+r7+5uwXo2dIOqCDqzkdFJSqYiPaObw/p4s7O6v2N5pzQ2XhLMtyVOEJKQX0r4f7YL4QHviwD+o3v5s8RfIBVV+MJBRUldDR0+nPJ0DMxFlcnK37ReyduDRD84VTCuj1KJd1Uh+YPMWmAqqgWVCz+QSImZhJoaDNCqhQUM1+bjCgu6dFMdxgvy2K70yOxqcf0Ga9cSahM3OF+YwCxGuYTvG/4klK/YUhs4DuVzdH2+uyx6ZsIKA6CZ3ZEWo1pwDRaufjfkGnQjlYiyCgWtIOaNVtmcwXdOrS+N5fhx0lYDFxNhYTKu2nernp/UTzpRMK6H4T/uzl4LG8NuEr6Ta4RUHbZzC6ag2IU28mHhRUa4en5Pc0XzqhgFaXo1rWu0XvmjzFZgI6vx2vsxnPkXhsQDNLC99LdlWZ9XSLAf1yvi/ob8IDX/f9HK2UTtpOQMXTg40LOghpqNEBXCjFszxPD+ic8ZdbQOvzmPbFvHh+VXtzPJPe6Cym1APa34afmyt0doSyCoooGcyTbT/7G2gW0dx6QKv354NLOc+emD3BlgJ6/N8lBbXc7QOszWRu7F3XLDzBMpovnlZAq91rMaFnj03vyLSZgApfTJnfjF86AwHumcyBvX66K6jmqycW0L3dp6vXFxcXj6/eWdzPbisBLftbK2oaO0Lb2cVuDgJcm5oBJRck92dd8SkW0BzU9AJqoJAIPUwLtecL9x9Um90RqphdCChCmWrY8PGy309xh+gymsNKQNPSrCsOH+3NQT12BTWYhQCnurlY+rP+T0bzsWEndRcIlXQDuvt49dZ4I34bAR29vdPzz6ig8hPkRk/ob6yA1nEGlc6C4/lVMh9rJ3Ka5tCmG1DTqzgPthLQ8cNTtFZCJU/nbayARilukw9+MJxjTYJoSnNwCWhiSunntczMDIYFNZyHAGfK7vo4YRbU6N38r+gy2teXUkD/vBF92gf03f6/f5g8xRYCKiub1kwx9zvjJ/M6ZoBwUVEp3MRznqN+mh8sSSig9d2TJXK6G1OlPLqjNWvM/c7omfyOGrJXtsUcXEtUubi4aIqYFJN5n4AmRvHWas4ic7/DvUIRUNmekFyVww2hcrV+jtppMusnFNDDhZxnF41/PS/O/rb/788Z3c6uWhJQ3ROaRL7HDulaPruUExd0lKqTRwzn6D5pOo1m/ZQCerj70p3mdkyZHkRS0Z5fzOaw0KOFZNjMLeNz4qvxWc3tz8xm3TnqdprM+UkFtKr+uV/t/PvxSwLa07zp8zON0VwWerSQDIvZpfcnZb+fvUPxpeN+TrfTZM5PLKDV10fNPUEJaE87o3Uzn3LWMZnVQo8XEiE/v27mT4Q5rBz0s/uydNrPmRVP0xk/tYBWu3+cbmJHQPu6N76Z5yZmoPn5jJPpYeQ455n9RVn1t9FPXxxmv+7rbq5eSjeeBjN+cgGtqi/7ldD7nwnogPC2iwFUzEbzs4/hjIS8ya8xnv8TcRu9+ba7kHM8O1ozaKfJfJ9gQKvdq/1K6AsC2te7dKOcLKheQs1mJOStqaHpX1TjLfn+vOti1dM0ngbzfYoBPZ7Q9LdzAtrT/UOuN0c5npOQs+5fbv2/6F/6Jn5p0DmtWd00ngazfZoBPZzQVBDQHtlMMD1fWc1JBBVj4qaP5KeyPyjbHwjz2cxca8oynu3gzEs0oIcTmghoz+DN15m3LGYl/VkL+RDXJmU/ld0+rOrt/yy7/23nO73Zc2oGt4xnqT+XJxvQ6uuvZhchHWw5oCftm681hxnOSiazFvLRO51T8lPJjT0rYQ6tKo3Z1YTtdrtAc8zTDaiVDAI6OqI5nDEGs5nJvGQ2byEX4iwhW9scPTjYVd886ISDeNY0R52AblD7/ivmjMHMpj0vGc5byMVgK2X4w1FAJeucw+8tuYlnqT+PE9AtaucAzTlOb26am7sIa6b6M0Qp+3a4I6hyfWV76TCeNd1xJ6DbVOrvWNKY50ZPNfGSyI50/bL/XX8jf9TPxTV1tN0u0Bx3ArpRp3lAe+6bm5tmZy+DmQ6bIjlENP66tyOocrn+aXJ9pgHNkSegW3WaCfTnQe25Sjp/Gc112BLFMfb+l4MvTHM2NeO6j2dNc+QJ6HaVBtcQ68yEvWcenx5qMtthO6TnLam/cn68yH07DzTHnoBuWGlyFwbdo0nCDtHeK5nNd9gM+f6c8c8cnu7pesVTHMjxY5MI6JZZzJZWc97pG4P5DpsxEdBmfnB5prybeDZD2X0jDPv4MTUCumk2M6f5nNh8ZTTnYRtUp2QIPxvOMYZz5WD+dHeOvOLf/P4vzSCgGzead+Zn0fkZcPSUh0VF+u85Nm7inDZZP4ff63O11d4fStUJJQRUKr+AChcc68+nGvNg//tTmSlodsQLfQePl729ntWSA/BrHi+SjxUBlcowoKMZ9viYej7WPCA/eIqq+9/A4wufJGcstd86Ot7usp7yxWFqtGYQ0AxYzbEaM6JkPXRinsQGiSug/XdeWitj656qVCrnVQIql21Axa9159uZGU++JT81V2JzeiugvXfetGSquXCderaDuHQCENDcnOaauX2i8jlXcUB1tKc19EjCj/G/zMNvTcvWm/+cx7M/7KWDGZWA5qfU27haMv+GHkX4MQpm/9Rgy/XQVa4xqtbIJwHNUqk5a5vMxFX/i9CjCB/6/RS34rW7Jp/p3MSzGmwZiQNeOppHCWiOyqag7bdTc7PpbOtu7kTcxPXN9jzg0n710+maZzdopXBSavuQo0lAQLNUDjo3M09PzqeKR4KOH3wQ/wEeX29kGFDr7Xbl6wjDNtrZ4GwaENA8lWL5qsl5fW62Vvxt6DHEOka3B6lkc0H7uJYlOz3lrzTcqVD21hUcTg4CmimTedR2zaB7sWCjCbfEd3a0u7Pqvflmc5fjI0bd6zen23XD73SCENBcjea0+blcZ84dzsanFwg3nnCo98aO5x67Szbc7fTsvmqHrGpv1VC53no/IKD5spjXbWZrcZ5GyoT3sxIupLA95O6unu3ACUfbu591a6Gl+/mQgGbMZoZfNpcjXe372HVp+dlK9k8g11svrrrAr5RPApq30bw3w3SeH74A0jV+P+2tdolmNVrX7P/UPQIK8+Ol1rM3EnV6+yzf+PEctNLtQYZnOFfVcMeoawQUR7rz6JLZP/Q4wk7z5pX9L8wtquf8y7Zn50mHfhUEFB2jxcByIUBymjfO6h0fzjYLDkQOT5Tqhq7sD6DHaUNA0WOyLGjO/evviMKKujdN7/2emmO076zQezHZSw+HsOzq6nXqEFD0mS0Rur+tmPmVMzuhjYTwhlm918K8smjXef80T9lG+mjm8oKAYsDHYiG8lHIQPI4yVPpvllVDFx406oakG57xzDH8bW8IKIaMFw7L5UI50wdaFjDUD5hFP5cfcheHRfhaPqwB5hkCiiGbZWRmKZj5yej1Kzbiw+u9NdNv48ScsfCEpd7gtF95nhITCChGDOdxjcVk+JSjO1D0X/z431Cjj4Pe2yJ7EzVmCsN4VqN5bzBAzRc+p8M0Aooxs9m+XHpyqLiodF/GtJjkp1+v4ztkODvY3b+rmyNK+aZJXDMGAcXYMG8ai47xMiO8ipDQdqEZfoZNN2zepkLGBvU6fqv3vi7ZcG9eTLUbnICGRkD19C6I0114FpwoLSyjlfCa0iGLafHZpl7PKpN+2tdTOImz/Y9syCLrJwGF1HgB0mKzAPWvsxZeXXWs1dc0yIDqaHb/isjje6H75pu89+08IPwrLd5CSTJwBDQkAqqpHyqTZcFyJaQSCnpagOSn+0W2ACVtHCnDd230ttv9cdWuV56+bR6VDXFcbz8BhYZT0tZamoYX46l2gjU/CTINNmc8iXvT3vjtXnKlkWxzRzXQq04UUwQUOoyXCifnAI4Pw3b/i4VGnWq+k70Ps2/0wnPlJds7vqeHHQIKLaVkPXGGixOpB8NQHXbbpbJwRW04jZsvJe/B7Fts+c52Ly85YSmR95iAQs9oxtfh8FK+ruHRbcalaDCVhf81fXNN31DxrW3eyXTfUAIKTeMlYM3lTFjKBq/PcaTlhPfR7riR9b+M3f1AhDc44feTgELTaFEwXtyMlzZhGeu9WspLXAwM3z7F22l3xm/7jpbiV2GnxwIEFLoGS8KoaToLncUyp3j60FMjYWVvFdDQwvOVym4XTPs2pvxmElDoGuy/quzu22S/P3RccNgo7fvp4p/BZgiqbi4KOTUWIqDQJlws0jww6prWAujkIxlDTomEHSedzQS3v0xT3N3aDETXzaTfSgIKbd0hgNO3NnvS3DU06LRIyuBQ3Pg+Ltpv26L3qxz+03v60vfkcImAQt/wrCLhZD6jRclyaRyeewM9wsQS37OZyTt6uww+EU75EuIwDb5IEwGFPtUSYLFR6ORKJe3hdjwdkiJOrHJ89E/zjTK4Lrd/QxL5G9Z9l3Y/CShM9Dffha/Nzq+3XDRly6rWQCe+kC5yHPvD/5q/TxbvUP8aUcW7Jf5CiIniDgGFAXGtU3y0nNxwm11AFx2bnxtirV/bquOo201Z41XP3psymiP6gzU8FJ8qAgoT0jOfxaXGejldHlHJojj+pbwcx3vBu2LyJgzfjv65bv3Bav43+beFgMJE7wpm4dGlFkdUtqE+/JXcnEba+t3Qn/CyqSzd3dM80PxG8u8KAYUReYsGy4/5Iissto6256d/moPjKNu+C7oTXD2JVRO8FNdN154IayOgMKIo0eHRshxstplbHNFqagDGg73ehArtNL52k3/ZxO+GQDFgM7+REgIKI6p5frgIWS6CvcXYyQVLfeoB3pjTuJlMG/uJLrxgfxjkA7b6yHtEQOHGKEcWC+J4aXYf0eGQqUZn/Sm2ptL0hM8FU3tqKPQeSxgBhSOjRclmYZQt1O4iKh0k1ciofhSYeqD6q/+V/htgM5m7vTZTwyrZaRLlRLVHQLEekyVyZuF2uyoqDtn0cJuMravJNvkiyqHqHj99pTcp7CbvcKe3cmjHA+lgIkSEgGJFZkvl3DLueHteWaPm0e4XNcfV7cRTvYp8oHoj1fvNCYsmq8ZRdM3jSgkjoFiVJFh2VtyeVwzy8GuNEV1hAo5fRTZUNhNg6RF3nfEd7RZ3PkUCI6BY1+RCaLfIL6/o5NmLw4dlvyUbTR/nNbYD0h9I8ylgMB1lz19p1nB4aq77KRIYAcXKBstd7xGLRb9wVtHRcImt7Odw+Huykaw8nBle9o7bWI+u2fQbv5jBqPZXk91OjRgQUKxtnB7rJX9QgJVOFj0aDnf7uHwUB1/0f+xwUg5ew3i0jCdb9yrCubOqCSEZYtmX20FAsbrxwma82E+lYK2KjjPR/UQ5gpKfutt0rZ+oV1DTUTKcXJKtB+MxEofVzVSICgFFCKZL/nwT3FS0N4RVL6GK36vGq4SycXU00ZZMuKUHjabGcWqgzf8mIQQUYQyXzwVlWGXHqHjAe/oE/OZrRSnKifPNzZqyZHRsps7oPeoGw2ioZVNlKwgoAplaVK142C8qOwW/+UJ8QLYmK58E5hOsMi/p7FSZeEbpi5u9z8ZjmpAEA7q7+Xh1dfX25rPF3xLQiJhmQMuiHaMGw9SehF/2DrO0IyaMZNXfbzmYAMbTy3TiLTtZXvZ+Gb/PGz2AVEstoJ+eCQvI/Xemf05AYyJZUu0W8yEfB+m7tbZmuPsHdyrxYWlB+3+iN7Usz5y1HUX5MBi/zdvtZ2IB/fqo6Lvz0uwJCGhUZIuj5bIusUpFZXse2vXM4zenseiPi3zfqMm27ejVZxmPfHeeVPO9Ew6fKjpJBfT2vJ4f7l0cfV9/c/aL0TMQ0AQYRUIzIivvGS1HF4aKD8nO35zctq8kh5eGrzQ3mezHWRg+Z2+oq6eKTkoB/evhPpgvhAc+7IP6ze8mT0FAU9BbkF30tPCV0YPBYDff9UNSKroq/lgyTXSZjKpy78AKb+3mpBTQ62KYyzqpD0yegoAmoXcMxpnCX0e7QRdGZHDCfbtHUbkbY/yIHqMRHO7Y7I8EZiQU0N3TohhusN8WxXcmR+MJaBrEBAnHY1zwmNGyXRuthgXt/YriYLe0arMMR0xyYEg2UFBIKKD71c3R9rrssSkENA2ShlRrro6uebC+GnwtjEm37TwcxUoMm/6L2R02kl46RT+1EFDESLEM65fBKDjeVkm708p7j5b9Ne7u8JLRafPmY6A8L0n+KMYSCuh+E/7s5eAxNuEzY1AHI6uXdHLIhWP2wvH7NbfcxReXT2fPb2yqEgpodTmqZb1b9K7JUxDQ1FkUwsSaGa2kX/Z+o+qf/6TJbGC7E/vb14S1lAL65Xxf0N+EB77u+zlaKZ1EQLfFsDQGvB5qOrIbI5thDPyubUlKAa3PY9oX8+L5Ve3N8Ux6o7OYCOgGqQthHCNlnXx21ITdgIV9v7YlqYBW788HW1hnT8yegIBu0ipx6kSaUcvhCf1mbUtaAa12r8WEnj02vSMTAd2mNfo0svJhJqth0f8Lydn5WC6xgO7tPl29vri4eHz1zuJ+dgR0q1bJlMwwowFCarXbs/kvnEovoAbG8zoB3axRMdaOaqCSLnut0O/S5hBQbISiEMN0OGhYn2wuW6mmS5/b6/uRh00HdIyA4sBl1hqKkrqqqclzyccx8DTfpMQCunv9072//Vu385NLOWFnmBYXiRO5Danh36tGEs6lFdB/Ho/Bd0ffCShsScpi27c5y0pq/Dfy0QkykTcvqYBet/Nfc0knAYU1SVhMumbBPKQ2wRXHRTqWcCelgNaXct55cXPzqv7vMZsEFAuMy2KaREt6G/gG9Rx9kNHgS7/TNSMpBfS6WfOsP1vuWFACCrccFVKTPKSL9pp2YxF4SuYhoYAKd6Svvzy0lIDCMVWUlpVSh1U9e4Mm3sk/5DTMSEIBFWPZ3MeOgMK1UZ+kP4hIJdzontvJe5ZoQJuPkyOgcK6LkrO7Oq1CGLSq/Z/2J/Ai1YDWR5TOXhJQrGGcpFIo1ek31r9YdEpvYBU/wfoSCujgUzlvi+Kb3wgo1qBoUikco1H9rhfqwfUyedBKKKD1Ufi7/W+/+Q8CijUoS9X8UPK73qgHeK3JAZWUAlqfB3r/j+77y8ORSgIKf9qPFOo/GDqfCCWlgB6uRBJ7+YqAwi9Fw1SBcxJMqhmxpAJaf6RHr5f1R3wQUHikiJl6/VDSVuN+rjlCWCStgFa7Dz/37kO/e3VOQBEBVT9VP6Kf25BYQJcioFiLYelUtaSfSSGgQBBCJaV3nut9g0gRUCCMsneAaJhQ+pkEAgoEMgikfB8ookZAgVCGgaSfySGgQDzoZ2IIKBAT8pkUAgpEhn6mg4AC0aGfqSCgAGCJgAKAJQIKAJayCygAuOO8Ua6f0KXQExvAtjhvlOsnXFHuW/SMf+ghCIvxDz0EEjEOk0qUE9Ajxj/0EITF+IceAokYh0klygnoEeMfegjCYvxDD4FEjMOkEuUE9IjxDz0EYTH+oYdAIsZhUolyAnrE+IcegrAY/9BDIBHjMKlEOQE9YvxDD0FYjH/oIZCIcZhUopyAHjH+oYcgLMY/9BBIxDhMKlFOQI8Y/9BDEBbjH3oIJGIcJpUoJ6BHjH/oIQiL8Q89BBIxDpNKlBPQI8Y/9BCExfiHHgKJGIdJJcoJ6BHjH3oIwmL8Qw+BRIzDpBLlBPSI8Q89BGEx/qGHQCLGYVKJcgJ6xPiHHoKwGP/QQyAR4zCpRDkBPWL8Qw9BWIx/6CGQiHGYVKKcgB4x/qGHICzGP/QQSMQ4TCpRTkCPGP/QQxAW4x96CCRiHCaVKCegR4x/6CEIi/EPPQQSMQ6TSpQT0CPGP/QQhMX4hx4CiRiHSSXKCegR4x96CMJi/EMPgUSMwwQASSCgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKApWQC+vXZeVGc3f8t9HAE8NfDb37vvstqQuxe3yuK4ltxbLMa/+rDT/X4P/7cPZLX+B98OS9+ab+JbPxTCej7/VSrnf099JB4t3taCAHNakI0I1sUPw4fymL892/9aWzbgGQ1/kd/PSyKaMc/kYDeFq1f5n97U3aXhRDQrCaEMLLF3dFD2x//tp/d2GY1/ieXwshGN/5pBLT+N+jOfqX90yMxJlk4LETtOGc1IQ4j+646ju3Zy/ahXMa/ut6Pdr31/p/7sf3usBWf1/gf3Qq1jG/80wjodTMD1Tl5EHpofPpw2GJp55WsJsRtu95Zj+3hy6zGfz+Ox3826m4cv8pq/I/qZrYBjW/8kwhoNyfV+5O/+zz5y1vydf8PbXH/URvQvCbEZbeZdhrbvMZ/P4qnf0DqcNSTIq/xP6iPAPzPZkaIcPyTCOj+H6FmYgmTMAP1NtwT4SBSthPiNOLZjv/p35IMx7/+p+O6CWiE459EQLttud5qyfZdn/34WTwKn+2EOC05uY7/fvQPs0B+479f0XxQtQGNcPxTCWi7v+M6junmx5/1P7f9gOY5IU5LTp7jv3t9fhrX7MZ/P+/v/+EUAxrb+CcRUHFa3Uay89gfIaC5TojmIEqO4395OOvxyeHr7Mb/8vC+t6Md4fgT0OgR0PpU2OYgfHbjfwjot08Ou/5yG//TOBLQhSKcbh5lH9DuUoIMx3/33+/9VJ/Kdjh4ktn4N8eMCOhCEU43j3IPaH3K3+mAa5bjXx1PZ6tXwTMb/8vhv5sRjj8BjV7mAf3aXoaU5/gfnHYC5zX+425GOP5JBDTCg28e5X0Uvr55xJ3mQqwMx//k+jDmWY1/dx0BR+EXivD0L4+yPg/0ur4TU3vFSX7j37htAprP+F8XfXGOfxIBjfACBI9yvhLpVdHbUMtu/FvHgGY1/pKARjj+SQQ0wktgPRICmtuEuC76S0le438t/ONxXN/KavwlAY1w/JMIaIQ3YfFIvKFyXhNiv9r1Tf/O41mN/74RwtGzwzyQ1fi3ut2d8Y1/GgFt7gwZz20APRIDmtWEaG/i1nson/E/nL9Vn0H/9VmzJyOr8W91AY1v/NMIaHw3ovao95EeOU2I/jbcsaU5jX/19WE3tjnekb8hHHCPbvwTCWj1z8g+CsWj/mci5TMhxA+0aAOa0fhXp/vBHrRnImQ1/ifiGUuxjX8qAY3tw/g86gc0nwnx10NZQPMZ/4Pjp3Lm+6mkB71TPiMb/2QCCgCxIaAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYAiXtdF8c3vwvdfzovil2BDA4wQUMTrr4dFcbf7dve09y0QHAFFxG4LcZVzuEIKhEZAEbNLoZlswCM6BBQxEzbi6w34B2GHBhggoIjafiP+7OXhq/0G/Hefww4MMEBAEbfLUzf366Knku7t3t8riuKHF21Q/3xdP3D2w4vmj777/OH7/W/81vzut4//8D3kyAABRdzqjfh6y/1S2ICv94Ye3Pnt+MB10TiupO4D+r7+bp/c9nfZ/od7BBSRO27E3wob8F0TT0eYun6eMnlZfHv4nbuH/jY4AgXXCChit1/3vLt72m3A102886LeNj8/HmGqH/ix3kL/9OiU2ct6XfSwdrpv6513+/9+fcouVLhHQBG7uo/3hC3wbmX0tF/0tj1Sv183PayTXra/ctmseO4TzEmkcI2AInq33c7N2mW3Mno72LO5L2oT0NPj182qKLACAor4XRbCEfh9JMW9od21nX9+/PW8aAN62uF5iO/Z3/6NrXesgYAifr1OioeFmjXT3YefmgNLp4C2wb08PX7GeUxwj4Aifr2ACsfgm4CKDw0DunvNeUxYDQFF/IYBHRxOP66TfvvDz8//38NRQPc+PKOgWAcBRfyGm/CDw+nX7Rn1f0kDuvfnm2cF5zHBOQKK+PUCWt9UpHdKvHCO6O1wE144eWkcXmApAor49QJ6uC1odwnnAyGgXx+O9oF2JzQRULhHQBG/fkDrXZ5nT/Zb43++Op7edHnchD8eLjqU81JcJ+2uUuJ29nCMgCJ+/YAez+0UjguJ3w8D2p7GVHA7e7hHQBG/QUCr28GZSa/acz1P+0fF05ieNr9752UFuEVAEb9hQPdb6/17fH746fDt5+ay+N5R+E/P6t4KNw8FXCGgkBYeIAAAANVJREFUAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGDp/wNelRINqJB+agAAAABJRU5ErkJggg==" width="672" /></p>
<p>We can also compare the biomass index using <span class="math inline">\({CV}_A=0\)</span> (black color) with the mean of the above 100 indices using <span class="math inline">\({CV}_A=0.2\)</span> (red color).</p>
<pre class="r"><code>a=matrix(0,ncol=100,nrow=41)
for (i in 1:100){
a[,i]=IA[[i]]$biomass[,,1]
}
amean=rowMeans(a)
plot(1:41,bio0[,,1],type="l",xlab = "Years",ylab="Biomass index")
lines(1:41,amean,col="red")</code></pre>
<p><img 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s1bOr30R783rbGAAu2Jcx3oxfSI5d2dVqRfPRKweOFGxwMiBNQ+PAxGpNWYvi7P/gSvSL/p7FPTM1IRAmoKCoMRbTm7z9WK9D/usCK9gAJpyWg90NXr8acS3IVXUBiMjAJa3gS6VsvysOidK373RgIKtCengH49nBS0fgT129Omp6SiBNRpJBiIWAH99PvR0q+BF9KfVifxj55XK+S9mV5J3+ya0igBNQWFgYgT0K+H7Syo/GH1dSY1fdJw4wQUaE2UgK71c4fFREav6y918LjpVDZOQO3DwzDEupWz+Pn52cKfu7z+6PPJ66Ojo8cn7wIOBMQJqCkoDEOsxUQanStvy6bnMUX5wgIKgxBpObvQ2zd301lA7cPDIMRfD7Qto89vmx8KiBRQU1AYhEi78K0F9NPJSXUl6HRp5YO/N/zjAgq0J9ZJpHaeIze9jOn2l9nSykXjtZljBVRBYQhirQfayhT0dFbNO9XSytM16ZudnRJQoD3RLqQ/eHy26yuXl5Peev5msvP+r7M7kMqiNprbCijQnkgnkVq5kH62mEh5VdR84nmc4mIiFQWF/ssooIsV6S+WS4g0fci8gALtiRLQR3dX/RwU0MWR1Noh1RQXVJ4SUOi/jJazyyugCgr9l1FAFyvSl6ekXk4/l+4uvIBC/2UU0MUZo+PlMqCnyZ5EmgQ03tcCOpFTQMuHI/9ydvaqfD7yYi6a6GVMY1NQ6L/9BnR6iLKty5iqqWd1+9F/TsJ57+TkWeNbkQQUaE9WAR29mq6j/HJxT1LTl4obUPvw0G97Duij8pKlli5jKn3+/e7P1TL0/5zeDH+v4aLKMQNqCgp9l9Mx0BWjT78fPW+8np2AAu3JNqBhIgc06fcC2JWA7pEpKPSbgO6RgEK/Ceg+KSj0moDuk4BCrwnoPjmNBL0moHtlCgp9JqB7JaDQZwK6V/bhoc8EdL9MQaHHBHS/BBR6LG5AP5TL0L1v+ys20EFAk/4HBdhFpIB+fFiuO3c6X42uM9EDagoKPRYnoKfVwp1fD3dbDrQFAgq0J0pAy3JOqllltHy6+/22v+bWugiofXjoqygBPa2evFGm8071ZKNGz4FrVfyAmoJCf8UI6Ox5xLMnwDV9lHurOgmoKSj0VIyAzpJ5Oj1/JKBAT0QM6PH09NHQAmofHnorXkBnh0DLPflmjyJuUzcBNQWFfop0DLT4bX4ItJyIDuokkoBCb8U6C3/r3f+o9uBH/yimHe1GFwG1Dw99FSWgk334yv3pr7qbgHYVUFNQ6KU4dyJN70G6/aUK6C+dHQEVUKBNke6FH308+vXd5L/f/+Xeu7a/YBOdBNQ+PPSU5ewiMAWFfhLQCAQU+sl6oDEoKPSS9UBjEFDoJeuBRqGg0EfWA41CQKGPrAcah4JCD1kPNA4BhR6yHmgkCgr9Yz3QSAQU+sd6oLEoKPSO9UBjEVDoHeuBRqOg0DfWA41GQKFvrAcaj1XtoGesBxqPKSj0jOXsIjIFhX4R0IhMQaFfOgno2RCvAy2ZgkKvxDoG+uZo7u5Pg7wTqWIKCr0SJ6AXs5VAi64XBO04oKag0CvR1gOtuzfUXXhTUOiVWHciFffePCgO/tvrh0Vx0N2NSJ0H1BQU+iTWvfD3q5vgf5svrtyVBALa9RYArYm0GlO5EOhpldHpyiJd6TygpqDQIxHXA509y2OYj/RYMAWF/ogY0NlCoJOPBrke6JwpKPRGpGOgs6XoZwEd7mVMYwGFHolyFv64Oga67OigA6qg0BuxLmMqD3tOT8NfdHkaXkCB9kRbUHkSzUk6f3j3nw+GfRJprKDQG5Fu5Szmj5UrlfvzHRFQoD2RFhP5+LA6/Pmw6ueTtr/k9pIIqIJCT0Rezu7j0dHjP9v+ig0IKNAeCyp3QUGhFwS0CwIKvSCgnVBQ6INoT+X8/Wjp10FfB1oSUOiDOAH9YEX6NQoKPRAloBerC9ILqIBCL0S6F74o7r09W+juQqZkAlooKOQv0nJ25VLKKUgmoKag0AMR1wNNQToBNQWF/EVcDzQF6QTUFBTyF3E90BQkFFBTUMherOXsulvBbkVCATUFhezFuQ7062HxS3fPMq5JKaCmoJC7SHcifXtYFD/enft5pyOio7NPJycnb89CipxSQE1BIXfRZqAtXUj/+Vntde69a7xxKQXUFBQyFyWga/0MD+i3h2v3NN162XDjUgqoKShkLtadSAePz3a/E+miCvHd2ZIkP1XL2//WbOOSCqgpKOQt1p1IzTq3Wfk6By9qn/h42HQ2m1ZATUEhbzndiXR6KZeNbxJNLqAKChnL6E6k8qGe6xPZpg+ZTyyg9uEha1GOgZ62sgu/aSLbdHKbWEAVFLIWJaCTuWOjeeJmfQyoo6CQszjXgU4KeqvxNZsbXuTSLfWZ78KbgkLWopxEenT3bnXV5o53Ih1fqmV5WLTRXfbJBdR5JMhYrMuY2riQvrwe//b72ie+TfrZbJ2n9AJqCgr5yimg5cmoSTGPnp+U3kyvpG+21H16AVVQyFdez4Vfe7rnpKZPmr1AggF1HgmylVdAx6PX9YQePG56bj/FgJqCQq4yC+jE6PPJ66Ojo8cn7wKujEoxoM4jQa7iBfSv8sDl26hPNC42iPn1tyWgkKdYAf3wU+BRy502JZOA2omHPEUK6Ktawtp5uMdfn0L24dMMqCko5ClOQMvrjw7unZy8eVaeA9rhAXOfH1WXQM3PJd16ceOfWNu4NANqCgpZirYi/WzeOXrV9Nr3mtGz6TWk5Q1IYdPZRAPqPBJkKdaK9Hc2f9BI1c1JQKv/HhwdHTWfzqYaUDvxkKNI64HWJp2T6Wjg0kwXk17+ly/T/1Y3II3+kf+tnDN24iFD8VekD1+f/njWzdoctul0NtmAmoJChjIK6OQPVtPN+X9LTaez6QZUQSE/Ge3Cz8tbL3D2CyovOY8E2cnoJNI8lvVHLPUooKagkJ0oAS3P+sxvQPpQhD4gafFQuePlhDb7FelrnEeC3MS5kP64vOj9+dnZ2ZuHO1xIP3+s8dfD+UuUTc35scar7MRDZqI9E2kp+Ply5cLMt8oV6U/nlzE1vio/6YDaiYfMRLoXfrS4Gf5gh1vhL8or53+ezGT/MSnp4+l9odmvSF9jJx7yEm85u0+/Hx0d/fp2p9f+ur4ifcN+Jh5QU1DIS2YLKq+uSN+bxUTmTEEhK5kFdOKvN0ePqmcj//q8N8vZLTiPBDnJL6A7ST2gduIhJ/sN6PQ699Yea9zCxqUeUDvxkBEBTYyAQj72HNBHd3/+o/zfVT8L6NUUFLLhGGhqnEeCbAhocgQUciGgyXEeCXIRM6Cffz86+q/v2/56jeQQUFNQyMX+A/rX/zmqrnifLyhyq8uEZhFQBYVM7D2g1SoiB3+vXct0ELYcaCtyCaiCQg72HdDjWTWfnFYLKb151OlloJkE1BQU8rDngJbLJ916fvKsmnm+nH+muyloJgFVUMjCngM6fwDS8XLludPwJel3J6BAe/Yb0MVjjC6WS8cHP5WzDbkEVEEhB3u/F36x4z4/8hn8XPg2ZBRQBYXkxVhMZLxSTQHdioJC+gQ0VQIKyRPQZCkopE5Ak2UnHlInoOlSUEicgCZMQCFt+7+M6fnJxJvD2S+qXwrolhQUkrb3gG4ioFuyEw9JE9CkKSikbM+3cn462eStWzm3dX6e2xbDgHikR+JMQSFdApo4O/GQLgFNnYJCsgQ0eQ6DQqoENH2moJAoAU2fnXhIlIBmQEEhTQKaA4dBIUkCmgUFhRQJaBbsxEOKBDQPCgoJEtBM2ImH9AhoLhQUkiOguThXUEiNgGbDYVBIjYDmwxQUEiOgGVFQSIuAZsRhUEiLgObEYVBIioBmxRQUUiKgeVFQSIiA5sVhUEiIgGZGQSEdApobBYVkCGh2FBRSIaD5UVBIhIBmSEEhDQKaIwWFJAholhQUUiCgeVJQSICAZkpBoXsCmisFhc4JaLYUFLomoPkqC9qn8UB2BDRj5yah0CkBzZmCQqcENG8KCh0S0MwpKHRHQHOnoNAZAc2egkJXBDR/CgodEdAeUFDohoD2gYJCJwS0FxQUuiCg/aCg0AEB7QkFhfgEtC8UFKIT0N5QUIgtw4COzj6dnJy8PfsS8Gf7HFDL20FsuQX087Ni6d67pn+834GxOBPElVdAvz0sVt162ewF+h1QBYW4sgroxWEZzbtHUz+VHxz81ugVeh5QB0IhqpwC+v3BJJgvap/4OAnqD380eYneB9SBUIgop4CeXsplmdT7TV5iAG2xGw/RZBTQ0dOiWN9hvyiK203Oxg8goAoK0WQU0Ml089L++qbPXWcIAXUgFGIR0D5yIBSiyCigk134g5drn7MLv5ndeIgho4COjy/VsjwseqfJSwwloAoKMeQU0K+Hk4K+r33i26Sflyal1xpMQB0IhQhyCmh5HdOkmEfPT0pvplfSN7qKaUgBdSAU9i6rgI4/HK7dynnwpNkLDKooduNhz/IK6Hj0up7Qg8dNV2QaVEAVFPYss4BOjD6fvD46Onp88i5gPbthBdSBUNiv/ALaQLFB19sUmQOhsEcC2nN242F/8g3o6NPJ28Y78cMLqILC/uQb0KZ3cVYGGFC78dDEeWXL3yygA2ASCts4X9ryT+QU0L/O6j5PAvpu8t8/m7zEMAM6/b4Y5tBhG7V0njc5WZJRQMvVkzewGtM2FBQ2C21nRUCHQkJhzXo6m/+AZBTQ6kbOg6O5fz0sDv42+e+vlrPbTpMjO9B3O0w7a3IKaLX60q35ckxOIjUmoVBqpZ2VrAI6Hv9zMu38+/SXAhpAQRm41Ynnzi+XWUDH3x7O1wQV0CASymC1HM9SbgEdj/4xW8ROQMPYj2eI2ttrX5FdQMfjr5NJ6L0vAhpMQhmW9ieeCxkGdDx6NZmEvhDQcBLKYOwvnqUcAzq9oOlvhwIarFBQBmC/8SzlGdDqgqaG19BXBHROQum5Wj3390UyDWh1QZOA7qKwH09/xYhnKduAjr/93uwmpIqA1kgo/RSrnuOcAxpEQOsKCaVvljvuUb6cgA6ahNIni3pG+4oCOnCFs0n0Q/R4lgR08CSU3J3Hn3rOCCjOJpGv5f3tnXwHCygOhZKjWjq7WypcQClJKBlJop0VAWVKQslBPZ0J/DgLKHMSStpW2pnGj7KAsuSEPKlKr50VAaVOQklOghPPBQFllf14EpJyPEsCyhqHQknDSjsT/dEVUC6RULqWQzxLAsoGEkqHVuOZ9M+sgLKRs0l0I594lgSUK0go0eVVz7GAcg378cSUx1HPVQLKNSSUWGr17HpTGhBQrmU/ngjyrOdYQLlR0f2SN/RaQmuDNCag3CiBVcPoq0U9u96QMALKFiSUfZjXs+vtCCegbCX3b3SSk389xwLK1nLe0SI1s3p2vRk7E1C2JqG0ouhJPccCShP5niwlFUWP6jkWUJrpxXErujKvZ1/yKaA0da6hBOlfPccCSojzHv4ksFdFz3bd5wSUIOciyrb6Ws+xgBKuEFFuVvRy131OQNnB4mejlz8c7Kro/T+yAspuChFlo/PzvtdzLKDsruj3ThqNnQ+inRUBpQXFIqJdbwndGlA7KwJKKyR08GrpHM7dagJKW4oeX63CDertHNJPmYDSIgkdpHo7B/YTJqC0y578wAy4nmMBZR+mCfVe99+w6zkWUPZk9nPV9WawT0Ov51hA2ZvFD1fXG8JeDPKc0SUCyt6ca2hfnfu7nRJQ9sgspY/Uc0lA2S+HyfpFPVcIKPs2zCuse2lwNxrdSEDZv4HepdI36nmZgBKHiOZNPTcSUKLR0GzN/+K63o7kCChR1SPa9bawHfW8moASm4bmZLY6jL+pzQSULohoFqytdRMBpSPn5yqaNEsTbkFA6dD5SkW73hqW1HM7AkrXzusZ7XpjGFsXuwEBJQkqmgiPWG1EQElIraJdb8ogFd7/hgSU1Pgh7ob3PYCAkiQ/zFF5uwMJKMnyUx2DiecuBJSk+fHeJyfudiWgJE9E92J5BW7XW5IxASUPItqueTz9ROxEQMmHHc6WqGdbBJS8LHc8/V0GsufeHgElP7WI+httSD1bJaBk6Xwlov5at2Nxz7YJKNlai6i/2+tZIGQPBJSsrTXUX/AVrK+0HwJK9i5F1N/yKvXcmwwDOjr7dHJy8vbsS8Cf9aPVU+eXyOiMxT33KbeAfn62nGYU9941/eN+qHrsckRdfG9xzz3LK6DfHharbr1s9gICOhQy6hbYGLIK6MVhGc27R1M/lR8c/NboFQR0aKp/ZweY0aGNtys5BfT7g0kwX9Q+8XES1B/+aPISAjpIxeWM9rkrQxhjKnIK6OmlXJZJvd/kJQR0wGw/yfIAAAweSURBVIaQ0dpJtK43ZRgyCujoaVGs77BfFMXtJmfjBXTwissd7XqTWlKLp+/zWDIK6GS6eWl/fdPnruMbi6lLGe16g3ZUa6dv8ogElAG7NBnN8/ujHs88R5CtjAI62YU/eLn2ObvwtGK1oll1SD27lFFAx8eXalkeFr3T5CV8h3Gd9Yom/+2S1cb2UU4B/Xo4Kej72ie+Tfp5aVJ6Ld9k3Gj1PH3KZVLPzuUU0PI6pkkxj56flN5Mr6RvdBWTgLKl80tS+97J8XBD/2QV0PGHw7VbOQ+eNHsB32s0czmkXW9RJYPp8TDkFdDx6HU9oQePm67I5NuNQAmVVD3TkVlAJ0afT14fHR09PnkXsJ6d7zh21HVI1TMp+QW0gWKDrreJvuiipMt67v9rsQ0BhZ1cDumepofqmaBeB/Qy33vszeaQtvTPtnqmKbOAjl4/uvu3f1se/HQrJ+m5uqS7vZ7v3vTkFdB/Hq6dfRdQ0rVh5/688ZWb6pm0rAJ6utglmt/SKaBkYHNJtzjvJJ6pyymg5a2ct16cnb0q/zvNpoCSmatjul5T9cxATgE9nc88y2fLTQsqoOTsmprO6tn1FnK9jAJaW5G+/GXVUgGlN9QzQxkFtB7L+Tp2Agp0J9OAzh8nJ6BAd3INaHlG6eClgAIdyiiga0/lvCiKH94LKNCdjAJanoW/s/rhD/8hoEBncgpoeR3ovT+XHx9X19QLKNCRnAJa3YlU7+UrAQU6lFVAy0d6rPSyfMSHgAIdySug49HHX1fWoR+9OhRQoCOZBXRXAgq0R0ABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCDS4gAK0p/VGtf2Cber6zQb6pfVGtf2CezT0PXrj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBilu01WSfAMjMv6ut6Bbxt/1FmyQ4jZdJck3MCLj73oLumX8XW/BBiluE0AWBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBMomoN+eHRbFwb33XW9HB74/+OGP5UeDeiNGr+8WRfFjfbSDGv/446Ny/I+/LD8zrPFXvh4Wvy0+SGz8uQT0w+RdKx38vestiW70tKgFdFBvxHywRfHL+qcGMf7JX/1stIuADGr8U98fFEWy488koBfFwm83/+5eGR0XtYAO6o2oDba4c+lT/R//op/L0Q5q/DPHtcEmN/48Alr+G3RrMmn//LAek0GofogWYx7UG1EN9t14OtqDl4tPDWX849PJsMu99/+cjPZ2tRc/rPFPXdRqmd748wjo6fwbqMzJ/a63JqaP1R7L4ntlUG/ExWLeWY62+uWgxj8Z4/SfjbIb018NavxTZTMXAU1v/FkEdPmdVB5Pvv3l2t/cJ98m/9AW9x4uAjqsN+J4uZs2G+2wxj8Z4uwfkDIc5VsxrPFXyjMA/3P+jZDg+LMI6OQfofmbVXsLB6Dch3tSO4k02DdiNvDBjn/2b8kAx1/+03E6D2iC488ioMt9uZVpSf+dHvzypX4WfrBvxOwnZ6jjnwy/+hYY3vgnE83740VAExx/LgFdHO84TeN9i+Ov8p/b1YAO842Y/eQMc/yj14ezsQ5u/JPv/ck/nPWApjb+LAJaf68uEjl4HE8toEN9I+YnUYY4/uPqqscn1a8HN/7j6u99MewExy+gyRPQ8lLY+Un4wY2/CuiPT6pDf0Mb/2yMArqjBN+3iAYf0OWtBAMc/+i/331UXspWnTwZ2Pjn54wEdEcJvm8RDT2g5SV/sxOugxz/eHo5WzkFH9j4j9f/3Uxw/AKavIEH9NviNqRhjr8yOwg8rPFf7maC488ioAmefIto2Gfhy8Ujbs1vxBrg+GdOq5EPavzL+wichd9Rgpd/RTTo60BPy5WYFnecDG/8cxfzgA5n/KfFqjTHn0VAE7wBIaIh34n0qljZURvc+BemAR3U+DcENMHxZxHQBG+BjagW0KG9EafF6k/JsMZ/WvvHYzrfGtT4NwQ0wfFnEdAEF2GJqL6g8rDeiMm064fVlccHNf5JI2pnz6rvgUGNf2F5uDO98ecR0PnKkOksAxhRPaCDeiMWi7itfGo446+u3yqvoP/2bH4kY1DjX1gGNL3x5xHQ9BaijmjlkR5DeiNW9+GmLR3S+MffHixHO8QV+edqJ9yTG38mAR3/M7FHoUS0+kyk4bwR9QdaLAI6oPGPZ+vBVhZXIgxq/DP1K5ZSG38uAU3tYXwRrQZ0OG/E9webAjqc8VemT+Uc7lNJKyuXfCY2/mwCCpAaAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAkq6Tovihz9qH389LIrfOtsauERASdf3B0VxZ/nh6OnKh9A5ASVhF0V9yrk+IYWuCSgpO6410w48yRFQUlbbiS934O93uzWwRkBJ2mQn/uBl9avJDvztL91uDKwRUNJ2POvmZC46K+nE6MPdoih+frEI6l+vy08c/Pxi/oduf/n40+R3vJ//3h8f/xl7yxkAASVt5U58ued+XNuBL4+GVm69n37itJibTlInAf1QfjRJ7uL32v+nfQJK4qY78Re1HfhlE2dnmJb9nGXyuPix+j13qv7OOQNF2wSU1E3mnndGT5c78GUTb70o980Pp2eYyk/8Uu6hf344y+xxORetZqeTtt56N/nvt6cOodI+ASV1ZR/v1vbAl5PR2XHRi8WZ+snctJqTHi9+y/F84jlJsItIaZuAkryL5cHN0vFyMnqxdmRzUtR5QGefP51PRWEPBJT0HRe1M/CTSNaPhi7v7fzr0++HxSKgswOeVXwP/vZv9t7ZBwElfSudrJ8Wms9MRx8fzU8szQK6CO7x7PMHrmOifQJK+lYCWjsHPw9o/VPrAR29dh0TeyOgpG89oGun06dz0h9//vX5/3twKaATH58pKPshoKRvfRd+7XT66eKK+u8bAzrx15tnheuYaJ2Akr6VgJaLiqxcEl+7RvRifRe+dvHS5fDCrgSU9K0EtFoWdHkL5/1aQL89uHQMdHlBk4DSPgElfasBLQ95HjyZ7I3/9Wp6edPxdBd+erqoKudxfU66vEvJcva0TEBJ32pAp9d21s4L1T9eD+jiMqbCcva0T0BJ31pAxxdrVya9WlzrOTs+Wr+M6en89956OYZ2CSjpWw/oZG99dY3Pj4+qD7/Mb4tfOQv/+VnZ29riodAWAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUIJKAAgQQUIJCAAgQSUIBAAgoQSEABAgkoQCABBQgkoACBBBQgkIACBBJQgEACChBIQAECCShAIAEFCCSgAIEEFCCQgAIEElCAQAIKEEhAAQIJKEAgAQUI9P8BoY/+ZJ4LbOIAAAAASUVORK5CYII=" width="672" /></p>
<p>The above plot focus on the deterministic iteration (iter=1) but the plot can be done also for the second iteration (stochastic).</p>
<pre class="r"><code>plot(1:41,bio0[,,2],type="l",xlab = "Years",ylab="Biomass index")
for (i in 1:100){
lines(1:41,IA[[i]]$biomass[,,2],col="red")
}
lines(1:41,bio0[,,2])</code></pre>
<p><img 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6sHm6zDA8lyF9A/r65+/Vwc+qNRt9YNKBtBgbQ5O5D+u9Nl7P56dOe3tR/RAAEFsB5HAX1dnYVUBTQ7e7X2Q863ekDV1wQloEAS3AT0ujwR6X+fHwN6eJZld3dxIP3oRtAVHwVAsJwEtDz/6Olx8FkevlQW9Oe1H3O2lQPKOjyQNCcBvcyye0Ud0OK2+sYTAgpgPS4Cehx0lts964Aeh6P+1uEJKID1ODqVs0xnHdD6Oz/WnV2xmGwEBdJDQBcYGYKu+TAAAsUq/AIEFEibq51ID9uAXu9mJxIBBRLnJKC32ekY+jKgx6/3chgTG0GBxDkJaHns59nLMqCHf2T7OZCegAKJc3MmUnlN+tZeTuUsWIcHEufoXPjDs7afu7mYSEFAgcQ5u5zd1+fl9ZjOHrxb+/GMrD27+nX4dR8HQIi4oPIibAQFUkZAF2EdHkgZAV2EgAIp2zagh49XKr/u5EykQlqH53oiQGq2Dah0+FJnL+fCF2wEBZJGQJdhHR5ImKNV+OflEUwvrq7+13l29mRfq/BcExRIlrPPRPovn9svH679kPOtP7uswwPpcnUxke4CTJc7uphIQUCBlDm8HmhtR9cDLRFQIF0Or0iv/M6xLQJaUFAgUQR0KYagQLIcrcILmz1vfV4QlIACWI+TnUjXWTfo/PrI5254twGloMC+OQloeTz9nZflV4f355nHNfhtAspGUCBRbo4DvT2dgXR/b1ekr+iGoAUFBXbO0dWYvjze5RXpK2Pr8BQU2DNnl7P7VF2R/tsVrkh/uClPEP31xmZP1NYBZSsokJLYrgf66blwURLzGjsPKAUFdiyugH59nMnuvDK7A6cBLSgosG9RBfT2vNoVdXHyXbVHyuy8+i0COroXiYICO+YqoB9/uej8ZHcgfXk01NlL4YYPxsdEbR7QwYFMBBTYLzcB/XK+xgWVrwd/+ZfpUfluA1rVk4ICu+UkoL1+WgZUPiP0xPS8UC8BpaDATrk6lTP7/sVN6w+rO1ZdhcT0yiSbBFRb0FM8KSiwV64uJnJP8ZuG4gtovQuJgAI75ehydmucvilfl/kkiFX4iXV4Cgrslfvrgdq7HNTSeGzrPqAMQYH9crQKv0pAy31Rd8Uz6b8+M700yTYBndgISkGBnXK1E2mVz5G7rg6