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README.html
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README.html
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<!DOCTYPE html>
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<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<!-- README.md is generated from README.Rmd. Please edit that file -->
<h1 id="rconnect">rconnect</h1>
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<p>rconnect is a simple package that implements riverscape connectivity
measures for quantifying the colonization potential of wind dispersed
plants species as described by Wagner & Wöllner (2023):</p>
<p><em>effectiveSeedrain</em><br />
<em>effectiveConnections</em><br />
<em>effectiveDistance</em><br />
<em>colonizationPotential</em> <em>absoluteConnections</em>
<em>connectionCapacity</em></p>
<p>It further provides a helper function to create a negative
exponential dispersal kernel, functions to create a connectivity and
distance matrix based on effectiveConnections and effectiveDistance and
a simple example raster file with suitable habitats.</p>
<h2 id="installation">Installation</h2>
<p>You can install the current development version of rconnect from <a href="https://github.com/">GitHub</a> with:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># install.packages("devtools")</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>devtools<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"TCWagner/rconnect"</span>)</span></code></pre></div>
<h2 id="example">Example</h2>
<p>Here is a basic example how to work with the package and calculate
simple connectivity metrics of a riverscape.</p>
<p>Let’s find out how well the habitats of a riverscape (a riverine
landscape) are connected for a certain species. As example we chose the
Asteracea <em>Chondrilla chondrilloides</em>, an wind dispersed species
well adapted to open gravel bars of alpine rivers. For details about the
species ecology, dispersal and status, see Wöllner et al. 2022.</p>
<p>We start with a raster file, containing the suitable habitats for our
species. Suitable habitats need to have a value > 0; 0 codes for
unsuitable habitats. Here we use our example data that comes with our
package: <em>habitats_lech</em></p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(raster)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rconnect)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="do">## load the example raster with suitable habitats. suitable habitats need to be coded with values > 0</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="do">## you may use your own raster, here we use our example data:</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(habitats_lech)</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(habitats_lech)</span></code></pre></div>
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" width="100%" />
<p>Now we need the dispersal kernel for the species under consideration
as a matrix. We can create a simple, negative exponential dispersal
kernel with the function <em>dispersalKernel</em>. Assuming a negative
exponential decrease of the seeds with distance and a dispersal distance
of ~14m the decay is 0.19.</p>
<p>To create the kernel, we need to provide the cell size of our raster
containing the suitable habitats (5m), and the intended radius of our
kernel in cells. By default, the kernel center cell will be set to 0 and
