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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 (First tutorial)</title>
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) 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<h1 class="title toc-ignore">Rfishpop (First tutorial)</h1>
<h4 class="author">Marta Cousido Rocha, Santiago Cerviño López, Maria Grazia Pennino</h4>
<h4 class="date">28/7/2020</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 this tutorial we explain how to simulate the real dynamics of the fishery system using a <strong>generic operating model (OM)</strong> implemented in Population.Modeling function which is the main function of our Rfishpop package.</p>
<p><span style="color:red">NOTE:</span> The aim of this tutorial is to learn all the possibilities that the Population.Modeling function offers, then the values used here for the different parameters required by the OM does not tried to generate a population in a particular situation or corresponding to a particular type of species. The values are more or less realistic values but the idea is to illustrate how to use the function. Next tutorials show examples corresponding to long life species, short life species and so on.</p>
<div id="population.modeling-function" class="section level3">
<h3><span style="color:blue">Population.Modeling function</span></h3>
</div>
<div id="arguments-of-population.modeling-function" class="section level3">
<h3>Arguments of Population.Modeling function</h3>
<p>The first step is to learn which are the arguments of this function. The arguments are divided in 4 blocks:</p>
<ol style="list-style-type: decimal">
<li><p>General information about the population that we desire to simulate and some control arguments.</p></li>
<li><p>Biological parameters of the population.</p></li>
<li><p>Fishing parameters of the population.</p></li>
<li><p>Parameters of spawning stock recruitment relationship.</p></li>
</ol>
<p>Let’s start with <strong>(1)</strong>.</p>
<p>Argument <em>years</em> is a vector containing the years for which the dynamics of the fishery system is simulated. In this case we consider the period from 1980 to 2020.</p>
<p>Argument <em>ages</em> is a vector containing the different ages presented in the stock. Note that the oldest age defines a plus group. <span style="color:red">The first age must be 0, in the current version the OM is not adapted to treat with populations started in other ages.</span> In this case we consider a stock where the ages goes from 0 years to 15 years.</p>
<p>Argument <em>niter</em> is number of iterations of the simulation process. In this case is fixed in 2 (just to make this example faster!)</p>
<p>Argument <em>N0</em> is a number corresponding to the population size at first age and year, or a vector containing the population size at first year for all ages. In this case we decide to establish only the value at first age and year in 10000 individuals. Note that it is important to take care of the units when we introduce the different parameters since N0 can be measured in individuals or in thousands of individuals.</p>
<p>Arguments <em>minFage</em> and <em>maxFage</em> are the minimum and maximum age (respectively) for which the corresponding fishing mortality is considered to compute the mean fishing mortality. In this case we fix it in 4 and 7 years, respectively.</p>
<p>Argument <em>tc</em> is the time of the year at which the catches are simulated. This parameter takes a value between 0 and 1, since the year is considered as a unit. In this case, we fix <em>tc=0.5</em> assuming that the catches occurs at mid of the year (this is an approximation since the catches occurs during all the year).</p>
<p>Argument <em>seed</em> is a numeric value to introduce into set.seed() function for having a reproducible result. In this case is NULL which means that the results are not reproducible, that is, each run returns different results.</p>
<pre class="r"><code>library(Rfishpop)
ctrPop<-list(years=seq(1980,2020,by=1),ages=0:15,niter=2,N0=10000,minFage=4,
maxFage=7,tc=0.5,seed=NULL)</code></pre>
<p>Now, we continue explaining block <strong>(2)</strong> which contains the biological information.</p>