dv3hRfVLy29OR9GbXFnUcUNbhgV1zdT3Qda4B+r53PFR29tRw4twHlHV4YLecHUh/9uRmhTs/vBETevbE9JQmFwFlHR5IhaOdSGscSF87fLp6c3Fx8eTqncUJoRsFlHV4IEnxBdRkUhS2eSQCCqTISUB/vC/7Pp2A5qzDA/sV1eXsJIdPv5qfEuokoKqPliOgwA7FFtCPV1fVkaCnSyuf/d3wz7cKKOvwQIriCujpMKa7n+tLK2eGJ3L6CGgzCKWgwP5EFdDrupr3qksrn65Jb3aVEjcBVX0yEgEF9mfbgJ6OoF9rL3x5KuedF2+PK+//Wp+BVBbV6BwnPwEtuDI9sEsxBbS+mEh5BZFm4HkZxMVECm1Ai247KAUFdmfjgP5YHrK00mFM7RXpb7tLiBwHpQFczq7ERlAgPRFtA23PqBdOrQ/jgsol1uGB9BDQlWgDWrAOD+xVRAFtr0hfXprk1em2QFfh+xtBWYcHdimigLZ7jC67y4Beh7ITqRfQtqBFNwQloMDexBTQ22M4f7i5eZ1l97uxaCCHMekCyjo8sGMxBbQaelanH/3nMZwPrq6eG5+KtGlAC01ACwIK7FNUAT28Pl1H+VV7TpLpIaXbBXRkNzzr8MBORRXQovj0y/3vq8vQ//N0MvwDw4sqOwso6/BAAiILaOfw8ZeLF8bXs9s2oKzDA2mJNqB2CCiA9RDQtfQCyjo8sH8EdC15rh+CElBgl9wGtLwg8oPf1n5EAwQUwHocBfTD4/J4o+vmKCRvNgxofze8UNCi2RBKQYFdcRPQYzmPAf1y7vZTjRW2Dqh+CEpAgf1xEtCynMdqVhktr+r5cO3HnM3PCFSOKIC9cBLQ6+qMyzKd96oz2s0+x2hNPgNKQYG9cRHQ+jp09ZU/TC/huaptAzp6VWUCCuyOi4DWybw+7T8ioAB2wmFAL0+7j9INKAUFdsZdQOtNoMYXkV/VxttAGYICSXG0DTT7ub34seknEa9qy4AO9iIRUGDnXO2Fv/Puf1Rr8Id/ZIYXkV/VpgEdrMSrAkpBgf1wEtDjOnzl4ekrfwPQjQM6ck1QNoICO+TmTKTTOUh3P1cB/cHbFlBXAS2UBS0IKLAzjs6FP3y4+Ond8f9//cuDd2s/oImNAzo1BGUdHtgVLme3LtbhgYQQ0JWxDg+kg+uBrm0yoBQU2AuuB7o2/Tr86acEFNgNrge6OnEIykZQYM+4HujqWIcHUsH1QNc3tg5PQIEd4Xqg69Ouw59+SECBveB6oBvQDUFPPysoKLATXA90A6zDA2ngeqAbGAso6/DAfnA90C1MBZSCArvA9UC3wDo8kASuB7oF1uGBJHA90E3kEwcyUVBgD7ge6CZYhwdSwOXsNsE6PJACAroNXUAZggI74iWgNzs/DrQgoEASXG0DfXvRuP/d7s9EKkbW4flkJGA/3AT0tr4SaOb7gqBBBJSCAjvh7Hqgogf7X4XXr8MzBAV2w9WZSNmDt4+ys//25nGWnfk7EclPQFmHB3bK1bnwD6uT4H9uLq7sSwgBpaDAXji6GlN5IdDrKqOnK4v4EkRAcwIK7IPD64HWn+Wx/4/0OBkJqLxLCUCsHAa0vhDo8bu9Xw+0MjYEpaDALjjaBlpfir4OaAKHMY2dzdlflwcQKSd74S+rbaBdR5MI6NQQtNoISkCBmLk6jKnc7HnaDX/rczd8OAFtf8PdBAFYmbMLKh+jeUznN+/+81EiO5GkgPY+nLMOJ0NQIG6OTuXMmo+VK5Xr854EEdCcgAL74OhiIh8eV5s/H1f9fLr2Q87nMqATQ9DmNxxOEIB1Ob6c3YeLiyd/rP2IBgIKaF6wERSIHBdU3k4+XtCCgAKRI6Db0Qc0L9pxKAEF4kVANzQxBM0JKBA3Z5/K+ctF56ckjgOdHIK2/wUQJzcBfZ/cFekr+UhB824rqMtJArAiJwG9lS9IT0AJKLALjs6Fz7IHv960/B3I5DagUwUt2A8PRM3R5ezKSymHILiAMgQFIubweqAh8BtQoaDtIUwEFIiXw+uBhsBxQEeGoIX4P7cTBWAlDq8HGgLPAVXtRyKgQLRcXc7O3xXsJMEFlCEoEDE3x4F+Oc9+8PdZxgLfAVUMQQkoEC1HZyJ9fZxl395vfJ/IcaAFQ1Bg15yNQFM8kL5gCArsmpOA9vpJQIcBpaBAhFydiXT25CbBM5EUAaWgwH64OhPp57Ufxo7zgM4cgvLZSECMOBNpYzOGoDkFBeLEmUgbm7kOT0CBCDnZBnrNKryqoIUYUAoKxMdJQI9D0LtBHEfvIaCswwP75eY40GNB77xb+4FsBBHQ4WXtGIICUXKyE+nH+/er4z8TPBPJKKAUFIiLq8OYUj2QvphV0Ly+Or37iQOwQIQBPdx8vLq6+vXGZqtquAGloEB8Yvtc+E/PhRA/MN6uGkhAGYICuxBXQMuLOknuvDK7A08BnXFRpvoDktxPHgBrUQX0trooyf2Lk+/Kb87MDjD1EdCZBT39pofJA2DLXUD/vCq3XC65jki5LfXspXDDh3PT7akBB5SCAtFxFdD339Ur3WdPre/5epBL4w9M9hbQ2UNQCgrEw1FAXwvbLW0/3OPwbHhG6G1mdo5T6AFlCArExE1Ar8uh54Orq7fPy62Ylh8wp7qok+mFnrwE9PTBm5MFbX7XxxQCsOHsivT1uPNwHItafsZx3AEdYAgKRM/VFenvqb8xcVyFH6Q3ilX4QjH+nByC0lEgfI6uByqU7zgctbw00+WgluVmUaMaexuBzrk0vTgEFYoKIFTur0hvf336ckvA3d+EG74+M90e4H0VXvxaPwTt3wQgSDEFtNoXlZ1dvCiPKL16ezqS3ugoJq8Bnb8jvv5SmVCaCoQjplX4onjf+3xk46NK/QS00DRUX9Den8l35HjSAehEtBOpdHgjJvTsiWmJvQV0/iVFesnsrcqzWg8ExElAb4+xa4aK77OFH5B0+HT15uLi4snVO4txrL+AFvOGoL1edn8prN/7mHoACm4OpL8sL5z04ubm5u1j+wPpLSZFwdVjD80bgubKT/cQfkBAgWA4+0ykjrvPlwsroMqPl5s1BBX/mt1IQDgcnQt/aE+GP7M9Fb7nz4826/CBBVS/Dq+K5NjPAHjg7nJ2H3+5uLj46ddld/7px+oQqGZf0p2Xk3/RmzifAVXtijcqaH+PEgC/orqg8uF5Vh1DKmwRMBzP+g3owpX4ojk61PVkA1CLKaBVN48Brf5/dhzPml/ayXNAF28GLQgoEJCYAloeDfVfPp/+X52AdPhHHKdyClQFnRvQ7ix555MNQGXbgJ5O21zrY40v624Kh+KbHpXvPaALCpp3h4K6n2wAChEF9Hg31XCz+X/J9LzQMAIqV3S4Ej9+FjwBBUKxcUB/vP/97+V/Zd/bBrQqr3g1kjguqCxRDUE1Y9De33V34H6qAShEtA20ieXh2c4Cql2J7/2dcAfuJxvAUEQBbT9U7rJbhY/jivSyOQUtBp2UrxbqY7oB9EUU0PZjjb+cN3uOyqaG/7HGPcqADi9spw6oamwKwBOXAf30y8XFf/1N++NJ5c6oO+XfXzeHMRl/Ql0IATUoqPg30p96mnIAku0D+uf/uXhRrmU3pw/dsU/obXnk/Pcvbm7+cbybJ6fPSI7hivQ9ioDqNoMKf9P8jwsyAcHYPKDVVUTO/i4cy3RmfznQL/0r0hv2M9yAaq9s1/xJ9z+GoEAotg7oZR26p9fV4PHtj/bH0ZfkK9LHdjGRhjqgYwVtT0IqCCgQjo0DWg4Z77y4el6NPF81tyy6Iv2fby+q40q//+lFZJeza2mGoKqCNn9Q/1n71/4mHkBr44A2p1pedmvb1w4vST8QRkCnVuKlz0EuhJFn99ceJx5AY9uAtodu3na7y5d8KudiIQdUvRn09OuFeAISQ1BgW7PfYJufC9+uuDdbPhd8LvxygQTUbCW+fxU7AgpsK5yAnmIpVJOAFvohaFNQ4RfagDZ/yTo8sDECqhZKQE1W4nv9HJ6kBMCG9m00//1FQP1QB1QuqHDeUVPM7nbP0w/ET/s+IqAa4QdUMQbt9ZOCAqsgoKZCD6hcUOFjPKU/IqDACnTvI4N3FwH1RF9Q1a2KP/E8/UDstJeVMHh/bX8Y04uro7fn9RfVlwRUXhvXFlS6el3zpTgmBWCrPcRleHs4AVUhoPoRqHIzqPIPfM8BEDcCaj5x4QdUeW1Q1e/7ngMgasJpfv0fzH97bXwq58crlV9TP5WzGAuo/nOO5ZL6ngMgarqAGl1xN6aP9FhBFAGd+JA5AgqsQLzIrny7wbuLgPoyGtDxa4NSUGAp+VqR4u0m7y0C6sv4EFR5PH3vLz1OPBC73tXKhdtN3l0E1JexgI5tBs0JKLDcWEBD2QsfnFgCSkGBTQ0+cKz9zui9RUB9mQroyK54AgosNPzI2+YbozcXAfVlPKCzCup7FoBoqQMqvMnmIaDeSC+TUUEJKLDM4Cq7gzfZPATUm4mAlgXVJZSCAotoTu8joOOCC2g+8o+gvqAEFFikH9DuZrO3FgH1pvfiqUI5VVDPcwBEqutn920hvAln3xEB9aZ+nZoXTBlKXUEZggIL9ALanhdv/LYioN60L5V6kDkxBiWggLX2g3LEIajNu4qAeiP9+2dZUL9zAESqH9BCWAlsvp2FgPpTv1hjI8ypgvqeBSBG3UFMQkDF9xQBVQsuoN2LRkEBR7rxZrcOb9VPAupR+5KNDUDz0QNCCShgTtpr29zUfWfwziKg/kx0c1ZBfc8DEJ9mC2ivmQR0UqABrTdoW6zG+54HID69wwYL+34SUI/kaxjU/6OgwMbq3e/te0gV05l3RUA9GlySUB9Q/SH1AIx0a/DSauDp7WT4xiKgHg2viW1eUMW9ARghDEDFYFqNTAioR8IOwPYW64IyHAVmOcWzOZNF83abeV8E1CP5RNzTV+MF1SbU6EUHEtbbB09ADQQZUHk93KKg9doIAQWm9XYhEVADIQY0nx3QsbOSuvsDMEJcgyegZsIKqHw2bnOLXUGLXokBqMwbgBJQpQAD2nuprAtq8qoDyRoZgBbmbyUC6lP9Wg5uMj2cqX9eLwCNyXcYAdULLqCDV2rkwIq2oKrXmoACM8zbAkpA1QIM6PA2+cUdvtRc3g4wMdzLQEDthBdQxW39UyKGAaWgwGzS22LmGjwBVYojoHYHM6k2CEqgn1YAACAASURBVADoBXSemfdNQEPTXtxutKCLX3ggEdI5JrNGnwRUJ4KAznqJRwrqe/KBoOS9gM40894JaHBmrMOPbgb1Pf1ASOSzVQjoIvEEtP2GggL25LOcB+8VAmokhoBK22y0w1E2gwLTqrdDNyTRv2kI6ByxBHTGKgdDUECgXvC7a9cVJvkkoGpxBXT0BdZfVoSCIkHK6rUr6fN3vxPQEVEEdOZrzWZQoKNa8I2S2b6tKjMflYAGqPtn8/SdZjsoQ1Cg0R4/Ld9okU4CqhdNQMVvtGvzFBSoDa+t2wxKJzeHyeVs3kTzENAQCS9f82JqXnhdQCkoktKurkm35RNbw5TpJKB60QW0G08aFtTfxAPuidu72pumzklRlpOAjogtoHm3YqJbAigo0K525eJ7Z2LdfY3LmhHQIMn/no4sCazEA70DV+pbFvWTgCrFFdD2ZRxZFBiCAv119e6YprF8jvWTgCpFFVDhVTReieczjpEQ9b6iYkE/CahSLAEtemvho0NQdUHnLwNA3KQB6KyjP6fySUDVIgqo/BqOLBesxCNxyneH/eZPAqoVVUB7388taNH939fUAw7NHHUa9ZOAKsUU0P4N4oHB0vIysjj4mXjApfYszhX7SUCVognoULdrcWqB6B/QAexZN7aYl9BZ/SSgSnEHVLt4MARFuqStVnPyOaefBFQp4oCOXuSOgiJV8shiahA6t58EVCnugHYvrGKxoKBIkvokJH0/Z+WTgKrFHNBmSam/Pr3KowvGYF8+sDuD8K3STwKqFHVAxassK8agqoWAgmLnprd8dj836CcBVdpDQLVr8QsXBSBCylKqmfSTgCrFHdBCenVnLx2+pxrYjLygr9dPAqq0g4Bqj3mjoEjP/CKa9XPHAT3cfLy6uvr15rPF30YeUOECoYr1FQqK1Jjk06ifew3op+fCZ5g8eGf657EHdOzayhQU+9dbmDfr5z4D+vWx/ClQ2Z1XZnewh4CKL/JwMVm2OABhkxfmuTU0zuc+A3p7Xkbz/sXJd+U3Zz8b3UP0ARUOZZq7oAwujQcET7fAynXbpp8mR//FFNC/Hh2D+VK44cMxqN/8bnIX+wio+PX0olL0/9kGQqdbYoWFupjuZ5E3n7xp1E/FxdC0Ygro9SCXZVIfmtxF/AGVF62Jgso/9zjRgAntEmsSwnz8c4vV7WzfNjOnNKKAHp5lWX+F/TbL7prsjd9BQKcSymnxiF6u2fK0ZTzt3iwRBfQ43Bysr6tuG7OLgE5sSBeXmUL6HwVFFOqFdbjMtjfMjKd+nyoB1d42Zh8BndgSOlxuepuOgJD1Mif/QP4Vi3hK9y0Xc98BPa7Cn73q3ZbkKnxJfI1HCyp8DggFRQy6JbboJXQ0iVI9p3+zr9vBb/RGiSigxeWgluVm0Xsmd7GbgI7/a8x2UDi32rLVLKb1/7vlVlqOlUu97UZPxXV6Zk5sTAH9cn4s6G/CDV+P/RwMSkftKKDygjZclhRLCIczYTtrLVvdQiov4PO2e5qm8/QY3Ve9SZgUU0DL45iOxbx4cVV6ezqS3ugopn0FVHdVkXppGiwdFBTbWWvRku6n/WYyggv2t3fvje5hZ09uVAEt3p/3TuU8e2p2B/sKqHR5puECpVlKpK3y/iYd+1ItXGvcjXQv3UI7bmk/ewPofW4DPTq8ERN69sT0ikx7C2i7hM09HnSwUd7ntGM/Tg1a437qIadww7Slw8/+xO82oEeHT1dvLi4unly9s7ie3f4Cql/AlAuVtIV8+EPATr0tSfNDg7vpVtmLuQfOLxt+KuO/z22gpjIF39O0gdNioFyuVIvLzIXT91whJs3eHs0P5y5NzYKnWBz1S+o6/SSgQ4kEVLtsjRZUvEF1N/5mB9HJ24Gj+oczF6fmF/uL4WgBl62+F/XWW9vlPd6AHj5e/Wq8Er/PgBpdYXn465p7dDoDiFq3UUj1M/3RH/nwVwvhoCLpfwbL+Iii9w5Y2M+IA2p6FmdlrwE1LOjgBtUdup0BRGzsAKAqWpqC9m7N+/+8T28ENV19l/vZToT9wk5A92FkAZuxVKnv0fEsIFojAc2bAZ5qOZMXv1x7XoiWzebP3mHzCw+/iimgf96IPh0D+u74/z9M7mK3AV29oOpbkQjNUqH7ZfXXRdG2U1lQ+aCQ3KqfJr8uLfHCwy5Z0iMKaHn1ZIUkr8Y0tGwhU9+dh9lAGHTLheaX1V8L/RSO/pB/KB4UUmw//OwWeOHRCej8idttQAvt8jdnMRve2cLlClHrVnRn/rL6u7y7H8Wy1n7Z/cCkgbb9lEe+S5fziAJanch5dtH41/Ps7G/H//+U5OXshvQrQBYFHdlxigQY7V/RBjSfjmL7i0Ky25+N/uXEUq3746IdaoweHjBbTAGtrr50p7kcEzuRZLlxQeVflu9r+T/NCJ/uJZZ27EwtBt1Asv99bxFr769fNPm3NOUbLNJmw09pY6sc0UWiCmhR/PM47Pz76UsC2mO8uPX/RrynQv/uwm7oXuLeavjoktD+UOxT8/1w+ZIy1h9zDv7EbIGeUogPLE2MvcgCWnx93FwTlID2mC9wg79p76i5P5/zg81pXuLhnvSRRUHakincogtY/YXxJs/e4mzxV+1kSpso7J+9SmwBLQ7/qC9iR0B7uqVkwTJXKIYU2CndIZCanYq6+xB+JW9vmkhY3o01FcPQCXOX5Vm/tFB0AS2KL8dB6IPPBHSEZqmbu9gJ9+J1NrCtXF3QUwCHvzsaW7FI+iiK96Ve9qYZLci56jiAwQQtEGFAi8Pr4yD0JQEds2DBq9duCoagO6eLiHiUz+DXFb8r3pcULO0wdHjbrAWzYrL5s53AXBgVqEbY9mIM6OmApr+dE9ARmmVvcpHr/ssQdOeEzMg3CwuP4g/6vyzeVd4tOOMjP1s2e9/FBVmI6jriDGh1QFNGQMcsX/wYgu5anZfBayy++oOf9m6Qtrn3j1mav6TNlU0uwMK/Cr0fdBO86kIdaUCrA5oI6CjdEji22MlLHgHds+6VV9/cfiP/gvhtLoWy2GTM2dHms5tS/RQ0E1wQ0NrXX8xOQqqkHlDDI0Ao6I51L3E/kO0+oO7XVDUS97dXK/6a317HyOhzfDq7r6RDmFYSb0CtEFDjY+h8zwa20b2+0otc3TRzodh+0NmaXnkXFXImc3GCcwK6QEoBHSmo2TVosUen1zbvNaX5ftZC0fyt0V50C9nMfPY2gCpmuVh7AEpAd2xseTRYfPv36mVesLK6OEXTnfbWmWPKQtvPtWM6M59FO+HtBA3nefV+EtA9G1smDZbg/h36myGspi5N3v6/GD17aGzvzKYD0Kl6Ko4p7Q7C689yQUAXIqDdYjl7GR7cmd+5wgoUFZrYgT38KwdmHLekOl6pvb03y+v3k4Du2cSSOXcpHtyP7/nCYnNfe+n1NvqjFcw67HNwFH9RCCNqaZYLArpUWgEdXeIXfZy2+tEczx3sdQGa/YJrbrdfiCbM2fQpHIvVP/Sq/r8wx8UG/SSguza5gJou1PoTqDltKSamr7t7s/YcFeJgsztquVtXz8Xhc0FAF0ssoOsWtH9/w4fyMo8wZvKqe5DNPm6pEKKZN1+Kh3tKt23QTwK6b2stqd3iKGV08FBeZhKmxFd11ovvTPdhkRO/2O0o6k5zF1vZFbT7YU5AlyKguuXVbKe89I3wSN5mFAY0r6V78mObLIvdEavDYaXYzC6nxSYDUAK6c3MW48wwo6f77XZEiI/jdWYxi/BCBjMCNfx3vDvuaphFYUhaz+2G/SSgO2e+BM876yMfjl5YiY+D/Co6MbpEWawEVaR9RtLsdV82NxDQlSQb0MJ4MDq+8PY23ufyYSQIl1mhlsvmMbpPaeVHdbin9IP6N7dZNgnozglLmuEiP74Ii9vsez9BqOYuAatZv52ibrbEWZTnt/sfAV1BygE1X/THl95cPa5VToTTWUafOj5mC4SxxX2cIs6f6suxH6yGgO6d7eI5p6DKO1dPgYc5R039Ko28rostH11Ok2Zw+FXv+5yAroGAGpj3Bpgcg2puhjNiX7Sv2mpcxDPvLU69Pe/iD3L1D9ZCQPeuW+CMl9EZ7wLVvSoffvCD0w9dPQspU/RzM9vEs5D/PxxONjcMF6hcOp5+fQR09+qFTvxmNru3Qv+xu2V/OGmunoWEtS+KxUtpYquRZ1FI6axv68+j+L/e3Kt/sA4CunuDw+RMjL0h9FvR+o/VnTcymBZXz0K6DF9xYyvtUtcqxNM2tAfL6TOpOth+PQR0//pHdhixe1O0D9UdhNcOgXqT4vSpSJH5q1e97tO/sd7xSGMK4YD46vtcm0lNJ3PF6Z6rIaD7N2iWGcuCtv9tH7mZAHliHD8ZybF6wY2YP0J/YRn7WdEtwOJtivnUZVL7J2sgoPvXW/6KweI5wfpN0juvThiXio/r+ulITPtCzOE2nHMUip1gmvnUjjN1f7IGApoA4R1Ufy8tnpNs3y3ykis9evO/LZdtFMann7kr4zynWZDnRj+nLp7QHgKaAGlxlG6Ya8EgtHq85lEH0zPyhsAaZr6427Vz0V6s0xz05kc/p5s/m0MENAHC0ijfMn8hX/rmah5Venz5Niyk3Dc9/bKGN+zsnObB9RNpgoAmoFkUezcJi2k+fW7K0vdY/ajS4xe5tqBBv2uC1H+Vp1+SoNtZOs2Gn6dzJgKaKGkxzV0NQnNhLNo9uHryXD4beyA8zfPWmoNsZ38XJwENCgFtWCzb9htCuzeE8N/mtlzxFslVN2JMPliJKEZf5RDzKcxL823oywEBTZXNAr7CIDQX3+dtSYeTFvw7JzBtb7r2CKdAKl9Jp/20WN6aWQgaAU2Wzdtgi7fd4E3S3Bj4WycoimdVdeuWr+Ncc5e8eg58PqszENB0KZZX1Q9kGxxM3WtlNO+dkBg94SGvvXfzU//f/XNphIAmTL8QV9+pl/T1T02RR5vd18G/ecJh8nQ7XnufO22a+XL8RBojoCnrllzlcjy91C9paPcI4j4jYRJ076pQuZlazTEL7fPoY+dR0f96bAqK4fTpD2YLfgkgoCnL++vOwjLe7Y+Y+zYySqj8eO2E5L1xsLOnYjk3U5srHmbwqun7tW4+FQ/TTqDi58NJ7m7PVc9eBK8/AYVI+XaY/45asipfqI4FN5r2VZ8JY/2J3+xBdGdFyN9pXx67V0fzkslfyZMn/8ZwR6H8k8GvFAQ0PAR0DmmJN3xP2SW0e/v1p8Roqtd7Csy5OfKqTZV0mxSo8dfG9JWZ+wIOVmCK/iB0MCO9Hyh/J3gEFBOM30zmW0M1bzKjKKr+3CXF8VjbPIi46br9Th5/Kl+zOS/K1IstPqD8y8JPpOkdeWmbg4LF31bMbdgIKCZNve0GbPfKqx56/kRavOHWe4c2g7DV7lD9IOJqgfBlPtgnp3xJ5rwCmtsHj6J47RQvov6lbe9MNX/F4EehIqCYNv3GG7BKqPKh506iRb+M/2D88bcuaC6P1qTxYDH+Kk2/GM2/QMNbeyNN+f/DXg8nWv3SKgIqr0YQ0PAQUEvjbz41i4YqH3jm9BXGbznNQ9roxoPr3N/ogzTfCRkrlh66pIin8G+S/IvSI/cLqphqzbOs2ugh/G4U/SSgmEf5ZpliltAFh7I0f2n0ptO94224eNurYtM9bxOvwoznfnij+A9D/0e6G5QTrgmoaqtLnrv4t2g1BBQzTbwFNQyHocrHnTNpvS9M5mf2X4zd12BSVqe459Pk5+MDUNt974P5kn4m/k7zpemTqf79wUMHjYBiJvmNMp/5WUqDx52etOFXM2fH9E0/NQGbFVR1vzOffbPXS3xSehtdu5/Jz5vtU6n+g+YRzO7LEwKKuZr3iPmb0eZcT/FxJyfM4JelebF6249PwErvfMW+mPYL7VOmfuZnP+WDJ6O/izxvwjl81qyeSc3vr/OiuEFAMdust6zW3IoO34sTb6be787cZqqLho3+3p2Fd1co1m7lfe7ChE895XNfnrx9BP18SdtbFVO84jENBDRIBHQJg/fi2Ft6sqLNYwmPO2sihr+sn5E1x6DDwdiiu1NtnpV3hre/tFI/NZsgdLvIlU/Y8qdRvrfV7mpbBBTzzXo3Tpu9Ri887rxJOP3GcLoHs7HqGNRiq+3E/XVTKNyjPLV5/zbVszz+bKmfspENyt1jqqd60VzHiYDCQPOmE969tlWdTKj4RhW+GL6ppSkr1O/7/rfqkMx6ChRPyvQtJnJ5z3p/isXx6cTzO+NlaB5QMfXqYaZ29drgOdwRAgoDI29BK6YFHb5JhxMw+KVuMtu/kB4i7/3C6bf0z8DwJsVvaZ/CKadJUT6xBk/z2BM72Emj2QShmgnVU5U0AgoT89/Ca7zTpX3PzV6M/vTMnayid095v0i9mRyZ/95NzReDyZp+OpUPUN1n3mZMfDZmmt5Tp5oB+QbGmXMQUBhRpWaRyYK2w6Hqa/kNnHdruivozZJ29pX9kW/W/Pnw3oZ3XwiRFx5zq3xqN0FQyjkIKIwM3n/Gb2/L97vwKOKD6/MpTN1MgzV75dz3Ii5XTvzNWc9l/9+Dtp/id+qp1Jl4PodToZow+jkPAcUyU2/nGWYVNBfGoOKW0dG/s5kacZ6Usyr/KyL9gfTLc5858YZutnq/MXuODPOpLj0BnYuAYiH5bW1XVKOj69sOGD6IzZRJs5m3m2W7H/XmWHpeJp836Y+E2ZK37RrN6vhuOeVk6F5UzEBAsVRum82573upAAsfyFQ3j6fvxMk4/UCzJ2qqQf0/av84X7BBZPoEheFk6F9TTCOgWCxfoaBbfmDPMorNvPVMK+e697SMPGfSX+X9Vtsw7ydr6gsRUCwnvfvr/xkPFy0L6npIenrQZl4V0yA+LWPPmPI+l/Rz6glUT8fiFz9tBBQraN+e4gY943FpsIPQIWEm+3MpPS0jz5fyTm2nZ8ZpXZoJWeX1TxcBxRqE/R/NDYq2zMmAbUJcK9pxtupH7ZOgebZWHTgbXypQNa2wQkCxCvEAxvqG0/vTvAU2Hdia4QGlwpNQ/a//XK3Xz6l4Tr0AThaOHSOgWEXeP8BcUdDuZv27OtSCmhGeBOGZkJ6YFcy5MmD9yJRyIwQU65Aj0d4iFlS6fTQLi7oSgt6T0N682gMYXOK/oJ/bIaBYST54l/ai0f/BWBsWpMUjxZFOU79nZe7VVIUpcbYYJIaAYjuDpHQ/GO2Is4Su/SgOjqmaPfIsxK98LQG7R0Cxobw7f33wk3yioNtGdO5F8cNiMM3d1s/6C2yBgGJDubQi2f/RnFSs3rdMYe3HGGj/BVlyJ8YTK74I2AYBxZZy4coYg5/MLMZagVNHM5aBqPlUun+1E0RAsSnVG9m8GwsLNzHcDL+hFtPn4bVOEQHFtuT38uAdXn85Ix9mCTFdU5/9AO4za5N3Ly91iggoNpYPN//1fz7nhCVVEWdbIVQWd7gGu8fz8TqniYBia5Nv7ep2g5qs10zdA8x7SKuHsJge079y/QKnjIBic5Pv65Wzs8isXK6V0RlbFYzv093rCgKKMFgnaBPz+riwohuNo32/komJMKCHm49XV1e/3ny2+FsCGiyLBAXBrnZr1HP8evhwIraAfnouLGAP3pn+OQENmEm2AmNU0TXW/XOhn8Jz5/slTE9cAf36uPdv9J1XZndAQIO2MCmezaroSvUUhp/dc+f1tUtUVAG9PS8XvfsXJ9+V35z9bHQPBDRwS7syNz+b3fPYKvha8cx7w0/4E1NA/3p0DOZL4YYPx6B+87vJXRDQ0K0Ql3kF2vLOVRVdsZ4C3y9X8mIK6HXWz2WZ1Icmd0FAg7duYFTJ2foRGsZ7hcz5frEQUUAPz7Ksv8J+m2V3TfbGE9DgDRqxenXUn365UVc3qKfiCtXwJaKAHoebg/V11W1jCGj4ulAUw6/0NZnXHmHTodEfbmvmtPSeIJ8vEmoEFKFRdsOwSPJn2anuSvdoAZOm1/3rAoWIAnpchT971buNVfg9Glajf+Oc0owdZt59OwhTJFy+HtCLKKDF5aCW5WbReyZ3QUCjkPfWapsbV4zO8Na8/6gBc/x6QCumgH45Pxb0N+GGr8d+DgalowhoHLpOCNGw6cxYdQa3Dh6oLbkm3yv11vgfB3cvBCbEFNDyOKZjMS9eXJXeno6kNzqKiYDGQspErx3S/5X7g3p3NDs7zS/2/i8/WNEOVovhWNmCYvo18zN7PuBMVAEt3p/3Dqw7e2p2BwQ0WhNJ0UWnGNww41HkMDY/kT7nsvmoYOPxo2Jm5vyFMH3LnkesKq6AFoc3YkLPnphekYmARk3VlPYmRZ7E3zB7iGI43hscV9XdPD+IgzkZ/+3hhNPPsEQW0KPDp6s3FxcXT67eWVzPjoDGTTEmK8QaqW8z6Gf7l8OHau+qWXUXbp5dxMGcyDdLD62aVQQmvoAaGJ5LR0Ajp43KeFiNQqT7K7l/4q9LfzXSUs3D6KaZfoaPgCIqxlWxCZGmXuIauPzb7R8V0uZT9YZb6TGGP6SfMdl1QIcIaOzcVEVdr/a24ahx8M3Y+LN34+B36Gc8Igvo4c2P9//2b93GT07lhENd4Pq358Mvlf3Uji7l36SfsYgroP887+19J6BwqbcDSbhdNTbtipjLq/Mj98/6e1yiCuh1uyWzOaWTgMKlenio+kH3c/n3devxow9CP2MRU0DLUznvvLy5eV3+/5RNAgqnuiPohz9QjU0ZUe5cTAG9bkae5WfLnQpKQOGWroTNirryD4jnbkUUUOGK9OWXVUsJKNzSxlDfSQK6YxEFVIxlcx07AopQkMkURRrQ5uPkCCgAf2INaLlH6ewVAQXgUUQB7X0q522WffObeUABYD1rd27TvfD35G+/+Q8CCsCjtTO37XGgD/7ovr+spt8ooD2pr9Ez/76nwC/m3/cUKGx8JpLYy9cEdBnm3/cU+MX8+54ChY0/0kPqZfkRHwTUHvPvewr8Yv59T4HCtldj+vCTdB36w+tzAmqP+fc9BX4x/76nQCHEadIJ8gl0iPn3PQV+Mf++p0AhxGnSCfIJdIj59z0FfjH/vqdAIcRp0gnyCXSI+fc9BX4x/76nQCHEadIJ8gl0iPn3PQV+Mf++p0AhxGnSCfIJdIj59z0FfjH/vqdAIcRp0gnyCXSI+fc9BX4x/76nQCHEadIJ8gl0iPn3PQV+Mf++p0AhxGnSCfIJdIj59z0FfjH/vqdAIcRp0gnyCXSI+fc9BX4x/76nQCHEadIJ8gl0iPn3PQV+Mf++p0AhxGnSCfIJdIj59z0FfjH/vqdAIcRp0gnyCXSI+fc9BX4x/76nQCHEadIJ8gl0iPn3PQV+Mf++p0AhxGnSCfIJdIj59z0FfjH/vqdAIcRp0gnyCXSI+fc9BX4x/76nQCHEaQKAKBBQALBEQAHAEgEFAEsEFAAsEVAAsERAAcASAQUASwQUACwRUACwREABwBIBBQBLBBQALBFQALBEQAHAEgEFAEsEFAAsEVAAsERAAcASAQUASwQUACwRUACwREABwBIBBQBLBBQALBFQALBEQAHAUjQB/fr8PMvOHvzmezo8+OvRN7933yX1RBze3M+y7FtxbpOa/+LDj+X8P/nc3ZLW/Fe+nGc/t98ENv+xBPT98Vkrnf3d95Q4d3iWCQFN6oloZjbLfujflMT8H1/6em7bgCQ1/yd/PcqyYOc/koDeZq2fp397Vw6XmRDQpJ4IYWaze4Ob9j//bT+7uU1q/muXwswGN/9xBLT8N+jOcdD+6bEYkyRUb6J2npN6IqqZfVec5vbsVXtTKvNfXB9nu1x7/8/j3N6t1uLTmv+TW6GW4c1/HAG9bhagMicPfU+NSx+qNZZ2WUnqibhtx53l3FZfJjX/x3k8/bNRduP0VVLzf1I2sw1oePMfRUC7Jancnnz38+gv78nX4z+02YPHbUDTeiIuu9W0em7Tmv/jLNb/gJThKJ+KtOa/Uu4B+J/NghDg/EcR0OM/Qs2TJTyFCSjX4Z4KO5GSfSLqGU92/ut/SxKc//KfjusmoAHOfxQB7dblpGHJ/l2f/fBZ3Auf7BNRv3NSnf/j7FeLQHrzfxxoPizagAY4/7EEtN3ecR3G8+bGn+U/t3JA03wi6ndOmvN/eHNez2ty839c9o//cIoBDW3+owio+FzdBrLx2B0hoKk+Ec1OlBTn/7I66vFp9XVy839Zve7tbAc4/wQ0eAS0PBS22Qmf3PxXAf32abXpL7X5r+eRgC4U4PPmUPIB7U4lSHD+D//9/o/loWzVzpPE5r/ZZ0RAFwrweXMo9YCWh/zVO1yTnP/idDhbOQRPbP4v+/9uBjj/BDR4iQf0a3saUprzX6k3Aqc1/8NuBjj/UQQ0wJ1vDqW9F768eMSd5kSsBOe/dl3NeVLz351HwF74hQI8/MuhpI8DvS6vxNSecZLe/Ddum4CmM//XmSzM+Y8ioAGegOBQymcivc6kFbXk5r91CmhS868IaIDzH0VAAzwF1iEhoKk9EdeZ/C5Ja/6vhX88TuOtpOZfEdAA5z+KgAZ4ERaHxAsqp/VEHIdd38hXHk9q/o+NEPaeVctAUvPf6jZ3hjf/cQS0uTJkOJcBdEgMaFJPRHsRN+mmdOa/On6rPIL+6/NmS0ZS89/qAhre/McR0PAuRO2Q9JEeKT0R8jrcqaUpzX/x9VE3tylekb8h7HAPbv4jCWjxz8A+CsUh+TOR0nkixA+0aAOa0PwX9fVgK+2RCEnNf008Yim0+Y8loKF9GJ9DckDTeSL+eqQKaDrzXzl9Kme6n0pakQ75DGz+owkoAISGgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlojN1TQAAA8lJREFUAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAopwXWfZN78L3385z7KfvU0NMEBAEa6/HmXZve7bwzPpW8A7AoqA3WbikLM/IAV8I6AI2aXQTFbgERwCipAJK/HlCvxDv1MD9BBQBO24En/2qvrquAJ/97PfiQF6CCjCdll38zgWrUt6dHh/P8uy71+2Qf3zTXnD2fcvmz+6+/nDd8ff+K353W+f/OF6ypEAAoqwlSvx5Zr7pbACX24Nrdz57XTDddY4DVKPAX1ffndMbvu7rP9jfQQUgTutxN8KK/BdE+s9TF0/60xeZt9Wv3Ov6m+DPVBYGwFF6I5jz3uHZ90KfNnEOy/LdfPz0x6m8oYfyjX0T4/rzF6WY9FqdHps6513x/9/fcYmVKyPgCJ0ZR/vC2vg3WC03i562+6pP45NqzHpZfsrl83A85hgDiLF2ggognfbbdwsXXaD0dvels1jUZuA1rdfN0NRYAMEFOG7zIQ98MdIiltDu3M7//z4y3nWBrTe4FnF9+xv/8baO7ZAQBE+qZPibqFmZHr48GOzY6kOaBvcy/r2M45jwvoIKMInBVTYB98EVLypH9DDG45jwmYIKMLXD2hvd/ppTPrt9z+9+H+PBgE9+vCcgmIbBBTh66/C93anX7dH1P+lDOjRn2+fZxzHhNURUIRPCmh5URHpkHjhGNHb/iq8cPDSMLzAUgQU4ZMCWl0WtDuF86EQ0K+PBttAuwOaCCjWR0ARPjmg5SbPs6fHtfE/X58Ob7o8rcKfdhdV5bwUx6TdWUpczh4rI6AInxzQ07Gdwn4h8ft+QNvDmDIuZ4/1EVCErxfQ4rZ3ZNLr9ljPevuoeBjTs+Z377wqgHURUISvH9Dj2rp8jc8PP1bffm5Oi5f2wn96XvZWuHgosBYCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCWCCgAWCKgAGCJgAKAJQIKAJYIKABYIqAAYImAAoAlAgoAlggoAFgioABgiYACgCUCCgCW/j8cOItg7SXS6QAAAABJRU5ErkJggg==" width="672" /></p>
<p>All the biomass indices obtained above can be computed at other time of the year changing tsampling=0 by other value between 0 and 1. For example, we can obtain the biomass index at other time of the year changing the tsampling parameter by 0.3.</p>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1;CV_A<-0
IA=Sampling_Survey(Pop.Mod=Pop.Mod,type="biomass",q_A=q_A,gamma=gamma,CV_A=CV_A,tsampling=0.3)
IA$biomass</code></pre>
<pre><code>## , , 1
##
## 1980 1981 1982 1983 1984 1985 1986 1987
## 118.0875 97.84868 81.80599 69.44149 59.98934 52.73152 47.08135 42.59976
## 1988 1989 1990 1991 1992 1993 1994 1995
## 38.98239 36.0117 33.53371 31.43857 29.64653 28.09846 26.74958 25.56538
## 1996 1997 1998 1999 2000 2001 2002 2003 2004
## 24.51877 23.58821 22.7564 22.00926 21.33524 20.72478 20.16987 19.6638 19.20085
## 2005 2006 2007 2008 2009 2010 2011 2012
## 19.31746 20.78276 22.31754 23.88186 25.45302 27.01438 28.55331 30.05954
## 2013 2014 2015 2016 2017 2018 2019 2020
## 31.52315 32.93606 34.2917 35.58487 36.81173 37.96972 39.0575 40.07481
##
## , , 2
##
## 1980 1981 1982 1983 1984 1985 1986 1987 1988
## 118.8789 99.30802 80.55472 66.43145 58.17186 52.4397 48.13391 42.19206 37.5958
## 1989 1990 1991 1992 1993 1994 1995 1996
## 35.19738 33.32158 31.19936 30.21693 28.8968 27.05806 24.47819 23.93794
## 1997 1998 1999 2000 2001 2002 2003 2004
## 23.97647 24.61172 24.74559 23.44322 23.66975 23.04633 22.40386 23.65858
## 2005 2006 2007 2008 2009 2010 2011 2012 2013
## 23.85328 25.5388 27.12171 30.38908 32.23875 33.20099 32.94197 33.79131 34.6986
## 2014 2015 2016 2017 2018 2019 2020
## 34.93348 36.1715 38.91363 41.75499 43.87309 47.18234 47.97059</code></pre>
<p>Note that when type=“biomass” the abundance index is also reported, and you can access it as follows.</p>
<pre class="r"><code>#IA$abundance</code></pre>
</div>
<div id="length-sample" class="section level4">
<h4>Length sample</h4>
<p>First of all we need to specify the values of the parameter <em>par</em> not required for the previous indices but important for obtaining the length sample. In this case we fix it as follows.</p>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1;CV_A<-0
par=c(0.2,1000,3000)</code></pre>
<p>First we compute a stock sample length at tsampling=0.</p>
<pre class="r"><code>our.sample1<-Sampling_Survey(Pop.Mod=Pop.Mod,type="LengthS",q_A=q_A,gamma=gamma,CV_A=CV_A,par=par,tsampling=0)</code></pre>
<pre><code>## It is working, please give us a while :)</code></pre>
<p>Second we compute the stock sample length at tsampling=0.4.</p>
<pre class="r"><code>our.sample2<-Sampling_Survey(Pop.Mod=Pop.Mod,type="LengthS",q_A=q_A,gamma=gamma,CV_A=CV_A,par=par,tsampling=0.4)</code></pre>
<pre><code>## It is working, please give us a while :)</code></pre>
<p>Now, we plot the density of both samples (in black for tsampling=0 and red for tsampling=0.4) and we verify that the performance is the expected one. Note that the plot refers to the first iteration and last year.</p>
<pre class="r"><code>plot(density(our.sample1$length[,41,1]), xlab="Length", main="Densities of length samples")
lines(density(our.sample2$length[,41,1]),col="red")</code></pre>
<p><img 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" width="672" /></p>
<p>The same plot can be done also for the stochastic iteration.</p>
<pre class="r"><code>plot(density(our.sample1$length[,41,2]), xlab="Length", main="Densities of length samples")
lines(density(our.sample2$length[,41,2]),col="red")</code></pre>
<p><img 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" width="672" /></p>
<p>As you can see above, the stock length sample can be access using:</p>
<pre class="r"><code>#our.sample2$length</code></pre>
<p>Note that when type=“LengthS”, the abundance index is also reported and can be access using:</p>
<pre class="r"><code>#our.sample2$abundance</code></pre>
</div>
</div>
<div id="sampling_catch" class="section level3">
<h3><span style="color:blue">Sampling_Catch</span></h3>
<p>The function <strong>Sampling_Catch</strong> which are going to describe has a similar structure as the previous one but instead of focus on the stock the aim are the catches.</p>
<p>The function returns the catch numbers for each year, age and iteration, the catch weight for each year and iteration, and a sample of the catch length (for each year and iteration) derived from the corresponding length distribution.</p>
<p>The arguments of this function are described above:</p>
<ul>
<li><em>Pop.Mod</em> is a list containing the components returned by Population.Modeling function (main function).</li>
<li><em>type</em> is an indicator of which element must be computed: catch weight (type=“catch weight”), catch numbers (type=“catch numbers”), or length catch sample (type=“LengthC”).</li>
<li><em>CV_CN</em> is the coefficient of variation associated to the catch numbers. Default value 0.</li>
<li><em>par</em> is a vector containing the required parameters if type=“LengthC”.