the kernel normalized to sum up to 1.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>cckernel <span class="ot"><-</span> <span class="fu">dispersalKernel</span>(<span class="at">cellsize=</span><span class="dv">5</span>, <span class="at">radius=</span><span class="dv">7</span>, <span class="at">decay=</span><span class="fl">0.19</span>)</span></code></pre></div>
<p>Lets start to calculate the seed rain that can be exchanged between
neighboring habitats (<em>effectiveSeedrain</em>). If we do not
<em>summarize</em>, the output will be a raster with the relative
contribution that each cell of an habitat has.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>eS <span class="ot"><-</span> <span class="fu">effectiveSeedrain</span>(habitats_lech, cckernel, <span class="at">summarize=</span>F)</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(eS)</span></code></pre></div>
<img src="data:image/png;base64,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" width="100%" />
<p>Now, lets see how many <em>effective Connections</em> a patch has
with the other patches of the riverscape</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>eCM <span class="ot"><-</span> <span class="fu">effectiveConnectionsMatrix</span>(habitats_lech, cckernel)</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>eCM</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="co">#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]</span></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="co">#> [1,] NA 0.005021085 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.000000 0.00000000</span></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="co">#> [2,] 0.005021085 NA 0.3773102 0.00000000 0.00000000 0.0000000 0.0000000 0.000000 0.00000000</span></span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a><span class="co">#> [3,] 0.000000000 0.377310226 NA 0.06423960 1.45851127 0.0000000 0.0000000 0.000000 0.00000000</span></span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co">#> [4,] 0.000000000 0.000000000 0.0642396 NA 0.04449116 0.0000000 0.0000000 0.000000 0.00000000</span></span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a><span class="co">#> [5,] 0.000000000 0.000000000 1.4585113 0.04449116 NA 0.1585712 0.3685038 0.980203 0.03742227</span></span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a><span class="co">#> [6,] 0.000000000 0.000000000 0.0000000 0.00000000 0.15857125 NA 0.0000000 0.000000 0.00000000</span></span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a><span class="co">#> [7,] 0.000000000 0.000000000 0.0000000 0.00000000 0.36850376 0.0000000 NA 0.000000 0.00000000</span></span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a><span class="co">#> [8,] 0.000000000 0.000000000 0.0000000 0.00000000 0.98020300 0.0000000 0.0000000 NA 0.00000000</span></span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a><span class="co">#> [9,] 0.000000000 0.000000000 0.0000000 0.00000000 0.03742227 0.0000000 0.0000000 0.000000 NA</span></span></code></pre></div>
<p>However, if we want to have the total <em>effective Connections</em>
or <em>eC</em> that a patch has with all neighbors (that is the