<p>Argument <em>M</em> is a matrix containing the rates of instantaneous natural mortality for each year and age. As we show below in this case is constant equal to 0.4 for all the years and ages. Associated to <em>M</em> we also have the parameter <em>CV_M</em> which is the corresponding coefficient of variation. In each stochastic iteration the rates of instantaneous natural mortality come from a log-normal distribution centered on the corresponding value of M and variability determined by CV_M. In this case CV_M=0.2. NOTE THAT the associated coefficient of variation can be equal to 0 and then the instantaneous natural mortality in the stochastic iterations are equal to the ones in the deterministic one.</p>
<pre class="r"><code>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</code></pre>
<p>Arguments <em>L_inf</em>, <em>t0</em> and <em>k</em> are parameters of Von Bertalanffy Growth Model whose equation is</p>
<p><span class="math display">\[L(x)=L_{inf}(1-e^{(-k(x-t0))})\]</span></p>
<p>where <em>L_inf</em> is the asymptotic average maximum body size, <em>t0</em> is hypothetical age at which the species has zero length, and <em>k</em> is growth rate coefficient and x is the vector of ages where the function must be computed. In this case we take <span class="math inline">\(L_{inf}=124.5\)</span>, <span class="math inline">\(t0=0\)</span>, <span class="math inline">\(k=0.164\)</span>. Associated to the length of the stock and catch length we have the coefficients of variation <em>CV_L</em> and <em>CV_LC</em>, respectively. In each stochastic iteration the length comes from a normal distribution centered on the corresponding length obtained from Von Bertalanffy Growth Model and the corresponding variability is determined by CV_L or CV_LC depending if we focus on stock or catch length. In this case CV_L=0.2 and CV_LC=0.2. <span style="color:red">NOTE: In the section of ISSUES of the GITHUB repository of the package it is possible to learn about the need of modifications of the current process of generating stochastic lengths.</span></p>
<p>Arguments <em>a</em> and <em>b</em> are the parameters of the Length-Weight relationship whose equation is <span class="math display">\[W=aL^b\]</span> where L is the vector of lengths by age, <em>a</em> is the allometric growth parameter, <em>b</em> scaling constant, and W is the age weight vector. In this case a=4.5*10^(-6) and b=3.1049, whose units are according to the units used to measure the length and weight which are cm and kg.</p>
<p>Arguments <em>a50_Mat</em> and <em>ad_Mat</em> are parameters of the logistic function used to generate the maturity matrix. More precisily, <span class="math display">\[L(x)=1/(1+e^{(x-a50_{Mat})/ad_{Mat}})\]</span></p>
<p>where <em>a50_Mat</em> is the x-value of the sigmoid’s midpoint of the logistic function, and <em>ad_Mat</em> is minus the inverse of the logistic growth rate (steepness of the curve). In this case a50_Mat=3 and ad_Mat=-0.5. Associated to the maturity matrix we have the coefficient of variation <em>CV_Mat</em>. In each stochastic iteration the maturity matrix values comes from a logistic function whose a50_Mat parameter is generated from a log-normal distribution centered on the given value of a50_Mat and variability determined by CV_Mat. In this case CV_Mat=0.2.</p>
<pre class="r"><code>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)</code></pre>
<p>The block <strong>(3)</strong> contains the fishing information. Below we describe each of the arguments in this block.</p>
<p>Argument <em>f</em> is the annual component of fishing mortality <span class="math inline">\(F = f * SEL\)</span>. Can be different for each of the iterations, hence we introduce a matrix whose rows contain the annual vector for each iteration. In this case a constant vector equal to 0.5.</p>
<pre class="r"><code>f=matrix(rep(0.5,number_years),ncol=number_years,nrow=2,byrow=TRUE)</code></pre>
<p>Argument <em>ctrSEL</em> specifies the selectivity function considered and the corresponding parameters. The different selectivity functions are:</p>
<ul>
<li>Constant selectivity function. In <em>ctrSEL</em> we must specify it using <em>type=“cte”</em> argument, and then in argument <em>par</em> we must introduce the constant value, in this case 0.5. Furthermore, we also need to introduce the corresponding coefficient of variation <em>CV_SEL</em>. The values of the selectivity matrix comes from a uniform distribution with mean equal to “cte” and whose variability is determined by CV_SEL. In this case CV_SEL=0.2</li>
</ul>
<pre class="r"><code>ctrSEL<-list(type="cte", par=list(cte=0.5),CV_SEL=0.2)</code></pre>
<ul>
<li>Andersen selectivity function. In <em>ctrSEL</em> we must specify it using <em>type=“Andersen”</em> argument, and then in argument <em>par</em> we must introduce the values of its parameters.</li>