<ul>
<li><p><em>CV</em> is the coefficient of variation associated to the log-normal distribution used to compute the catch length distribution from which the length sample is obtained (a deep explanation below).</p></li>
<li><p><em>RF.value</em> is the number of values generated for each age (given a year and an iteration) from the log-normal distribution used to obtain the catch length distribution from which the length sample is obtained (a deep explanation below). By default RF.value=1000.</p></li>
<li><p><em>sample.size</em> is the size of the desired sample.</p></li>
</ul></li>
</ul>
<p>The <strong>result</strong> is an array containing the catch numbers for each year, age, and iteration. If type=“catch weight” an array containing the catch weight for each year and iteration is also reported. If type=“LengthC” an array containing in each column (year) the corresponding length sample for each iteration (third dimension) is also provided.</p>
<p>The important point is: <strong>How such results are obtained?</strong> Below the corresponding details are provided.</p>
<p>Let’s start explaining the catch numbers.</p>
<p><strong>CATCH NUMBERS</strong></p>
<p>When <span class="math inline">\({CV}_{CN}=0\)</span>, the catch numbers are equal to the matrix <span class="math inline">\(C_N\)</span> reported by the Population.Modeling function (main function). Whereas if <span class="math inline">\({CV}_{CN}\)</span>>0, then the catch number for year t and age i (<span class="math inline">\({C_{N,S}}_{it}\)</span>) is generated from a log normal distribution center in <span class="math inline">\({C_N}_{it}\)</span> and variability determined for the corresponding <span class="math inline">\({CV}_{CN}\)</span>.</p>
<p><strong>CATCH WEIGHT</strong></p>
<p>Once the matrix of catch numbers <span class="math inline">\({C_{N,S}}\)</span> is computed, we can continue computing the catch weight.</p>
<p>Firstly, we compute the WC (third dimensional array containing the weight corresponding to the catch length for each age, year and iteration) at time tc (see Population.Modeling function). Then, we define <span class="math inline">\(C_W\)</span> (third dimensional array containing the catch weight for each age, year and iteration) multiplying the catch numbers (<span class="math inline">\(C_{N,S}\)</span>) by the weight WC. Finally, the catch weight for each year is the sum by columns of matrix <span class="math inline">\(C_W\)</span>.</p>
<p><strong>SAMPLE LENGTH</strong></p>
<p>The procedure to obtain a catch sample length needs a detailed explanation.</p>
<p>A catch length sample is obtained generating random values from the computed <strong>catch length distribution</strong>.</p>
<p>Hence, the question is: <strong>How the catch distribution length is computed?</strong></p>
<p>The catch distribution length from which the sampling procedure is carried out is computed as follows. It is computed generating for each age, year and iteration RF.value random values from a log-normal distribution centered in the corresponding catch length (LC matrix) and whose variability comes from the given CV. The distribution obtained for each age (given a year and an iteration) is scaled using the corresponding catch numbers (<span class="math inline">\(C_{N,S}\)</span>).</p>
<p>Remember that the matrix LC (catch length) is computed at the time instant tc (see Population.Modeling function).</p>
</div>
<div id="practical-use-of-sampling_catch" class="section level3">
<h3>PRACTICAL USE OF Sampling_Catch</h3>
<div id="catch-numbers" class="section level4">
<h4>CATCH NUMBERS</h4>
<p>First, we check that if <span class="math inline">\({CV}_{CN}=0\)</span> then the catch numbers reported by Sampling_Catch function are equal to the matrix <span class="math inline">\(C_N\)</span> reported by the main function (Population.Modeling function).</p>
<pre class="r"><code>IC=Sampling_Catch(Pop.Mod=Pop.Mod,type="catch numbers",CV_CN=0)</code></pre>
<p>We check such correspondence at last year and first iteration, as an example.</p>
<pre class="r"><code>cbind(Pop.Mod$Matrices$C_N[,41,1],IC[,41,1])</code></pre>
<pre><code>## [,1] [,2]
## 0 366.9999916 366.9999916
## 1 143.3866083 143.3866083
## 2 73.5578386 73.5578386
## 3 41.6586892 41.6586892
## 4 25.4424956 25.4424956
## 5 15.9265909 15.9265909
## 6 10.1788806 10.1788806
## 7 6.6636281 6.6636281
## 8 4.3495234 4.3495234
## 9 2.8300473 2.8300473
## 10 1.8351760 1.8351760
## 11 1.1857285 1.1857285
## 12 0.7632225 0.7632225
## 13 0.4894269 0.4894269
## 14 0.3128875 0.3128875
## 15 0.5325618 0.5325618</code></pre>
<p>Now, taking <span class="math inline">\({CV}_{CN}\)</span>=0.2 we can compare using the following code the catch numbers for the first iteration and last year obtained above using <span class="math inline">\({CV}_{CN}\)</span>=0 (black color) with 100 replications computed using <span class="math inline">\({CV}_{CN}\)</span>=0.2 (red color).</p>
<pre class="r"><code>IC=list()
for (i in 1:100){
IC[[i]]=Sampling_Catch(Pop.Mod=Pop.Mod,type="catch numbers",CV_CN=0.2)
}
plot(1:16,Pop.Mod$Matrices$C_N[,41,1],type="l",xlab = "Ages",ylab="Catch numbers")
for (i in 1:100){
lines(1:16,IC[[i]][,41,1],col="red")
}
lines(1:16,Pop.Mod$Matrices$C_N[,41,1])</code></pre>
<p><img 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" width="672" /></p>
<p>We can also compare the catch numbers using <span class="math inline">\({CV}_{CN}\)</span>=0 (black color) with the mean of the above 100 replications using <span class="math inline">\({CV}_{CN}\)</span>=0.2 (red color).</p>
<pre class="r"><code>a=matrix(0,ncol=100,nrow=16)
for (i in 1:100){
a[,i]=IC[[i]][,41,1]
}
amean=rowMeans(a)
plot(1:16,Pop.Mod$Matrices$C_N[,41,1],type="l",xlab = "Ages",ylab="Catch numbers")
lines(1:16,amean,col="red")</code></pre>
<p><img 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" width="672" /></p>
<p>The above plot focus on the deterministic iteration (iter=1) but the plot can be done also for the second iteration (stochastic).</p>
<pre class="r"><code>plot(1:16,Pop.Mod$Matrices$C_N[,41,2],type="l",xlab = "Ages",ylab="Catch numbers")
for (i in 1:100){
lines(1:16,IC[[i]][,41,2],col="red")
}
lines(1:16,Pop.Mod$Matrices$C_N[,41,2])</code></pre>
<p><img 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" width="672" /></p>
</div>
<div id="catch-weight" class="section level4">
<h4>CATCH WEIGHT</h4>
<p>The first checking is to verify, as an example, that the catch weight when <span class="math inline">\({CV}_{CN}\)</span>=0 matches the weight of the catches reported by Sum.Pop.Mod function.</p>
<pre class="r"><code>IC=Sampling_Catch(Pop.Mod=Pop.Mod,type="catch weight",CV_CN=0)</code></pre>
<p>The above comparison is done for the first iteration.</p>
<pre class="r"><code>cbind(IC$weight[,,1],Sum.Pop.Mod(Pop.Mod,c("C"))$C[,,1])</code></pre>
<pre><code>## [,1] [,2]
## 1980 21.907582 21.907582
## 1981 18.179029 18.179029
## 1982 15.246326 15.246326
## 1983 12.993464 12.993464
## 1984 11.270956 11.270956
## 1985 9.944957 9.944957
## 1986 8.908261 8.908261
## 1987 8.082617 8.082617
## 1988 7.413446 7.413446
## 1989 6.861843 6.861843
## 1990 6.400228 6.400228
## 1991 6.008846 6.008846
## 1992 5.673287 5.673287
## 1993 5.382814 5.382814
## 1994 5.129260 5.129260
## 1995 4.906306 4.906306
## 1996 4.708977 4.708977
## 1997 4.533306 4.533306
## 1998 4.376096 4.376096
## 1999 4.234742 4.234742
## 2000 4.107103 4.107103
## 2001 3.991399 3.991399
## 2002 3.886143 3.886143
## 2003 3.790079 3.790079
## 2004 3.702145 3.702145
## 2005 1.889922 1.889922
## 2006 2.032783 2.032783
## 2007 2.181310 2.181310
## 2008 2.332083 2.332083
## 2009 2.483111 2.483111
## 2010 2.632889 2.632889
## 2011 2.780281 2.780281
## 2012 2.924298 2.924298
## 2013 3.064027 3.064027
## 2014 3.198724 3.198724
## 2015 3.327787 3.327787
## 2016 3.450746 3.450746
## 2017 3.567260 3.567260
## 2018 3.677112 3.677112
## 2019 3.780198 3.780198
## 2020 3.876512 3.876512</code></pre>
<p>Now, taking <span class="math inline">\({CV}_{CN}\)</span>=0.2 we can compare using the following code the catch weight for the first iteration obtained above using <span class="math inline">\({CV}_{CN}\)</span>=0 (black color) with 100 replications computed using <span class="math inline">\({CV}_{CN}\)</span>=0.2 (red color).</p>
<pre class="r"><code>IC=list()
for (i in 1:100){
IC[[i]]=Sampling_Catch(Pop.Mod=Pop.Mod,type="catch weight",CV_CN=0.2)
}
plot(1:41,Sum.Pop.Mod(Pop.Mod,c("C"))$C[,,1],type="l",xlab = "years", ylab="Catch weight")
for (i in 1:100){
lines(1:41,IC[[i]]$weight[,,1],col="red")
}
lines(1:41,Sum.Pop.Mod(Pop.Mod,c("C"))$C[,,1])</code></pre>
<p><img 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" width="672" /></p>
<p>We can also compare the catch weight using <span class="math inline">\({CV}_{CN}\)</span>=0 (black color) with the mean of the above 100 replications using <span class="math inline">\({CV}_{CN}\)</span>=0.2 (red color).</p>
<pre class="r"><code>a=matrix(0,ncol=100,nrow=41)
for (i in 1:100){
a[,i]=IC[[i]]$weight[,,1]
}
amean=rowMeans(a)
plot(1:41,Sum.Pop.Mod(Pop.Mod,c("C"))$C[,,1],type="l",xlab = "years", ylab="Catch weight")
lines(1:41,amean,col="red")</code></pre>