connections weighted by distance) we can use:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>eC <span class="ot"><-</span> <span class="fu">effectiveConnections</span>(habitats_lech, cckernel)</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>eC</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co">#> patch eC</span></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 0.00000000</span></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 2 0.37731023</span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 3 1.90006110</span></span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 4 0.10873076</span></span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 5 3.04770271</span></span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 6 0.15857125</span></span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a><span class="co">#> 7 7 0.36850376</span></span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a><span class="co">#> 8 8 0.98020300</span></span>
<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a><span class="co">#> 9 9 0.03742227</span></span></code></pre></div>
<p>We may want to have this a a raster file that we can use for further
modeling. So lets set <em>summarize</em> to <em>FALSE</em>:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>eCr <span class="ot"><-</span> <span class="fu">effectiveConnections</span>(habitats_lech, cckernel, <span class="at">summarize=</span>F)</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(eCr)</span></code></pre></div>
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAYAAAB6jN80AAAEDmlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPpu5syskzoPUpqaSDv41lLRsUtGE2uj+ZbNt3CyTbLRBkMns3Z1pJjPj/KRpKT4UQRDBqOCT4P9bwSchaqvtiy2itFCiBIMo+ND6R6HSFwnruTOzu5O4a73L3PnmnO9+595z7t4LkLgsW5beJQIsGq4t5dPis8fmxMQ6dMF90A190C0rjpUqlSYBG+PCv9rt7yDG3tf2t/f/Z+uuUEcBiN2F2Kw4yiLiZQD+FcWyXYAEQfvICddi+AnEO2ycIOISw7UAVxieD/Cyz5mRMohfRSwoqoz+xNuIB+cj9loEB3Pw2448NaitKSLLRck2q5pOI9O9g/t/tkXda8Tbg0+PszB9FN8DuPaXKnKW4YcQn1Xk3HSIry5ps8UQ/2W5aQnxIwBdu7yFcgrxPsRjVXu8HOh0qao30cArp9SZZxDfg3h1wTzKxu5E/LUxX5wKdX5SnAzmDx4A4OIqLbB69yMesE1pKojLjVdoNsfyiPi45hZmAn3uLWdpOtfQOaVmikEs7ovj8hFWpz7EV6mel0L9Xy23FMYlPYZenAx0yDB1/PX6dledmQjikjkXCxqMJS9WtfFCyH9XtSekEF+2dH+P4tzITduTygGfv58a5VCTH5PtXD7EFZiNyUDBhHnsFTBgE0SQIA9pfFtgo6cKGuhooeilaKH41eDs38Ip+f4At1Rq/sjr6NEwQqb/I/DQqsLvaFUjvAx+eWirddAJZnAj1DFJL0mSg/gcIpPkMBkhoyCSJ8lTZIxk0TpKDjXHliJzZPO50dR5ASNSnzeLvIvod0HG/mdkmOC0z8VKnzcQ2M/Yz2vKldduXjp9bleLu0ZWn7vWc+l0JGcaai10yNrUnXLP/8Jf59ewX+c3Wgz+B34Df+vbVrc16zTMVgp9um9bxEfzPU5kPqUtVWxhs6OiWTVW+gIfywB9uXi7CGcGW/zk98k/kmvJ95IfJn/j3uQ+4c5zn3Kfcd+AyF3gLnJfcl9xH3OfR2rUee80a+6vo7EK5mmXUdyfQlrYLTwoZIU9wsPCZEtP6BWGhAlhL3p2N6sTjRdduwbHsG9kq32sgBepc+xurLPW4T9URpYGJ3ym4+8zA05u44QjST8ZIoVtu3qE7fWmdn5LPdqvgcZz8Ww8BWJ8X3w0PhQ/wnCDGd+LvlHs8dRy6bLLDuKMaZ20tZrqisPJ5ONiCq8yKhYM5cCgKOu66Lsc0aYOtZdo5QCwezI4wm9J/v0X23mlZXOfBjj8Jzv3WrY5D+CsA9D7aMs2gGfjve8ArD6mePZSeCfEYt8CONWDw8FXTxrPqx/r9Vt4biXeANh8vV7/+/16ffMD1N8AuKD/A/8leAvFY9bLAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAKgoAMABAAAAAEAAAHgAAAAABJf29wAAEAASURBVHgB7d0JmBTVtcDxM+wg+y6gCLIZwyqrQUFAEJCAiaABFwLKovBigoRPlEQlbCIJQSWsgiBBEyB5UXZExIAYg7JEEYEABkwQBCQssvarc5Pq193TS830dHXV9P9+39DdVbfqVv2q6Dlzt8oKWElICCCAAAIIIIAAAgi4JFDApXIoBgEEEEAAAQQQQAABI0AAyo2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBAAMo9gAACCCCAAAIIIOCqAAGoq9wUhgACCCCAAAIIIEAAyj2AAAIIIIAAAggg4KoAAair3BSGAAIIIIAAAgggQADKPYAAAggggAACCCDgqgABqKvcFIYAAggggAACCCBQCILcCXz/+9+XN998UwoXLpy7HbAVAggggAACCORKoFChQvLJJ59ItWrVcrU9G6VfICtgpfQfhv+O4KqrrpLRo0dLlSpV/HfwHDECCCCAAAI+Fnj44Yfl9ddflz59+vj4LDL70KkBzeX1L1CggFSqVEkeeuihXO6BzRBAAAEEEEAgNwLDhw/PzWZs4yEB+oB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIEIBmwlXmHBFAAAEEEEAAAQ8JEIB66GJwKAgggAACCCCAQCYIFMqEk8zJOZ49e1aOHz+ecJPLly/LN998kzAfGRBAAAEEEEAAAQTCBQhAwz3kzTfflBEjRkQszf7x3Llz8tFHH2VfwRIEEEAAAQQQQACBuAIEoBE8