</ul>
<p><span class="math display">\[SA(x) = \begin{cases} p0+p2e^{-(ln(p5/x)-p1)^2/p4}, & \mbox{if } {ln(p5/x)\leq p1} \\ p0+p2e^{-(ln(p5/x)-p1)^2/p3}, & \mbox{if } {ln(p5/x)>p1} \end{cases}.\]</span></p>
<p>We fixed p0=0, which is the beginning size for the plateau, and p2=1, which is the maximum value attainable by the function. Remember that p3 and p4 are the ascending and descending slope parameters, respectively, whereas p1 and p5 define the value of x at which the transition between the two gaussian functions happens; that is, x=p5/exp(p1). In this case, p1=2,p3=0.2,p4=0.2,p5=40.</p>
<p>Furthermore, we also need to introduce the corresponding coefficient of variation <em>CV_SEL</em>. The value of the parameter p1 in each stochastic iteration comes from a normal distribution centered on the given value of such parameter, and whose variability is determined by CV_SEL. In this case CV_SEL=0.05.</p>
<pre class="r"><code>ctrSEL<-list(type="Andersen", par=list(p1=2,p3=0.2,p4=0.2,p5=40),CV_SEL=0.05)</code></pre>
<ul>
<li>Gamma selectivity function. In <em>ctrSEL</em> we must specify it using <em>type=“Gamma”</em> argument, and then in argument <em>par</em> we must introduce the values of its parameters.</li>
</ul>
<p><span class="math display">\[SG(x)=((x/((\alpha-1) \beta\;\gamma))^{(\alpha-1))}e^{\alpha-1-(1/\beta\;\gamma)},\]</span></p>
<p>where <em>gamma=<span class="math inline">\(\gamma\)</span></em> is the size of the mesh, <em>alpha=<span class="math inline">\(\alpha\)</span></em> is the shape parameter and <em>beta=<span class="math inline">\(\beta\)</span></em> is the scale parameter. In this case gamma=10, alpha=15, beta=0.03.</p>
<p>Furthermore, we also need to introduce the corresponding coefficient of variation <em>CV_SEL</em>. The value of the parameter alpha in each stochastic iteration comes from a normal distribution centered on the given value of such parameter, and whose variability is determined by CV_SEL. In this case CV_SEL=0.05.</p>
<pre class="r"><code>ctrSEL<-list(type="Gamma", par=list(gamma=10,alpha=15, beta=0.03),CV_SEL=0.05)</code></pre>
<ul>
<li>Logistic selectivity function. In <em>ctrSEL</em> we must specify it using <em>type=“Logistic”</em> argument, and then in argument <em>par</em> we must introduce the values of its parameters.</li>
</ul>
<p><span class="math display">\[L(x)=1/(1+e^{(x-a50_{Sel})/ad_{Sel}})\]</span></p>
<p>where a50_Sel is the x-value of the sigmoid’s midpoint of the logistic function, and ad_Sel is minus the inverse of the logistic growth rate (steepness of the curve). In this case a50_Sel=1.5 and ad_Sel=-1.</p>
<p>Furthermore, we also need to introduce the corresponding coefficient of variation <em>CV_SEL</em>. The value of the parameter a50_SEL in each stochastic iteration comes from a normal distribution centered on the given value of such parameter, and whose variability is determined by CV_SEL. In this case CV_SEL=0.2.</p>
<pre class="r"><code>ctrSEL<-list(type="Logistic", par=list(a50_Sel=1.5, ad_Sel=-1),CV_SEL=0.2)</code></pre>
<p>Finally, we save in the same object all the fishing information.</p>
<pre class="r"><code>ctrFish<-list(f=f,ctrSEL=ctrSEL)</code></pre>
<p>To finish the description of the arguments, we focus on block <strong>(4)</strong> which contains the stock-recruitment relationship information.</p>
<p>Argument <em>type</em> specifies the selected stock recruitment model.</p>
<ul>
<li><em>type=“cte”</em> means that a constant recruitment is used. Then, argument <em>par</em> contains the values of the parameters of the stock recruitment model specified previously. If type=“cte” then recruitment is equal to the population size at first age and year introduced previously in N0 parameter then par contains only <em>CV_REC_C</em> which is the associated coefficient of variation. For the stochastic iterations the recruitment is generated from a log-normal distribution centered on N0 and whose variability is determined by CV_REC_C.</li>
</ul>
<pre class="r"><code>CV_REC_C=0.2
SR<-list(type="cte",par=c(CV_REC_C))</code></pre>
<ul>
<li><em>type=“BH”</em> corresponds to Beverton-Holt Recruitment Model. Then, argument <em>par</em> contains the values of the parameters of the stock recruitment model specified previously.</li>
</ul>
<p><span class="math display">\[R=a_{BH}SSB/(b_{BH}+SSB)\]</span></p>