<p><img 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sMR0BTvqqAwSqbwKd5VQGGUUp1E+nwV0ExPhJ/LFFAFhVFKEtDzxTX0XUCnP27sMqZLu6AwUkkC2l37efRtF9CL/5g0dyH97H0VFEYozZ1IG2vSN3Yr5/x9BRRGKNG98N0+6EJri4ks3lhBYXySLWf37lm3HtPRgx+Hfr9eBBQYjgWVU72zgsLoJLmQ/vHTfKeNNgkoMJxEdyJNjv6c89DnSr6AKiiMT6qAFnD8syOgwHDSHAN992/H81Pwn3479Nv1kzGgCgqjk+wk0sX3jxeXMf014wFRAQWGk/Qs/Ktn8x3RjxpbjWn55goK45L6MqZ3z+YPl8tEQIHhJA3o7//78fLpnJlkDaiCwsgkC+gynt3Z+HxnkgQUGE6SgG7EM+s19XkDqqAwLimvA/1T3nh2BBQYTrI7kR78OvQbRWQOqILCqCS9E6n5PVABhVFJdCfSi0fzY6AfftHocnarDVBQGI90lzG9+rdPFhH9c76ICigwnLQX0i8i2up1oLMtUFAYjdR3Il28OBZQYBySBvSXxb3wLQf0UkBhNNI9E2l5CPQo4yHQEgJqFxRGI81z4Vd3ImVdy+6yiIDaBYXRSHgd6CTjPfBLJQRUQWEsPBMpPQGFkUgS0H/MPHG/UkRAFRRGwnPhMxBQGAcBzUFBYRQENAcBhVEQ0CwUFMZAQLMQUBgDAc1DQWEEBDQPAYURENBMFBTqJ6CZCCjUT0BzUVConoDmIqBQvVQBffXiyZUvst0aX1BAFRSqlyagbxcP5VxoekX6FQGF2iUJ6HJBUAHdoKBQuSQBPZtG89NvXq/8OvR73pmAAsNJEdCLryeTj4d+m5iiAqqgULlEK9IffTf028QIKDCcRAHNd9RzU1kBVVCoW6IpvIBu9eZNWdsD9JLqJNJXQ79NTHEBtQsKFUvzXPhidkELC6hdUKhamgvpL55Njp6+HvqdAkoLqIJCzQ4b0OtX0LuQ/ro3Cgr1EtDMHAaFeh04oI/vb/OpgF6xCwrVspxddgoKtRLQ7EzioVYCmp9dUKhUsoBe/N9uFeU//vGv2RZT7hQZUAWFSiUK6LvH81Pv3boi/zz0O/ZQakBN4qFGaQJ6fjxZBXQy+Xzot7y7MgOqoFCnJAF9O+3n0Wezufvvzyc517YrNKAm8VClJAE9mUw++m3tJ/lWVy41oAoKNUq0nN3aTud0d/QjT+W8ziQeKpR+QeWsyysXG1AFhQoJaClM4qE6qR4qd7Wg8vnEFH4ru6BQm1Qr0q8OgnZn5Pe7juni9S+np6c/vI5UuOyAKijUJUlAZ1d/Pvj29evXvzyb7LcD+urZ2rJ4D37svXEFB9QkHmqT5kL6brdzZY/LQN89uray6L2er1V0QE3ioTKpbuW82nF8EN//PJ91+P6TuU9mNe73tLrSA6qgUJN0i4n88pdp9L74do/pe3ck4OjbtS+8PO67un3ZAVVQqEtNy9md3chll9ReZ6QKD6jDoFCVJJcx/XL6w9qO5y8vnoZ2Qzevhprre01U6QF1GBRqUtGF9Nt+Yd8XqyCgCgrVENDCmMRDPQ4c0L93p8v/6Xhy9KcnS4+ijzXeXJNkbnRTeJN4qMiBA7pxAehKcDm7kxu17A6L9nqxKgKqoFCJQ0/hT7b0815wLZGuxh/9tPaFd1/3vSy/goAqKFTj0AG9OD09/X46hf/mdOV1+KXPZpfOP5m/1vfzK+n73VdfQ0BN4qEW6U8i7ePn60cEjr7suXE1BFRBoRIZrgPdx8WLjbvqe19QWktAFRRqUNOdSHMXr05fPHny5Onpj4Eo1xFQBYU6ZAloaCnPgG1XAKR5530JKNQgUUAvvl9dB3r/k2SP9Kg3oAoKNUgT0PPNkz+eifReJvFQgSQBvX45/R5Lgu6pmoAqKFQg1TORJg++fzg5+u8vHvVeA3lQ9QRUQaF8qZ7K+fnspqSvZjHN91DOmgLqMCgUL9GF9N0Nl2ezjG5b1TOdmgKqoFC6hHcinc8X/jiPLiYye7bnTSNbzm6NSTwULmFA3x7PJu/Tn8Xm8M0FVEGhcImOgXaVW5Qzfmf8jYcajz2gCgplS3IW/mR2DPSqo9HrQBdno/bZuLoCqqBQtFSXMXWHPeen4fsuIr9u7zNQtQVUQaFkSQLaHb2cRnOazg9+/K+H4RXpL/dfGa+6gDoVDwVLdCvn7FBlt/84W4Wu1yLyN15pn0l8fQFVUChXosVEXj6aHf6cnQXquwjyhsWB1KgqA6qgUKjEy9m9fPLk6a97vfzbJ0/+Pf6rKwyogkKx6ltQeS81BlRBoVQCWgEFhTIlCejvyx8833P+vrc6A6qgUKYEAf35eHnap7uc6bN8SzFdVhtQBYUiHTyg3bVLy+uWZgsr3/tp6HfsodaAupgJSnTogM6u/Vxd+PnuWdYHelQcUAWFAh06oCfTYv7D2qy92wmN34i0t5oDqqBQmgMHtOvl5s3r5/vdibSnegOqoFCeAwf07Ob+5sm+Kyrto+KAKigU57AB3bZ60j6rMe2t5oAqKJTmsAHdtnjSdFbvufAxCgplSR/QfVek20vdAXUqHsoioFVRUCjJwY+B3jjlvni2XB71B1RBoRwHPgu/5ZT7lhPz6dQeUAWFkhw4oDdPue//YLh9VB9QBYWCHDig3fIhm7k8cyH9fhQUinHoWzmnvdwoaPdzt3LuRUGhFEkWE7n37Xwaf/HzJ5Ocl9GPI6BdQccwDKhfkuXsugWZ7t+/fzz70b2MizGNI6AKCoU4/ILKF88na47+wYLK+1NQKEKKR3r8/vx4kc8Pn2bN52gCqqBQhEQPlbt4dXr6w+uh36q/sQRUQaEEnspZKQWF/AS0VgoK2QlotRQUchPQenUFHdN4oDoCWrE3dkIhKwGtmYJCVgJaNwWFjAS0cgoK+Qho7RQUshHQ6iko5CKg9VNQyERAR8AFoZCHgI6BgkIWAjoKCgo5COg4KChkIKAjoaCQnoCOxayg4x0elEhAR0NBITUBHY83pvGQloCOyBs7oZCUgI6KgkJKAjoudkIhIQEdGQWFdAR0dJxLglQEdHzshEIiAjpGCgpJCOgomcZDCgI6TqbxkICAjpWCwsEJ6GjZCYVDE9DxUlA4MAEdM+eS4KAEdNTshMIhCejIKSgcjoCOnZ1QOBgBHT0FhUMR0AZIKByGgLbgjdPxcAgC2gYJhQMQ0FbME5p7K2BUBLQdEgoDE9CWzBKaeyNgPAS0LW80FIYjoK2RUBiMgLZnIqEwDAFtkYTCIAS0SRMJhQEIaKMkFPYnoK2aSCjsS0DbtUho7s2AeglowyYSCnsR0KaZx8M+BLRxEgpxAto883iIElAkFIIElEtXhUKMgDIjodCfgLJgHg99CSgrEgr9CChrzOOhDwFlg4TC3Qko15jHw10JKDdIKNyNgLKFByfBXQgoW73RUHgvAWWHiYTCewgou2ko3EpAuc3EVB52E1BuN9FQ2EVAeZ/lmssSCtcIKHegobCNgHInE1N5uEFAuaOJhsI1AsqdTSam8rBOQOlDQ2GNgNKPhMKKgNKbhsKcgBLhjBJcCihhdkNBQInTUBonoOxDQmmagLInDaVdAsr+nFGiUQLKIDSUFgkoQ7lqqIjSCAFlQBpKWwSUYbnEnoYIKMPTUBohoByEyTwtEFAORUMZPQHlgBwQZdwElAPTUMZLQDk8DWWkBJQ0NJQRqjWgF7+c/vBr/18moDk5qcTY1BTQ318vk/ny0eypEkef/dbzJQQ0Nw1lTCoK6B8PJx/8rfvBxfPJ0tFX/V5DQAtgMs9o1BjQk66cf3ry5HGX0H4FFdBCaCijUGFAz6fZnM/d3309WTT1rgS0HMuG+i2hXhUGdLoD+vHiSxfTgn7e5zUEtCwaSt3qC2gXzdW8fbo3+lGfE0kCWpw3Ikq96gvo6lDo2td2bcoWh9o64lYN9btDZQSUIqwa6neIitQX0MuTydF3y6+9Pe53Fslfz4JpKNWpLKBdOc/XThw5Bjoub0SUqtQV0KkP//zjv652QbsvOQs/LmsN9btF6aoL6Nxs3n7x92PXgY7SGxWlDhUFdFrMVy8eH18FtCvq1eHQO/G3sRZv3qgo5asqoDOzii4Deu+nfr/YX8SqqCiFqy+gVy7+s2c+BbRGKkq5ag5ogL9+lVJRiiSgVOPNtYzm3h4QUOry5s21jubeIFomoNToekZzbw+NElAqttnR3FtDewSU6skouQgoY6GjJCegjIuMkpCAMko6SgoCyoi9eSOkHJKAMn7XO6qkDERAacVkIqQMTEBpy+xWehllGAJKkyZbQpp7m6iPgNIwGWU/AkrzJjpKkIDC3LaMCim3ElBYt3V3VEnZTkBhi4mQcgcCCreYbC9p7s2iEAIKd7ElpLk3ifwEFPq4sTvqj1TLBBRCNivqIXdtElDYx/WKKmnNeh+fEVDY281z9Z67XJXwxRYCCsPYUlGnm8q2/++UgMIBbMuo+X0phrvhTEDh0LZk1NHSDA5wPa+AQhrbdkf9eUzhgLdCCCikdC2juTdnzLbfRTb0ewz7egIKd6Cgh7N9DYPDnMoTUMhGQAe0rZkHTOecgEI+Crq3Hfubyd598Fcc+gWHJKAURUDDyljcRUAhIwXtabL11FDGzRn8FYd+wSEJKGUR0DubFNbOxUYN/opDv+CQBJTCKOh7bdx6UEw65wQUchLQW+x6rEoR7ZwRUMhKQbfJcUlShIBCVgK6poZmbhBQyEtBh1hXLhMBhbwaDmi13VwRUMisxYLWHs4lAYXMmgroOLq5IqCQWxsFHVk65wQUcht7QMdYzgUBhezGWtBR7nRuEFDIbnx1GX865wQU8hvPLmgr5VwQUCjACFrTWDrnBBQKUPMuaEnLy6UmoFCCKstT3vJyqQkolKCuXdCyVjXOSEChCJVUqLAV4XMTUChC8bugRa4In5uAQhmKLdKWZeFzb1IxBBTKUOAu6LbHaeTeprIIKBSipDgJ590IKBSikF3QiW7enYBCKbLnaiKcPQkolCLjLujGaaJcG1EhAYViZGnXxjn2DO9fNQGFYqQu2MSO554EFMqRrGLXr4pP9LajI6BQkIOnzPVJgxJQKMhBenYzmuI5EAGFkgwcNc08LAGFkgxXuLVozo94DvXCXBFQKMoABX1zvZ0DbBZbCSgUZb+AXmvnUBvFDgIKZYkWVDozEFAoS++Abpwj8ic8KQGFwty9oM6v5yagUJg7pFA5CyGgUJrbkiidRRFQKM32MCpngQQUirMZSDud5RJQKM4yk9JZOgGF8ihnJQQUyiOdlRBQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUICg5gIKMJzBGzX0Cw4p94cNjMvgjRr6BQ+o9Rm98efegryMP/cWbFHiNu1S5AeYkPHn3oK8jD/3FmxR4jbtUuQHmJDx596CvIw/9xZsUeI27VLkB5iQ8efegryMP/cWbFHiNu1S5AeYkPHn3oK8jD/3FmxR4jbtUuQHmJDx596CvIw/9xZsUeI27VLkB5iQ8efegryMP/cWbFHiNu1S5AeYkPHn3oK8jD/3FmxR4jbtUuQHmJDx596CvIw/9xZsUeI27VLkB5iQ8efegryMP/cWbFHiNu1S5AeYkPHn3oK8jD/3FmxR4jbtUuQHmJDx596CvIw/9xZsUeI27VLkB5iQ8efegryMP/cWbFHiNu1S5AeYkPHn3oK8jD/3FmxR4jbtUuQHmJDx596CvIw/9xZsUeI27VLkB5iQ8efegryMP/cWbFHiNu1S5AeYkPHn3oK8jD/3FmxR4jbtUuQHmJDx596CvIw/9xZsUeI27VLkB5iQ8efegryMP/cWbFHiNgFUQUABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIKiagL57djyZHD34Kfd2ZPDHww/+dvWzpj6Iixf3J5PJh+ujbWr8ly8fd+N/+tvVV9oa/8zb48lXq58UNv5aAvrz9FPrHP1z7i1J7uLryVpAm/ogloOdTD67/qUmxj/9rV+MdhWQpsY/98fDyaTY8VcS0PPJylfv/+5RuTiZrAW0qQ9ibbCTj298afzjX/XzarRNjX/hZG2wxY2/joB2/wbdm+60v3q0HpMmzP4Srcbc1AcxG+yPl/PRHn23+lIr4788mw67m73/13S0H81m8W2Nf+58rZbljb+OgJ4t/wB1Ofk899ak9HI2Y1n9WWnqgzhf7Xd2o539sKnxT8c4/2ej68b8R02Nf65r5ihq+NgAAAbKSURBVCqg5Y2/ioBe/Unqjid/9Nvt3z0i76b/0E4ePFoFtK0P4uRqmrYYbVvjnw5x8Q9IF47uo2hr/DPdGYD/sfyDUOD4qwjo9B+h5Ye19hE2oJvDfbl2EqnZD2Ix8GbHv/i3pMHxd/90nC0DWuD4qwjo1VxuY7dk/M6OPvtt/Sx8sx/E4m9Oq+OfDn/2R6C98U93ND+/XAW0wPHXEtDV8Y6zMj63NH7v/rndDGibH8Tib06b4794cbwYa3Pjn/7Zn/7DuR7Q0sZfRUDXP6vzQg4ep7MW0FY/iOVJlBbHfzK76vHL2Y+bG//J7Pd9NewCxy+gxRPQ7lLY5Un45sY/C+iHX84O/bU2/sUYBXRPBX5uCTUf0KtbCRoc/8W/3H/cXco2O3nS2PiX54wEdE8Ffm4JtR7Q7pK/xQnXJsd/Ob+crdsFb2z8J9f/3Sxw/AJavMYD+m51G1Kb459ZHARua/w3u1ng+KsIaIEn3xJq+yx8t3jEveWNWA2Of+FsNvKmxn91H4Gz8Hsq8PKvhJq+DvSsW4lpdcdJe+NfOl8GtJ3xn002lTn+KgJa4A0ICbV8J9LzycZErbnxr8wD2tT4twS0wPFXEdACb4FNaC2grX0QZ5PNvyVtjf9s7R+P+f5WU+PfEtACx19FQAtchCWh9QWV2/ogprtdH2yuPN7U+KeNWDt7Nvsz0NT4V64Od5Y3/joCulwZspxlABNaD2hTH8RqEbeNL7Uz/tn1W90V9O+eLY9kNDX+lauAljf+OgJa3kLUCW080qOlD2JzDjdvaUvjv3z38Gq0La7Iv7R2wr248VcS0Mu/F/YolIQ2n4nUzgex/kCLVUAbGv/lYj3YmdWVCE2Nf2H9iqXSxl9LQEt7GF9CmwFt54P44+G2gLYz/pn5UznbfSrpzMYln4WNv5qAApRGQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIEFCBIQAGCBBQgSEABggQUIEhAAYIElKK9PZ589Nv8h+eTyefdfy9+vj+ZTD799rfl9/z+ovvC0affzn96Mv0VLz+ZfsdPy+/98Omv6becFggoRbv4enL03fyHJ/MfTZM6d++n+dfPJkvz1E4D+nP3s+l3r753nl4YmIBStrNl/P54OAvkVRMnH/xt8Q2TjUyeTD6cfc/H3S9Z+SrjGBgtAaVsqzn8fAbfNfHet93c/LhL5PwLn3Uz9FePFrugJ92+6GzvdNrWez9O//vu68nqQAAMSEAp3MlqT7ObwZ+vUjgt5+ILH8+/cZra2XeerL7lZLnjefH14kVgUAJK4c7nFZw2sMviyeqQ6Oqk0tK0qMuALr5+ttwVhcMQUAo37WK3izndv/x8dSB0ZvqVj1ff9fsvfzmerAK6OOB5PjuX9Ke/mr1zIAJK6eZz+PkMfv200PK0+8XLx8sTS4uArnZSTxZfP3IdEwchoJRuNodfzODXzsEvA7r+pesBvXjhOiYOSUAp3WwOP5/Br19Xf/X/dpfKf/rFN//v4Y2ATr18pqAcjIBSvG4OP5/Br04UXTlbXVH/x9aATv3+/bOJ65g4BAGleG+Pj/7X14vjnV9fuyR+7U6l8+tT+LWLl26GFwYgoBRvGsL/9nAxBZ/ucH5wdQvn52sBfffwxjHQqwuaBJSDEFDKdzb58HgRxe6Q59GX053R35/P7nbvItlN4eeni2bfdLK+T3p1l9LHO18eogSU8nUn2tfWZNq49X3959cDurqMaXXjPAxKQClfd+RzdRL9/NqVSc9X13oujo+uX8b09fJ773235XVhTwJKBTZOrF+82Fzj8+Xj2U9/W94Wv/HNr551vV1bPBQGJKCUb/0GTiiIgFK+M5fBUyYBpXgvj50CokwCStnO3YdJuQSUss3WCnEElDIJKGX749Fk8pl+UiYBBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQgSUIAgAQUIElCAIAEFCBJQgCABBQj6/5fyJOCkHZgoAAAAAElFTkSuQmCC" width="672" /></p>
<p>The above plot focus on the deterministic iteration (iter=1) but the plot can be done also for the second iteration (stochastic).</p>
<pre class="r"><code>plot(1:41,Sum.Pop.Mod(Pop.Mod,c("C"))$C[,,2],type="l",xlab = "years", ylab="Catch weight")
for (i in 1:100){
lines(1:41,IC[[i]]$weight[,,2],col="red")
}
lines(1:41,Sum.Pop.Mod(Pop.Mod,c("C"))$C[,,2])</code></pre>
<p><img 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" width="672" /></p>
<p>Note that if type=“catch weight” the catch numbers are also reported and you can access as follows.</p>
<pre class="r"><code>IC=Sampling_Catch(Pop.Mod=Pop.Mod,type="catch weight",CV_CN=0)
#IC$weight
#IC$numbers</code></pre>
</div>
<div id="length-sample-1" class="section level4">
<h4>Length sample</h4>
<p>First of all we need to specify the values of the parameter par not required for catch weight or numbers but important for obtaining the length sample. In this case we fix it as follows.</p>
<pre class="r"><code>par=c(0.2,1000,3000)</code></pre>
<p>Below, we compute a stock length sample and a catch length sample for <span class="math inline">\({CV}_{CN}\)</span>=0 and <span class="math inline">\({CV}_{A}\)</span>=0. Then we plot the density of both samples for the first iteration and last year. Note that stock sample density is in black and the catch sample density is in red.</p>
<pre class="r"><code>IC=Sampling_Catch(Pop.Mod=Pop.Mod,type="LengthC",CV_CN=0,par=par)</code></pre>
<pre><code>## It is working, please give us a while :)</code></pre>
<pre class="r"><code>q_A<-matrix(1,ncol=41,nrow=16);gamma<-1
IAS=Sampling_Survey(Pop.Mod=Pop.Mod,type="LengthS",q_A=q_A,gamma=gamma,CV_A=0,par=par,tsampling=0)</code></pre>
<pre><code>## It is working, please give us a while :)</code></pre>
<pre class="r"><code>plot(density(IAS$length[,41,1]),xlab="Length", main="Densities of length samples")
lines(density(IC$length[,41,1]),col="red")</code></pre>
<p><img 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" width="672" /></p>
<p>The same plot is carried out in the above code but for <span class="math inline">\({CV}_{CN}\)</span>=0.2 and <span class="math inline">\({CV}_{A}\)</span>=0.2.</p>
<pre class="r"><code>par=c(0.2,1000,3000)
IC=Sampling_Catch(Pop.Mod=Pop.Mod,type="LengthC",CV_CN=0.2,par=par)</code></pre>
<pre><code>## It is working, please give us a while :)</code></pre>
<pre class="r"><code>IAS=Sampling_Survey(Pop.Mod=Pop.Mod,type="LengthS",q_A=q_A,gamma=gamma,CV_A=0.2,par=par,tsampling=0)</code></pre>
<pre><code>## It is working, please give us a while :)</code></pre>
<pre class="r"><code>plot(density(IAS$length[,41,1]),xlab="Length", main="Densities of length samples")
lines(density(IC$length[,41,1]),col="red")</code></pre>
<p><img 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" width="672" /></p>
<p>As you can see above, the catch length sample can be access using:</p>
<pre class="r"><code>#IC$length</code></pre>
<p>Note that when type=“LengthC”, the catch numbers also reported and can be access using:</p>
<pre class="r"><code>#IC$numbers</code></pre>
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