<p>where SSB is the maturity biomass (spawning stock), a_BH is the maximum number of recruitments produced and b_BH is the spawning stock needed to produce recruitment equal to half maximum. In this case a_BH=1000 and b_BH=400. Furthermore, we have a coefficient of variation associated, in each stochastic iteration the deterministic equation of the model is multipled by log-normal residuals centered on 0 and whose variability is determined by <em>CV_REC_BH</em>. In this case is CV_REC_BH=0.2.</p>
<pre class="r"><code>a_BH=10000; b_BH=400; CV_REC_BH=0.2
SR<-list(type="BH",par=c(a_BH,b_BH,CV_REC_BH))</code></pre>
<ul>
<li><em>type=“RK”</em> refers to Ricker Recruitment Model. Then, argument <em>par</em> contains the values of the parameters of the stock recruitment model specified previously.</li>
</ul>
<p><span class="math display">\[R=a_{RK}SSBe^{-b_{RK}*SSB}\]</span></p>
<p>where SSB is the maturity biomass (spawning stock), a_RK is the recruits-per-spawner at low stock levels and b_RK relates to the rate of decreasing of recruits-per-spawner as SSB increases. In this case, a_RK=10 and b_RK=0.0002. Furthermore, we have a coefficient of variation associated, in each stochastic iteration the deterministic equation of the model is multipled by log-normal residuals centered on 0 and whose variability is determined by CV_REC_RK. In this case CV_REC_RK=0.2.</p>
<pre class="r"><code>a_RK=10; b_RK=0.0002; CV_REC_RK=0.2
SR<-list(type="RK",par=c(a_RK,b_RK,CV_REC_RK))</code></pre>
</div>
<div id="use-of-population.modeling" class="section level3">
<h3>Use of Population.Modeling</h3>
<p>Now, that we are defined correctly the FOUR blocks of arguments we introduce them in the main function using the following line of code.</p>
<pre class="r"><code>Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)</code></pre>
</div>
<div id="results-of-population.modeling" class="section level3">
<h3>Results of Population.Modeling</h3>
<p>Now, the object Pop.Mod contains two blocks of results.</p>
<ul>
<li>Matrices. Arrays of matrices containing the population values.</li>
</ul>
<p>N: Third dimensional array containing the population size for each age, year and iteration.</p>
<pre class="r"><code>N=Pop.Mod$Matrices$N</code></pre>
<p>F: Third dimensional array containing the instantaneous fishing mortality for each age, year and iteration.</p>
<pre class="r"><code>F=Pop.Mod$Matrices$F</code></pre>
<p>M: Third dimensional array containing the instantaneous natural mortality for each age, year and iteration.</p>
<pre class="r"><code>M=Pop.Mod$Matrices$M</code></pre>
<p>W: Third dimensional array containing the weight corresponding to the stock length for each age, year and iteration (at ts time of the year at which the stock is simulated. It has been fixed in 0 by default. No possibility of changing such default value is provided).</p>
<pre class="r"><code>W=Pop.Mod$Matrices$W</code></pre>
<p>Mat: Third dimensional array containing the proportion of mature at each age, year and iteration.</p>
<pre class="r"><code>Mat=Pop.Mod$Matrices$Mat</code></pre>
<p>C_N: Third dimensional array containing the number of catches for each age, year and iteration.</p>
<pre class="r"><code>C_N=Pop.Mod$Matrices$C_N</code></pre>
<p>C_W: Third dimensional array containing the weight of catches for each age, year and iteration (at tc).</p>
<pre class="r"><code>C_W=Pop.Mod$Matrices$C_W</code></pre>
<ul>
<li>Info. In this block we save useful information employed to create the population and which will be used by other functions of Rfishpop package. We can check this information using the next line of code.</li>
</ul>
<pre class="r"><code>Pop.Mod$Info</code></pre>
<pre><code>## $ctrBio
## $ctrBio$CV_M
## [1] 0.2
##
## $ctrBio$L_inf
## [1] 124.5
##
## $ctrBio$t0
## [1] 0
##
## $ctrBio$k
## [1] 0.164
##
## $ctrBio$CV_L
## [1] 0.2
##
## $ctrBio$CV_LC
## [1] 0.2
##
## $ctrBio$a
## [1] 4.5e-06
##
## $ctrBio$b
## [1] 3.1049
##
## $ctrBio$a50_Mat
## [1] 3
##
## $ctrBio$ad_Mat
## [1] -0.5
##
## $ctrBio$CV_Mat
## [1] 0.2
##
##
## $ctrFish
## $ctrFish$f
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [2,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
## [1,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [2,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
## [1,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [2,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [,39] [,40] [,41]
## [1,] 0.5 0.5 0.5
## [2,] 0.5 0.5 0.5
##
## $ctrFish$ctrSEL
## $ctrFish$ctrSEL$type
## [1] "Logistic"
##
## $ctrFish$ctrSEL$par
## $ctrFish$ctrSEL$par$a50_Sel
## [1] 1.5
##
## $ctrFish$ctrSEL$par$ad_Sel
## [1] -1
##
##
## $ctrFish$ctrSEL$CV_SEL
## [1] 0.2
##
##
##
## $SR
## $SR$type
## [1] "RK"
##
## $SR$par
## [1] 1e+01 2e-04 2e-01
##
##
## $minFage
## [1] 4
##
## $maxFage
## [1] 7
##
## $seed
## NULL
##
## $ts
## [1] 0
##
## $tc
## [1] 0.5</code></pre>
</div>
<div id="comments-about-the-performance-of-population.modeling-function" class="section level3">
<h3>Comments about the performance of Population.Modeling function</h3>
</div>
<div id="n0-argument" class="section level3">
<h3>N0 argument</h3>
<p>Argument <em>N0</em> is a number corresponding to the population size at first age and year, or a vector containing the population size at first year for all ages. If we introduce only the population size at first age and year the remaining values of the first year are computed as follows.</p>
<p><span class="math display">\[ N_{i1}=e^{(-Z_{i-1,1})}*N_{i-1,1}, \]</span> for <span class="math inline">\(i \in {2:15}\)</span> in our example, to wit, all ages except the first one and the plus group which is defined as:</p>
<p><span class="math display">\[ N_{i1}=e^{(-Z_{i-1,1})}*N_{i-1,1}/(1-e^{(-Z_{i,1})}),\; i=16.\]</span></p>
<p>Note that index <span class="math inline">\(i\)</span> corresponds to age <span class="math inline">\(i\)</span> plus 1 since we can not use i=0 which is the first age.</p>
<p>Furthermore, if N0 is a number then its is updated using the recruitment model evaluated at the SSB (spawning stock biomass) of the first year (such SSB assume negligible the contribution of age 0). More precisely, the maturity at first age (age 0 years) is assumed 0. Whereas if N0 is a vector such procedure of updated is not carried out.</p>
<p>On the other hand it is important to note that the time of the year (<span class="math inline">\(t_s\)</span>) at which the stock is simulated has been fixed in 0 by default, which means that the stock numbers correspond to 1st of January. No possibility of changing such value is provided.</p>
<div id="deterministic-and-stochastic-performance" class="section level4">
<h4>Deterministic and stochastic performance</h4>
<p>The first iteration (niter=1) contains the results corresponding to the deterministic case, to wit, all the coefficients of variation (CV’s) associated to the different biological and fishery components are zero.</p>
<p>We can access to the first iteration of the population size matrix N (for example) as follows.</p>
<pre class="r"><code>N_D<-N [,,1]</code></pre>
<p>The next iterations contain the stochastic results. In this case iteration 2, since niter=2.</p>
<pre class="r"><code>N_D<-N [,,2]</code></pre>
<p>Hence, if niter=1 (CV’s=0), we only obtain the deterministic performance. If niter=1 and the CV’s are different than 0, the function returns ERROR because the first iteration is the deterministic one and hence the CV’s can not be used.</p>
<pre class="r"><code>ctrPop<-list(years=seq(1980,2020,by=1),niter=1,N0=10000,ages=0:15,minFage=4,
maxFage=7,tc=0.5,seed=NULL)
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)</code></pre>
<p><strong>Error in Population.Modeling(ctrPop = ctrPop, ctrBio = ctrBio, ctrFish = ctrFish, : niter is equal to 1 whereas the coefficients of variation are not zero. Note that the first iteration is the deterministic one, hence you need al least one iteration more to use the coefficients of variation.</strong></p>
<p>If niter>1, the CV’s are 0 and the matrix of the annual component of fishing mortality has constant rows, the function returns ERROR because it does has sense to repeat a deterministic process (a process without variability).</p>
<pre class="r"><code>ctrPop<-list(years=seq(1980,2020,by=1),niter=2,N0=10000,ages=0:15,minFage=4,
maxFage=7,tc=0.5,seed=NULL)
ctrBio<-list(M=M,CV_M=0, L_inf=124.5, t0=0, k=0.164, CV_L=0, CV_LC=0, a=4.5*10^(-6), b=3.1049,
a50_Mat=3, ad_Mat=-0.5,CV_Mat=0)
ctrSEL<-list(type="Logistic", par=list(a50_Sel=1.5, ad_Sel=-1),CV_SEL=0)
f=matrix(rep(0.5,number_years),ncol=number_years,nrow=2,byrow=TRUE)
ctrFish<-list(f=f,ctrSEL=ctrSEL)
CV_REC_C=0
SR<-list(type="cte",par=c(CV_REC_C))
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)</code></pre>
<p><strong>Error in Population.Modeling(ctrPop = ctrPop, ctrBio = ctrBio, ctrFish = ctrFish, : The coefficients of variation are zero whereas the number of iterations (niter) is greater than 1, and the matrix of the annual component of fishing mortality has constant rows</strong></p>
<p>If the number of iterations does not match with the number of rows of the matrix of the annual component of fishing mortality an error is reported.</p>
<pre class="r"><code>ctrPop<-list(years=seq(1980,2020,by=1),niter=2,N0=10000,ages=0:15,minFage=4,
maxFage=7,tc=0.5,seed=NULL)
ctrBio<-list(M=M,CV_M=0, L_inf=124.5, t0=0, k=0.164, CV_L=0, CV_LC=0, a=4.5*10^(-6), b=3.1049,
a50_Mat=3, ad_Mat=-0.5,CV_Mat=0)
ctrSEL<-list(type="Logistic", par=list(a50_Sel=1.5, ad_Sel=-1),CV_SEL=0.2)
f=matrix(rep(0.5,number_years),ncol=number_years,nrow=1,byrow=TRUE)
ctrFish<-list(f=f,ctrSEL=ctrSEL)
SR<-list(type="cte",par=c(CV_REC_C))
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)</code></pre>
<p><strong>Error in Population.Modeling(ctrPop = ctrPop, ctrBio = ctrBio, ctrFish = ctrFish, : The annual component of fishing mortality must be a matrix whose rows contain the annual vector for each iteration</strong></p>
</div>
</div>
<div id="units-of-the-different-parameters" class="section level3">
<h3>Units of the different parameters</h3>
<p>The value of N0 in our example is the number of individuals. The L_inf value is suitable to measure the length in cm. The values of a and b are corrected to relate length in cm with the weight in Kg. The values of the parameters of the stock recruitment relationship have been introduced also taken into account that we are working with individuals, Kg and cm.</p>
</div>
<div id="function-sum.pop.mod" class="section level3">
<h3><span style="color:blue">Function Sum.Pop.Mod</span></h3>
<p>Furthermore than the information accessible by <em>Pop.Mod$Matrices</em> we can extract more information using the function Sum.Pop.Mod. This function allows us to extract additional information obtained in the simulation process of Population.Modeling (main function).</p>
</div>
<div id="arguments-of-sum.pop.mod-function" class="section level3">
<h3>Arguments of Sum.Pop.Mod function</h3>
<p>This function is applied on the object Pop.Mod returned by Population.Modeling function (main function). The second argument is <em>Elements</em> in which we specify the information that we desire to extract from Pop.Mod object.</p>
</div>
<div id="results-of-the-main-function-sum.pop.mod" class="section level3">
<h3>Results of the main function Sum.Pop.Mod</h3>
<p>The information that can be extracted is:</p>
<p>-“Z”: Third dimensional array containing the instantaneous mortality for each age, year and iteration.</p>
<p>-“LS”:Third dimensional array containing the (stock) length for each age, year and iteration (at ts=0, 1st of January).</p>
<p>-“LC”:Third dimensional array containing the length of the catches for each age, year and iteration (at tc).</p>
<p>-“WS”:Third dimensional array containing the population weight for each age, year and iteration.</p>
<p>-“WSSB”:Third dimensional array containing the weight of the mature population for each age, year and iteration.</p>
<p>-“C”:Weight of the catches for each year and iteration.</p>
<p>-“SEL”:Selectivity by age, for each iteration.</p>
<p>-“BIO”:Total biomass for each year and iteration.</p>
<p>-“SSB”:Maturity biomass for each year (spawning stock) and iteration.</p>
<p>-“REC”:Population numbers at first age.</p>
<p>-“F”:Mean fishing mortality (only takes the values between the minFage and maxFage).</p>
<p>-“WC”:Third dimensional array containing the weight corresponding to the catch length for each age (at tc), year and iteration.</p>
<p>Below we have some examples extracting different elements.</p>
<pre class="r"><code>Z<-Sum.Pop.Mod(Pop.Mod,c("Z"))
LS<-Sum.Pop.Mod(Pop.Mod,c("LS"))
LC<-Sum.Pop.Mod(Pop.Mod,c("LC"))
WS<-Sum.Pop.Mod(Pop.Mod,c("WS"))
WSSB<-Sum.Pop.Mod(Pop.Mod,c("WSSB"))
C<-Sum.Pop.Mod(Pop.Mod,c("C"))
SEL<-Sum.Pop.Mod(Pop.Mod,c("SEL"))
BIO<-Sum.Pop.Mod(Pop.Mod,c("BIO"))
SSB<-Sum.Pop.Mod(Pop.Mod,c("SSB"))
REC<-Sum.Pop.Mod(Pop.Mod,c("REC"))
F<-Sum.Pop.Mod(Pop.Mod,c("F"))
WC<-Sum.Pop.Mod(Pop.Mod,c("WC"))</code></pre>
</div>
<div id="selecting_units-function" class="section level3">
<h3>Selecting_units function</h3>
<p>This function returns the following matrices provided by Sum.Pop.Mod function but in tonnes instead on Kg:</p>
<ul>
<li><p>“C”:Weight of the catches for each year and iteration.</p></li>
<li><p>“BIO”:Total biomass for each year and iteration.</p></li>
<li><p>“SSB”:Maturity biomass for each year (spawning stock biomass) and iteration.</p></li>
</ul>
<p>Below, we can see the easy use of this function.</p>
<pre class="r"><code>E<-selecting_units(Pop.Mod,c("C","BIO","SSB"))</code></pre>
</div>
<div id="internal-calls" class="section level3">
<h3>Internal calls</h3>
<p>The function Population.Modeling uses some functions that are also available to use independently.</p>
<p>Previously, we mentioned that several selectivity functions are available.</p>
<ul>
<li>Andersen Selectivity function. The function <em>andersen</em> computes this function, and the arguments are the ones decribed above in Population.Modeling section.</li>
</ul>
<pre class="r"><code>p1=2;p3=0.2;p4=0.2;p5=40
ages<-0:15
SA<-andersen(x=ages,p1=p1,p3=p3,p4=p4,p5=p5)
plot(ages,SA,type="b", pch=19, col="red", main="Andersen Selectivity function")</code></pre>
<p><img 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" width="672" /></p>
<ul>
<li>Gamma selectivity function. The function <em>gamma_SEL</em> computes this function, and the arguments are the ones decribed above in Population.Modeling section.</li>
</ul>
<pre class="r"><code>gamma=10;alpha=15; beta=0.03
ages<-seq(0, 15, by=0.1)
SG<-gamma_SEL(x=ages,alpha=alpha,gamma=gamma,beta=beta)
plot(ages,SG,type="b", pch=19, col="red", main="Gamma Selectivity function")</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<ul>
<li>Logistic selectivity function. The function <em>Logistic</em> computes this function, and the arguments are the ones decribed above in Population.Modeling section.</li>
</ul>
<pre class="r"><code>a50_Sel=1.5; ad_Sel=-1
ages<-0:15
LO<-Logistic(x=ages,x50=a50_Sel,xd=ad_Sel)
plot(ages,LO,type="b", pch=19, col="red", main="Logistic Selectivity function")</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>The logistic function is also used to determine the maturity.</p>
<pre class="r"><code>a50_Mat=3; ad_Mat=-0.5
ages<-0:15
Mat<-Logistic(x=ages,x50=a50_Mat,xd=ad_Mat)
plot(ages,Mat,type="b", pch=19, col="red", main="Maturity function")</code></pre>
<p><img 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" width="672" /></p>
<p>On the other hand, we have two functions useful to define the length and to obtain then the weight.</p>
<p>The function <em>Length_VB</em> computes the length using Von Bertalanffy Growth Model, and the arguments are the ones decribed above in Population.Modeling section.</p>
<pre class="r"><code>L_inf=124.5; t0=0; k=0.164;ts=0 # (ts=0 is fixed by default)
ages<-0:15
LS<-Length_VB(L_inf,k,ages+ts,t0)
plot(ages,LS,type="b", pch=19, col="red", main="Stock Length")</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>Once we have the stock length we can obtain the weigth using the function <em>Weight</em> which computes the weight from the length using the relationship explained in Population.Modeling section.</p>
<pre class="r"><code>a=4.5*10^(-6); b=3.1049
WS<-Weight(LS,a,b)</code></pre>
<p>There are two insteresting plots that we can provide. The first one gives the weight of the stock for the different ages.</p>
<pre class="r"><code>plot(ages,WS,type="b", pch=19, col="red", main="Stock Weight")</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>The second one shows the length-weight relationship plotting the corresponding weight for each length.</p>
<pre class="r"><code>plot(LS,WS,type="b", pch=19, col="red", main="Length-Weight")</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>Finally, we have several functions related with stock-recruitment relationship.</p>
<ul>
<li><em>RBH</em> which computes the recruitment using Beverton-Holt Recruitment Model.</li>
</ul>
<pre class="r"><code>years=seq(1980,2020,by=1)
a_BH=10000; b_BH=400
R<-RBH(SSB$SSB[,,1],a_BH,b_BH)
plot(years,R,type="b", pch=19, col="red", main="Beverton-Holt Recruitment")</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<ul>
<li><em>RRK</em> which computes the recruitment using Ricker Model.</li>
</ul>
<pre class="r"><code>a_RK=10; b_RK=0.0002
R<-RRK(SSB$SSB[,,1],a_RK,b_RK)
plot(years,R,type="b", pch=19, col="red", main="Ricker Model")</code></pre>
<p><img src="data:image/png;base64,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" width="672" /></p>
<p>Note that we have used the SSB of the first iteration as an example.</p>
<p>Related to the stock recruitment relationship the following function is useful.</p>
<div id="steepness-of-the-stock-recruitment-relationship" class="section level4">
<h4><span style="color:blue">Steepness of the Stock Recruitment Relationship</span></h4>
<p>This function returns the steepness of a stock recruitment relationship. Remember that the steepness is commonly defined as the fraction of recruitment from an unfished population obtained when the spawning stock biomass is 20 percentage of its unfished level. The other point of view and possibility of this function is to compute the parameters of a stock recruitment model for which the corresponding steepness is equal to a desired value.</p>
<p>The <strong>arguments</strong> of this function are:</p>
<ul>
<li><strong>Pop.Mod</strong> is the object returned by Population.Modeling function (main function).</li>
<li><strong>Fish.years</strong> is the number of recent years to estimate the mean of SEL (selectivity).</li>
<li><strong>Bio.years</strong> is the number of recent years to estimate the mean of M, Mat, WC, and W (natural mortality, maturity, stock weight and catch weight).</li>
<li><strong>type</strong> is the desired approach which has two possibilities, type=“steepness” or type=“parameters”.</li>
<li><strong>h</strong> is the desired value of the steepness when type=“parameters”. In other case (type=“steepness”) this parameter is equal to NULL.</li>
<li><strong>Method</strong> is the procedure to obtain the age vector of weight (stock and catches), natural mortality, selectivity and maturity. By default is “mean” which means that the mean of the last “Bio.years” is used. The alternative option is “own”, the user can introduce these elements.</li>
<li><strong>par</strong> If Method=“own” it is a list containing the matrices whose columns report for each iteration the age vector of weight (stock and catches), natural mortality, selectivity and maturity. In other case is equal to NULL.</li>
</ul>
<p>The results of this function are:</p>
<ul>
<li><p>if type=“steepness” an array <strong>h</strong> whose third dimension is the number of iterations. For each iteration the value of the steepness is reported.</p></li>
<li><p>if type=“parameters” an array <strong>parameters</strong> whose third dimension is the number of iterations. For each iteration the value of the parameters of the stock-recruitment relationship.</p></li>
</ul>
<ol style="list-style-type: decimal">
<li>We focus on type=“steepness” first.</li>
</ol>
<ul>
<li>We consider the Beverton-Holt Recruitment Model:</li>
</ul>
<pre class="r"><code>a_BH=10000; b_BH=400; CV_REC_BH=0.2
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)
steepness_value<-steepness(Pop.Mod,Fish.years=3,Bio.years=3,type="steepness",Method="mean",par=NULL)
steepness_value</code></pre>
<pre><code>## , , 1
##
## [,1]
## [1,] 0.9311782
##
## , , 2
##
## [,1]
## [1,] 0.9266195</code></pre>
<ul>
<li>We consider the Ricker Recruitment Model:</li>
</ul>
<pre class="r"><code>a_RK=10; b_RK=0.0002; CV_REC_RK=0.2
SR<-list(type="RK",par=c(a_RK,b_RK,CV_REC_RK))
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)
steepness_value<-steepness(Pop.Mod,Fish.years=3,Bio.years=3,type="steepness",Method="mean",par=NULL)
steepness_value</code></pre>
<pre><code>## , , 1
##
## [,1]
## [1,] 2.340839
##
## , , 2
##
## [,1]
## [1,] 2.439506</code></pre>
<ol start="2" style="list-style-type: decimal">
<li>Now, we focus on type=“parameters”:</li>
</ol>
<ul>
<li>We consider the Beverton-Holt Recruitment Model:</li>
</ul>
<pre class="r"><code>a_BH=10000; b_BH=400; CV_REC_BH=0.2
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)
parameters_value<-steepness(Pop.Mod,3,3,type="parameters",h=0.93,Method="mean",par=NULL)
parameters_value</code></pre>
<pre><code>## $a_BH
## , , 1
##
## [,1]
## [1,] 10003.47
##
## , , 2
##
## [,1]
## [1,] 10007.79
##
##
## $b_BH
## , , 1
##
## [,1]
## [1,] 407.5046
##
## , , 2
##
## [,1]
## [1,] 417.2695</code></pre>
<ul>
<li>We consider the Ricker Recruitment Model:</li>
</ul>
<pre class="r"><code>a_RK=10; b_RK=0.0002; CV_REC_RK=0.2
SR<-list(type="RK",par=c(a_RK,b_RK,CV_REC_RK))
Pop.Mod<-Population.Modeling(ctrPop=ctrPop,ctrBio=ctrBio,ctrFish=ctrFish,SR=SR)
parameters_value<-steepness(Pop.Mod,3,3,type="parameters",h=2.34,Method="mean",par=NULL)
parameters_value</code></pre>
<pre><code>## $a_RK
## , , 1
##
## [,1]
## [1,] 9.99552
##
## , , 2
##
## [,1]
## [1,] 8.342268
##
##
## $b_RK
## , , 1
##
## [,1]
## [1,] 0.0001999709
##
## , , 2
##
## [,1]
## [1,] 0.0001888658</code></pre>
<p>Note that if we want to use <em>Method=“own”</em> we need to specify in argument <em>par</em> the required vectors. This must be something like (assuming that W, WC, M, Mat and SEL are defined previously).</p>
<pre class="r"><code>par=list(); par$W<-W; par$SEL<-SEL; par$Mat<-Mat; par$M<-M
steepness(Pop.Mod,3,3,type="parameters",h=2.34,Method="own",par=par)</code></pre>
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