ffr0Ef1JlAoWLChVqlRJlI31CCCAAAIIIIAAAhEC9AGNAOEjAggggAACCCCAQGoFCEBT68veEUAAAQQQQAABBCIECEAjQPiIAAIIIIAAAgggkFoBAtDU+rJ3BBBAAAEEEEAAgQgBAtAIED4igAACCCCAAAIIpFaAADS1vuwdAQQQQAABBBBAIEKAADQChI8IIIAAAggggAACqRUgAE2tL3tHAAEEEEAAAQQQiBAgAI0A4SMCCCCAAAIIIIBAagUIQFPry94RQAABBBBAAAEEIgQIQCNA+IgAAggggAACCCCQWgEC0NT6sncEEEAAAQQQQACBCAEC0AgQPiKAAAIIIIAAAgikVoAANLW+7B0BBBBAAAEEEEAgQsD3AWggEJCjR4/K8ePHI06NjwgggAACCCCAAAJeFPBlAHr48GEZNWqUXHfddVKkSBGpXLmyVKhQQcqUKSONGzeWESNGyOnTp73ozTEhgAACCCCAAAIZL1DIbwIHDx6Utm3bSlZWlvTu3Vtq164t5cuXN5+1FnT//v2yZMkSWbp0qbz11lty/fXX++0UOV4EEEAAAQQQQCBfC/guAJ08ebKp+Vy3bp0ULVo06sUZP368dO3aVRYsWCDPPPNM1DwsRAABBBBAAAEEEEiPgO+a4Ldt2yYPPPBAzOBTGQsXLiz9+/eXVatWpUeVUhFAAAEEEEAAAQRiCvguAL355ptl06ZNMU/IXrF+/XqpXr26/ZFXBBBAAAEEEEAAAY8I+K4Jvm/fvqJB6JEjR6Rfv36mj6cOQCpQoIAZCX/gwAFZtGiRrFixQrSZnoQAAggggAACCCDgLQHfBaBNmjSRHTt2yODBg+XBBx+UK1euZBPt1KmTrFmzRtq1a5dtHQsQQAABBBBAAAEE0ivguwBUuerUqWNGuF+4cEE+//xz0VrPixcvSrVq1aRGjRpmSqb0slI6AggggAACCCCAQCwBXwag9snoHKA6zZLO/1mwYEEzHZO9jlcEEEAAAQQQQAABbwr4bhCSMjIRvTdvJo4KAQQQQAABBBBwIuC7GlAmondyWcmDAAIIIIAAAgh4V8B3ASgT0Xv3ZuLIEEAAAQQQQAABJwK+a4JnInonl5U8CCCAAAIIIICAdwV8F4AyEb13byaODAEEEEAAAQQQcCLguyZ4JqJ3clnJgwACCCCAAAIIeFfAdwEoE9F792biyBBAAAEEEEAAAScCvgtA9aSYiN7JpSUPAggggAACCCDgTQFfBqA2pU5Er8Go/mjSx3Lu37/fTExfqJCvT80+RV4RQAABBBBAAIF8J+C7QUh6Bf72t7/JkCFDZMCAAfLOO++YizJlyhSpWrWqCUbLlSsns2bNyncXixNCAAEEEEAAAQTyg4Dvqgk1+GzRooVo7WfFihXltddek1//+tfy9NNPy7333isdO3aU3//+9zJ06FCpVauW3H777fnhOnEOCCCAAAIIIIBAvhHwXQ3oxIkTpWnTpvLFF1/I3r175dFHH5VBgwbJiBEjZPbs2SYIXbp0qdxxxx0yffr0fHOhOBEEEEAAAQQQQCC/CPguAP3ss89Ep2K66qqrJCsrS+6//35zLXr37h12Te6++275+9//HraMDwgggAACCCCAAALpF/BdAFqtWjVZv359UM5+v2HDhuAyfaNN9TVq1AhbxgcEEEAAAQQQQMCvAoFAIFeHntvtclWYw418F4Dq4KM//OEPph9o165d5ac//an85Cc/kXHjxpm+oB9//LE8//zz5r32CSUhgAACCCCAAAJ+FtAWXY11rr32WjPgWlt/v/7664Sn9Morr0j79u2lRIkS0rJlS3n77bcTbuNWBt8FoNq383e/+50ULVrU4Otodx0B36FDB/nxj38s3/72t01Q+qMf/SjYPO8WJuUggAACCCCAAAJ5LfDDH/5QNm/ebMa6zJgxQ7Zu3SqdO3eOW8zGjRvNGJk+ffrIli1bTMVdt27dZPv27XG3c2tlllUtm7v6XLeOMAfl/POf/5QPP/zQBKE1a9bMwZY5z1qwYEEZOXKk6KAoEgIIIIAAAgi4J1C8eHHR2j0NrvJ7WrNmjXTp0kU++OADad68uTnd119/3Qy61nExdevWjUrwrW99S5o1ayavvvpqcH3Dhg1NTejcuXODy9L1xnc1oPGgSpcuLY0aNZJUB5/xjoF1CCCAAAIIIJA/BZYtW2YGQJcsVVJi/ZQtW1bOnz+fZwA6naQ2wdvBp+74X//6l9l/4cKFo5Zz6NAh2bVrl9x1111h63v27CkrVqwIW5auD76bBzQelN4Y2gx/7NixeNlYhwACCCCAAAII5FhABz5f1SdLit18Nua2p35aUM6dO2e6CsbMlIMVOuOPzmuu6dSpU7J69WqZPHmy3HnnnXLdddeZ5ZH/6DSVmqpXrx62SgdyHz161Dw5skCB9NZB+i4A1QFGBw4cCAO1P+zevVvOnDkjw4YNM4u06lmflkRCAAEEEEAAAQTyQiCroEjBq7Ji7urypSumi55OFxkvaYzywAMPxMuSbZ0+bOevf/2rlC9fXn7zm99kW28v0EBVU4UKFexF5lWfFHn58mX56quvpFKlSmHr3P7guwBUO97q048UMTKyP3nypFy8eDH4eE4dqJTT9Mknn4j2t0iUtOvs6dOnE2VjPQIIIIAAAgjkN4HY8aepXdSaRg0S46XcBIBz5syRw4cPm8HXjRs3NvHKTTfdlK2YQoX+E95F1nLany9cuJBtG7cX+C4AXbRokennOX78ePOXgz4ByQZduHChaYLfuXNnrh01qDx48KCj7fWvCBICCCCAAAIIZI6A1SJu9QONf77a+pqK8SgadOrPd77zHTMdkw4wihaAVq1a1RzgiRMnwg7U/lyqVKmw5en44LsAVIPNJ554wkw/0K9fP3nzzTdlwYIFeXahdZ4s/UmUpk2bJmXKlEmUjfUIIIAAAgggkJ8ETACaIALNw/P9/PPPRX/atm0b3KvGH61btzbTKwUXhryxA9AjR46ELBXRz1ozq4O2053S2wM1ibPXiF+nXLrxxhtNjahOx0BCAAEEEEAAAQTyk8C8efPMZPL79+8PnpZOQv/+++9L7dq1g8tC31x99dXSoEEDWblyZehiMwK+Xbt2YcvS9cG3AaiC6cz+06dPl9/+9rdm8vkxY8aky5FyEUAAAQQQQCADBLTus4D1T6yfvCbo27evGVGvT0Las2ePaDfDQYMGmTEvjz32mClOg9OHHnpI/vznP5vPOnJeB2TPnz9fli9fbkblT506VXbs2CGTJk3K60PM1f581wQf7Sy7d+9uLog+/UinFyAhgAACCCCAAAKpEDB9QF2svtOJ5vUR5Bp01qtXz5zSNddcIzr1ZIsWLcxnjX10cvk2bdoEm+oHDx5sAtZevXqZke9aI6pPUYo1cX0qrOLtM18EoHqCJUuWlMcff9z0a9DBQfqkIhICCCCAAAIIIJCXAk4GIeVlebovfeym1nJqX1AdC6MBaGjSsSuRD7bUkfBa66lPbPzyyy/Nc+RDt0n3exdj+Lw5VX3ue2RT+4QJE0ynWn1KgP51UL9+fUdTKeXNEbEXBBBAAAEEEMgoAW2Hj/WTIghtVteR9ZHBZ6LiihUr5rngU4/ZdzWg2n8htJldO+eOHj1adHJW7Sehk68uXrxYtMp58+bN0qRJk0TXhvUIIIAAAggggIAjAa0B1f6fpOQEfBeARp7u7NmzzVQE69atC67STrn6hAGtetYOuCQEEEAAAQQQQCAvBAhA80LRCuLzZjfp24s+/ei+++7LdgAPP/ywbN++PdtyFiCAAAIIIIAAAgikV8CXNaDnzp0z0w8ULlxYevbsaR5LFcmoj+ysWLFi5GI+I4AAAggggAACuRbQ1nea4HPNF9zQdzWg2gl3w4YNoo+R0lFfGmjqU4k++ugjc1KHDh2SoUOHij6Ws0+fPsET5Q0CCCCAAAIIIJCsgDbBx/tJdv+Zsr3vAtBZs2aZYFPnstL5rs6fPy861cC+ffvMNdMJV3WdNsEPHDgwU64j54kAAggggAACLgiYPqBW9GTNhhT1x4VDyBdF+K4JvkiRImZku45u79+/f/AiXLp0ybzv0aOH6MT0NWrUCK7jDQIIIIAAAgggkCcC/60BzZN9ZfBOfBeAhl4rnXRVm+Q1aS2opmrVqplX7SeqqXjx4uaVfxBAAAEEEEAAgWQFtOmYPqDJKlqGye/C/T0sWLBAatWqJTq5qjbDb9q0KdtB6JygDz74YLblLEAAAQQQQAABBHIrYAYhWRubQDTKa273m2nb+S4AXbt2rQks9WkAI0eONJPS33rrrfLiiy9m2rXjfBFAAAEEEEDAZQHTB9SKQrUWNNqPy4fj2+J81wQ/c+ZM6dKli6xatcqgjx07Vn7+85/L8OHDzch4aj19ey9y4AgggAACCHhewNSA6j9pSleuXDHPg09T8XlWrO9qQA8ePCh33nlnEED7gD777LPmcZw68j30iUjBTLxBAAEEEEAAAQTyQEBrQOP95EER2XbxySefSLdu3aR06dJSokQJad68uWiLcLx05swZueGGG6RBgwZhPxoreSH5rgZUBxmtX79ehg0bFuY3btw4+cc//iF33323bNy4MWwdHxBAAAEEEEAAgbwQME3wLlbfHT9+XG6//XYzyFqnotSH7Oj85xqQbtmyRW666aaop7Vz50759NNPZcSIEVKyZMlgnjp16gTfp/ON7wJQHVykj97UCP6RRx6Rpk2bBv1efvllE4C2a9dOKlSoYJ4HH1zJGwQQQAABBBBAIEkBE4C62AT/xz/+Ub744gvR1xYtWpijb9WqlQlI58+fHzMA3bFjhwk8J0+eHJwxKMlTz9PNfReA3nPPPfLZZ5+J1njq05BCA1Cdiun111+XIUOGiF6UZs2a5SkWO0MAAQQQcFfg5MmT2QosW7ZstmUsQMAtAdMH1K3CrHI0zpk+fXow+NSiNf7RmtBo/z/sQ9MAtHHjxib4DJ220l6f7lcXK5Hz7lTHjBlj0H/6059m22nRokVl3rx5plpaa0pJCCCAAAIIIIBAXgno2JMC1vD3WD95VY69Hw1A9RHjoWnz5s1y4MABad26dejisPcagBYuXFgGDBgg5cqVE5096PnnnxcNRr2QfFcDaqPpHKBVq1a1P8rZs2dl165dpoNu7dq1RaunSQgggAAC6RE4umJxzIIrdftBzHWsQMDrAlpzVzBBE/zgwYPNYKF459K2bVv5yU9+Ei9L1HU6uOjRRx+VunXrykMPPRQ1jy7UAPTixYui5Tz33HMyd+5cM32lPsL8ySefjLmdWyt8F4BOmTLF1H7q9Et2mjBhgjzzzDPmufC67PrrrzfV1Z07d7az8IoAAggggAACCCQtoLFngvhT7rrrLqlcuXLcsnLzyPDTp0/Ld7/7Xdm3b59s2LBBtNU3WtLHk0+dOlXq169vHtijeQYNGiQ6Rka7MOrAJK3IS2fyXQCqEf3Ro0eDZtrcPnr0aOnYsaPoAKVTp07J4sWLpVevXqJV1PrMeBICCCCAAAIIIJAXAk4GId1xxx2myTsvyrP38fXXX5t50Hfv3i2rV6+OO85Fx8T079/f3jT42rt3bzNTkO5D+4emM/kuAI3Emj17tukDETr/52OPPWYujEb/OhiJhAACCCCQOoF4ze3RSrXzO2mKZ8BRNEGWpVNAn35UyOURNFq5pq26Ohf622+/nbByTbsl7tmzR3TKpauuuirIpYOXNBUo4PIJBI/g/9+k/wj+/1hy9U5HgEUbbKTTNG3fvj1X+2QjBBBAAAEEEEAgmoBdAxrtMZy6LK+TDhrSB/AcOnRI3n333YTBp5a/d+9eky/yMeXLli0zI+i1/2i6ky9rQM+dO2c61urorp49e8rhw4ezOW7dutVMUZBtBQsQQAABBHItYNde5noHbIiAzwU0xkxFoBmLRVtyNfDUR45/8MEH5sfOqw/nad++vezfv9/07dRmdx101LBhQzNtk05Yr10RdVpKfZS5Nt1rt8V09//U4/ddAKrTH2jHW61GbtSokWjzjPb11H4NOlWB/oWgHWwXLlwokZG/fcF4RQABBBBAAAEEciNg14DmZtvcbKMP2dH0wgsvmJ/QfXTp0sUEoDo2Rke5t2nTxgSgGistXbpUBg4cKNofVZM+Dempp57yxAh4PR7fBaD6GCrt47lt2zb56KOPzKt2ttURYRqALl++XGbMmGGmKFB4EgIIIIBA8gLJ1nw66e+Z/FGyBwRSL1DQKqJQCpraYx251n4mSi1btsw2v+c111wja9asMTMHHTt2THSKSi/0/bTPxXcBaJEiRUx1slYph47w0ikHNPXo0UO6d+8uuZnewEbhFQEEEEAAAQQQiCZgmuCjrfDoMm0p9uJgPt8FoLGur9aCatL+ECQEEEAAAQQQQCAVAm43wafiHLywz3wTgHoBk2NAAAEEEPiPAE3u3An5VSAd0zDlR0sC0Px4VTknBBBAAAEEEEiJgNaA6g8pOQEC0OT82BoBBBDI1wJOBh9R25mvbwFOLkJAJ1BP9Cz4iE34GEWAADQKCosQQAABBBBAAIFoAqYJnirQaDQ5WkYAmiMuMiOAAAL5X8BJracqUPOZ/+8FzjC7gN9GwWc/A28sIQD1xnXgKBBAAAEEEEDABwLaBO/mPKA+IMnVIRKA5oqNjRBAAAEEEEAgEwW0CZ4+oMlfeQLQ5A3ZAwIIIIAAAghkiIAZBa/VoKSkBAhAk+JjYwQQQAABBBDIJIH/DELKpDNOzbkSgKbGlb0igAACCCCAQD4USHcf0CtXruT4me6BQMCau9Rbk5dSiZwP/3NwSggggAACCCCQGgF7FLwGUNF+UlHqJ598It26dZPSpUtLiRIlpHnz5rJ27dqERb3yyivSvn17s03Lli3l7bffTriNWxkIQN2SphwEEEDAJwI6vZL9E3nI9nKmYIqU4XOmCOgAJB0FH+snrx2OHz8ut99+uxw9elRmzZolb775plSrVs0EpFu3bo1Z3MaNG2XQoEHSp08f2bJli7Ro0cJss3379pjbuLmCJng3tSkLAQQQQAABBHwtUECyrODTvebsP/7xj/LFF1+IvmoQqalVq1YmCJ0/f77cdNNNUT2HDBkivXv3lkceecSsf+mll0SD0mnTpsncuXOjbuPmQgLQCO0VK1bI+PHjI5Zm/6h9MI4dO5Z9BUsQQAABBBBAIN8KaOypA5HcSk2bNpXp06cHg08tt1SpUlKxYkU5efJk1MM4dOiQ7Nq1S8aOHRu2vmfPnp4IPvWgCEDDLo2YfhWTJk2KWJr94y233GL6YmRfwxIEEEAg/wjQ1J5/riVnkjcCpgnexQ6MGoDqT2javHmzHDhwQB5//PHQxcH3e/fuNe+rV68eXKZvtOlem/JzM5ApbEd58IEANAKxcuXKoj+Jko4mK1KkSKJsrEcAAQQQQACBfCTgZBqmO++8U4oWLRr3rDt37uyoxTVyJ2fOnJFHH31U6tatKw899FDkavP51KlT5rVChQph68uVKyeXL1+Wr776SipVqhS2zu0PBKBui1MeAggg4FGBl3YUi3lkjzb6JuY6ViCQSQJa+ZnoUZwTJkyQq6++Oi5L+fLl466PtvL06dPy3e9+V/bt2ycbNmyIGeQWKvSf8K5AgfCqWvvzhQsXou3e1WUEoK5yUxgCCCCAAAII+FlAu3+Gh3XZz6Zhw4ZSs2bN7CuSWPL1119Lly5dZPfu3bJ69Wpp1qxZzL1VrVrVrDtx4kRYHvuz9iFNdyIATfcVoHwEEEAgzQLxaj7tQ4uWh1pRW4fXTBLQPqCFXRyEpLbapK5N9gcPHjRzeTZp0iQuuR2AHjlyJCyfftaaV51PNN0pURCf7uOjfAQQQAABBBBAwDMCBf87DZNOxRTtJ68PVJ9ipH1KdWT7u+++K4mCTy1fm/8bNGggK1euDDscnemnXbt2YcvS9YEa0HTJUy4CCCCAAAII+E7A7WmYdK5PDTyHDx8uH3zwgfmx0XRUuz7paP/+/TJu3Djp37+/tG3b1jx2c9iwYTJq1Cjp2rWrdOjQQWbOnCk7duyQV1991d48ra8EoGnlp3AEEEDAvwKRzfJOm+Rzu51/pTjy/CRgRsG72H788ssvG74XXnhB9Cc0aZ9QDUB1aiWdXL5NmzYmANU8gwcPlj179kivXr3MyHetEZ0xY4YZPR+6j3S9JwBNlzzlIoAAAggggIDvBLQPaBEX+4Bq7WeipM9516b60KQj4adOnSoTJ06UL7/8Uq699trQ1Wl/TwCa9kvAASCAAALuCUTWPuZlybndd+h2TmtR8/K42RcCORHQyk8XK0BzcmhR8xYrVsxzwaceKAFo1MvFQgQQQAABBBBAILuABp+J5gHNvhVLIgUIQCNF+IwAAgjkE4HQmkUvnxK1nl6+OhxbpMB/noTkYht85AHkk88EoPnkQnIaCCCAAAIIIJB6Aa0B1X6gpOQECECT82NrBBBAAAEEEMgggXRMRJ8feQlA8+NV5ZwQQCCjBfzS9J7RF4mT962ACUD9NArJo9IEoB69MBwWAggggAACCHhPQFvfiT+Tvy6+D0B13qtjx45JwYIFzfNNkydhDwgggIA/BfxS88mgI3/eXxz1fwQKWo9CKqKPQyIlJeDLIP7w4cPm8VLXXXedFClSRCpXriwVKlSQMmXKSOPGjWXEiBFy+vTppGDYGAEEEEAAAQQQiBQoaC3QaZhi/UTm53N0Ad/VgB48eDD4nNPevXtL7dq1Tc1nlvXXyPHjx83zUJcsWSJLly6Vt956S66//vroZ85SBBBAAAEEEEAghwJa+cko+ByiRcnuuwB08uTJojWf69atk6JFi0Y5JZHx48dL165dZcGCBfLMM89EzcNCBBBAAAEEEEAgpwJaA1rYCkJJyQn4rgl+27Zt8sADD8QMPpWjcOHC0r9/f1m1alVyOmyNAAIIIIAAAgiECNjTMGkQGu0nJGtK3l6+fFkuXLiQkn27uVPf1YDefPPNsmnTJnn44YfjOq1fv16qV68eNw8rEUAAAb8K+GXAkV99OW4EYglozZ3WgqYjXblyRXr06GHGvsyfPz/uIZw5c0aaN28uOlg7NN1yyy0ye/bs0EVpee+7ALRv376iQeiRI0ekX79+po+nDkAqUKCA6QN64MABWbRokaxYscI006dFlUIRQAABBBBAIF8KpGse0PPnz8vw4cNl5cqV8uCDDya03blzp3z66admYHbJkiWD+evUqRN8n843vgtAmzRpIjt27JDBgwebC6B/DUSmTp06yZo1a6Rdu3aRq/iMAAII+E7Az7WdTLnku9uNA04goAFoEZf7gG7dulXuv/9++ec//2lqPxMcolmtsZIGnjp2Rgdqey35LgBVQI3edYS79oH4/PPPRWs9L168KNWqVZMaNWqYKZm8Bs3xIIAAAggggID/BUwTvMvx3Jw5c0y3wuXLl4u2BDtJGoDq1JQafGozvNeCUF8GoDa8zgGq0yzp/J9MRG+r8IoAAggkJ5DTWku7hjan2yV3lGyNQHoETAAacDcCffrpp6VKlSo5OmENQHVQ9oABA2TZsmUmVtImfJ0r3QvBqO9Gwas+E9Hn6B4kMwIIIIAAAgjklcBlK/i8ZIVPsX6scpo1a2YCRg0aY/1oV0KnKafBp+5XA9C//OUvpub0ueeek6pVq8rIkSPNVJVOy01lPt/VgDIRfSpvB/aNAAIIIIAAAnEFrljB58X49Xdr1641XQLj7adYsWLxVie17tKlSzJ16lSpX7++tGnTxuxr0KBBZmzMuHHjTC1oKst3cvC+C0CZiN7JZSUPAghkooDdBO52k7hdrlPz43s/C8tavk69sM98QMDTAjr2+Ur8JnidnUcfE56uVKhQITMfemT5+gTJjRs3yu7du03/0Mj1bn6OH8K7eSQOy2IieodQZEMAAQQQQACBvBfQ/p/aDB/rJ+9LzPEez549K9u3bxedCzQ0lSpVynzUqSvTnXxXA8pE9Om+ZSgfAQTcFgitYXRSuxmaPzfHWmm2s2m2jz58OTe7ZxsE/C1g9wH18Fns3btXdNrKiRMnyqhRo4JHqoORNAitW7ducFm63vguAGUi+nTdKpSLAAIIIIAAAmI9WCgrQRO820r79+8X7dupjyFv27atNGzYUFq0aCHTpk0zgagOipo5c6asXr1aRo8eLenu/6k+vgtAmYje7dua8hBAwEsCua3ddFqrmZNzjdyn0xpR+nzmRJm8nhMIWM3Xl9PfhB3qcvToUZk7d64ZcKQBqE6ztHTpUhk4cKDccccdJqtOSv/UU0/Jk08+Gbpp2t77LgBVqVgT0WtEX7t2balZs2baQCkYAQQQQAABBPKxQJqb4N97771suC1btsz2zPdrrrnGPBXy5MmTcuzYMRMfeaHvp33w3grh7aNK8KrzWt15552iz0XVYPTcuXMydOhQ6dChg5mY/qabbpJ33303wV5YjQACCCCAAAII5EJAByLF+snF7lK5SdmyZU2s5KXgU8/XdzWg77//vqli7tKli7leW7ZskbvuustMJzBs2DApWrSoLFmyRDp37mwi/1tuuSWV15V9I4AAAmkRiGz+TstBxCnUPj6nzfJxdsUqBLwlYDW/By45G6jnrQP31tH4LgBdvHixqf3805/+ZCRnzZplZvfXwFTnvdKkj5rq1KmTzJ8/XwhADQn/IIAAAggggEBeCGjNp05GT0pKwHcB6F//+lfRkfB20r4NPXv2DAaf9nLN89JLL9kfeUUAAQR8J2DXIvrlwKMdb7Rl1Ir65YpynNEEsiRLCmgQSkpKwHchfKNGjeT3v/+9fPPNN+bE27dvL1obevny/89HFwgEZOXKlXLDDTckhcPGCCCAAAIIIIBAmIDWfl62muBj/YRl5kMsAd/VgOqEqjrIqFWrVvLEE0+ITkzfoEED83zTH/7wh2aC1UWLFpm5rjZt2hTrvFmOAAIIuC7gtDYwWj7XDzaiwJzWWtrnkNPtIorlIwKeEwhYtZ8BmuCTvi6+C0B1iiV9jukvfvELue+++8JqPu2As2nTpqZWVANVEgIIIIAAAgggkGcC9AHNE0rfBaB61t/61rfkt7/9rbzwwguis/8fPnxYvv76a6lWrZrovFf169fPExx2ggACCCCAAAIIhAlo7ecVRsGHmeTigy8DUPs8K1SoIPrTvHlzs+jMmTNy/PhxezWvCCCAQFoF7GboeAfhJE+87b26jqZ3r14ZjitpAWpAkybUHfhuEFK8s162bJlo8zsJAQQQQAABBBBIhUCWFYAWsGpBY/2kosz8uE/f1YA+//zzcuDAgajXYvfu3aK1oDohvaZmzZrJgAEDouZlIQIIIJAKgZzWaEarKczpPlJxHuwTAQRiCOiz4GmCj4HjfLHvAtCtW7fKa6+9JuXKlZPq1auHnanOCXrx4kV55513zHJ9KlJOkz7WU/fjJF25csVJNvIggAACCCCAQH4R0FHwgfT1AdVpJ/WnSJEijkV1esqsLG/NXeq7AFSnWNK5QMePHy8PPPCAjBgxQuznmy5cuFB+/OMfy86dOx1flMiMOqeo7iNR0uDzyy+/TJSN9QgggEC+ELBrZaPV2OaLE+QkEHAqkMY+oBp79OjRQypXrmye9pjokF955RWZN2+e6NMiGzZsKJMmTZLbbrst0WaurPddH1ANNnX+zw0bNsjcuXMN5MGDB/MM65577pEvvvgi4Y8eR9WqVfOsXHaEAAIIIIAAAn4QsEInbYaP9ZOiUzh//rwMGTLEPGjHSRE6ZeWgQYOkT58+smXLFmnRooV069ZNtm/f7mTzlOfxXQBqi+gcnx9++KHceOONpkZUo3wSAggggAACCCCQUgHTB9RqQL4S4ycFhWv3Qx1krU+C1NpPJ0mD1d69e8sjjzwijRs3No8nr1OnjkybNs3J5inP47sm+FCREiVKyPTp06V79+5msFHx4sVDV/MeAQQQcCxwfOc2k7d8wyaOt0kmY2RTtt3Encw+U7lt5PGmsiz2jYCXBbLMCHh3+4DOmTPHjHtZvny59O3bNyHPoUOHZNeuXTJ27NiwvD179jStx2EL0/TB1wGobaYBqPb7/NGPfiRHjx61F/OKAAIIIIAAAgjksYAO5onfgHzhwgXRJvN4qWDBglKokLMw7Omnn5YqVarE213Yur1795rPkYO19YE9GidpX1J7/EzYhi5+cHbmLh5QbosqWbKkPP7441K6dGkzOkwvLAkBBBDIqUAqa0Kj1SJS85nTK0R+BNItYAWg2gwfJ2n3wEQBnjaP6+BpJyknwafu79SpU2a3+rCe0KQzCOkI+q+++koqVaoUusr19/EFXT+cxAVOmTJFxowZE5ZxwoQJUr58efNEpHr16plHca5ZsyYsDx8QQAABBBBAAIFkBQJW8BkIFIr5o/vfs2ePfPPNN3F/nAafuTleu2Y1Mgi2P2sNbbqT72pAd+zYEdbMrtMLjB49Wjp27Gj6RWjUv3jxYunVq5ds3rxZmjRxpz9Xui8k5SPgBQG7Ni9aTZ8Xji/eMeS276d9ztH2Hc8h3nbR9uXmsnjH7eZxUBYC3hRIXAOa7uO2Z+k5ceJE2KHYn0uVKhW2PB0ffBeARiLNnj1bWrduLevWrQuueuyxx8xTkKZOneponqzghrxBAAEEEEAAAQTiCmjjsbe7+dkB6JEjR8LORD9ri7F2V0x38l0TfCSYPrXovvvui1wsDz/8sGfmusp2cCxAAAEEEEAAAV8K/OcZ8AWtZ8FH//HCSV199dXSoEGDbHOGrlixQtq1a+eFQxRf1oDq4zL1kZuFCxcWnVLg8OHD2TB1zqyKFStmW84CBBDIG4F4Tcjx1rndvJuKY4m3z2i6Oc0fbR9uLHP72rhxTpSBQN4LJB4Fn/dlxt/j/v37Zdy4cdK/f39p27ateezmsGHDZNSoUdK1a1fp0KGDzJw5U7Qb46uvvhp/Zy6t9V0Aqs8y1acgaf8FfSRn2bJlTV9PHU2mk7Tq3Fd6EbRz74svvugSI8UggAACCCCAQGYIWI3H1iAkLyWdWkmfDtmmTRsTgOqxDR482AyG0jExOvJda0RnzJghdevW9cShe0vQAcmsWbNE+3hu27ZNPvroI/Oqo7327dtnAlCdpFWBH330URk4cKCDPZIFAQScCORVLV7ofnJS43Z0xeKYh1mp2w9irou3IvRYIvOFHlu8fJHbhX7O7Xah+0jV+9DzS1UZ7BeB/Cmg/T/TFz6999572VhbtmxpjcoPhC3X2EjHwkycOFG+/PJLufbaa8PWp/tD+gRzeeZFihQxI9t1dLtWNdvp0qVL5m2PHj3Mk5Fq1Khhr+IVAQQQQAABBBDIQwFthvdHKlasmOeCT5XzXQAaerk12tcmeXMi/32agM7yr0n7iWri8ZyGgX8QyJWAl2rwQms549WG2iea7LHndPtoNYo53Yd97Kl8jXacqSyPfSOQ/wS8PwreD+a+HAW/YMECqVWrlmhUr/0dNm3alM1an5X64IMPZlvOAgQQQAABBBBAIPcCBSTLqr+L9ZP7/WbWlr4LQNeuXWsCy5o1a8rIkSPNpPS33norA44y677lbBFAAAEEEEiTgD0KXkOoaD9pOiyfFeu7JnidRqBLly6yatUqQz127Fj5+c9/LsOHDzcj46n19NkdyOGmRMBJ02+8plgn26fkwB3uNLQ5PnITLx27beyFY7KPJdKLzwggkFOB9A5CyunRejW/7wLQgwcPhjWtax/QZ5991kwxoJPPV69eXTp16uRVb44LAQQQQAABBHwtoLWevgufPCfuO0EdZLR+/XrRCVZDk879+Y9//EPuvvtu2bhxY+gq3iOQrwVyW7uW2+28hJkfziEVntR2pkKVfSJgC9hN8PZnXnMjoGG8r5IOLnrjjTfMozZ1HtDQ9PLLL0v79u3NY6Z27twZuor3CCCAAAIIIIBAHghYoVOW1Qwf6ycPSsiEXfiuBvSee+6Rzz77zDztSJ+GpE8/spNOuvr666/LkCFDZP78+dKsWTN7Fa8I5DuBTK798+K5RzsmN2oi3Sgj3/3n4YQQSEIgyxp4pCPgSckJ+FJwzJgxZgT8yZMns5190aJFZd68eSYIPXLkSLb1LEAAAQQQQAABBHItoPOPZ/muATnXp5uqDX0ZgCqGzgFatWrVoMvZs2dl165dUrp0aaldu7a0atUquI43CCCAAAIIIIBAnggErOb3QOE82VUm78R3AeiUKVNEaz51+iU7TZgwQZ555hk5f/68WXT99dfL9OnTpXPnznYWXhHIFwLRmnn9fGKR55OK5uTQfUaWl2o7t8tL9fmwfwQQsAQKWAFogfSFT6FPgfTz9fBdHfKOHTtk69atQXNtbh89erS0bdtW5s6dK7/61a+kQoUK0qtXL9m2bVswH28QQAABBBBAAIHkBXQQkhWAxvpJvoBsezh9+rSMGjVK6tatK+XLl5fvfe978tVXX2XLF7rgzJkzcsMNN0iDBg3CfnTKSi+k9IXweXT2s2fPltatW8u6deuCe3zsscfMAKSpU6eawUjBFbxBwKcCmVKTFu88Q2syQ98nuqTx9ploWy+vz4mBl8+DY0PAdwI6C5P2A3UxaUXbypUrRR/GU6RIEfPwnY4dO4rOBqTzoUdLOhvQp59+KiNGjJCSJUsGs9SpUyf4Pp1vfB+AanP8o48+ms1QI/xZs2ZlW84CBBBAAAEEEEAg9wIFraDPvfBJW35feuklWbZsmXTo0MEc9uLFi03t5urVq+WOO+6Ieiq6nQaekydPjhmkRt3QpYXuCebhCZ07d04uXrwohQsXlp49e8rhw4ez7V2b6StWrJhtOQsQ8KqAXVMXWrNlL/PqMbt5XPEs3DILLcc+93jHZefJ7Wu08nK7L7ZDAIE8EjDzf7o3COmtt94ytZ5du3YNnoA2q9erV09WrFgRNwBt3LixCT692G/UdwGoVjVv2LDBPPe9UaNGUrZsWdm8ebP07t3bzAl66NAhM0fowoUL5cUXXwxeLN4ggAACCCCAAAJJCziYhkkfG64VZfGSzmVepUqVeFnMuj179kilSpVMEBqaWZ8MGW+6Sa0B1Yq6AQMGmNrTMmXKmKZ7bZKP1Wwfuv9Uv/ddAKrN6trHUwcYad8HfdUJ6Pft22cC0OXLl8uMGTNMs/zAgQNT7cf+EUAAAQQQQCCjBP47CCnOOd9///3ZAsbI7N27dxcdq5Io/fvf/zYDjyLzlStXLmEAqkGwDtJ+7rnnzEDtkSNHmhmDnnzyycjduf7ZdwGodr5t0qSJ+enfv38Q7NKlS+Z9jx49RC9qjRo1gut4g4CXBSKbcCM/e/nYvXJsqTaL1xQeb108n3jHnNt9xiuPdQggkEcCBawAtGD88Gnjxo1Ss2bNPCmwYMGCUkDLjEhai3nhwoWIpf/5qDGRBrf169eXNm3amIWDBg0yjyofN26cGZik86mnM2U/o3QeTRJlay2oJq2SJvhMApJNEUAAAQQQQCCOgNaAWnOBxvqJs2VuVl199dVy4sSJbJvqIGx9+E60pDGRVtLZwaedR7sr6jia3bt324vS9ho/hE/bYVEwAvlbIF7tV/4+c++fnVu1j26V431xjhABfwlkWY/hLODiKHh96uOxY8fkypUrYTWh2v9Tm9ejJX06pPYd1SmXrrrqqmAW7XeqKVqNajCTS2/yTQ2oS14UgwACCCCAAAKZLKDPgTdPQ7JqQaO95rHNbbfdJjoR/bvvvhvcsw5y+vjjj02TenBhyJu9e/earoqRg7F1KicNQnVC+3QnakDTfQUoHwEEcizgpPYwp7XM8fZ5aHGnhMdY4wf//zCMhJnJgAAC/hXQAFSb311KOuNPu3btTL/NJUuWmBrNoUOHmub1e++91xzF/v37zQxA2uyutaINGzaUFi1ayLRp00wg2qxZMzOJvc4bqpPap7v/px40NaAu3UAUgwACCCCAAAL5QMDUgFr1d/o8+Gg/KTjFBQsWmCmVatWqJdWrVxcdZDRnzpzgdEpHjx41o9ztvp06QGnp0qVy4403mnlCK1euLJMmTZKnnnpKxowZk4IjzPkuqQHNuRlbIIAAAggggECmCmize0H3JqJX5muvvVbee+890UBTR8Xr8+BDU8uWLUUnmw9N11xzjaxZs0Z0sJL2Ia1du7Yn+n7ax0gAakvwigAC+UogXpN6vBN10tweb3vWIYBAPhfQieijTIvkxlnrhPQ5TfrAHv3xWiIA9doV4XgQQAABBBBAwLMCWVb/z6wC7taAehYjiQMjAE0Cj00RiCZgD36JVwMXb128fUZbl9+X5dQqJx55UdvJ4KOciJMXgXwg4GAi+nxwlik/BQLQlBNTAAIIIIAAAgjkGwF7GqZ8c0LpOREC0PS4U6qHBewazGQPMdp+Ulmbl+zxurW9nw2o7XTrLqEcBDwsoAFogkdxevjoPXNoBKCeuRQcCAIIIIAAAgh4XiANo+A9b5KLAyQAzQUamyCAAAIIIIBAhgroKHitBSUlJUAAmhQfG/tdIFozeSrPyS7PC83Q9jHYx5Sq87bLSdX+U7VfmttTJct+EfC3QJZVA1qgEKPgk72KBKARgrt27ZJ16xI/Uk8nfD1z5kzE1nxEAAEEEEAAgXwtoE3wBKBJX2IC0AjCU6dOyZ49eyKWRv+oj8Ii+Ucg1TV9OZEIPRa3awgjywv9HHpcOTmfyLyh+4xc56XP1HJ66WpwLAj4RKCATkRv/ZCSEiAAjeBr1aqV6E+i9NJLL0mZMmUSZWM9AggggAACCOQngTTXgGoLrD7rPScpN9vkZP+5yUsAmhs1tkEgDwUiax2d1h7a+ezt7c85PTR7+5xuF5o/t2WH7iPy/cl//StsUdmqVcM+8wEBBBBIi4D1LHYp7G4f0NOnT8vYsWNl2bJl5rnut912m8yePVsqVKgQl+CVV16RefPmyfvvvy8NGzaUSZMmiW7rhcQwLi9cBY4BAQQQQAABBHwhkGWNgM8qWDjmTypOYvTo0Sb4nDlzprzxxhuyf/9+6dixo2jNZqy0ceNGGTRokPTp00e2bNkiLVq0kG7dusn27dtjbeLqcgJQV7kpDAEEEEAAAQR8LWAexamT0cf4yeOT27Fjh2i3v+eff146dOggbdu2lcWLF5tAcvXq1TFLGzJkiPTu3VseeeQRady4sdlHnTp1ZNq0aTG3cXMFTfBualNWWgVCm4nzotk5VSfj9Nic5os8ztxuF7mf0M/2PkONQ9fzHgEEEMg3AlYTfJaLTfBvvfWWFClSRLp27RokbNCggdSrV09WrFghd9xxR3C5/ebQoUOis/pos31o6tmzp8ydOzd0UdreE4CmjZ6CEUAAAQQQQMB3AloD6uI0TDozT6VKlUwQGmpVrVo1OXLkSOii4Pu9e/ea99WrVw8u0ze6zdGjR+XKlStSQM8jjYkANI34FJ0+Abumzq65S9+R5F3JqTgX2ynaUUYrz14Wb7to+4q3jMFH8XRYhwACbgpon8uT1nSN77z7btxiN23aJPv27Yubp0qVKnLjjTfGzaMr//3vf0v58uWz5StXrlzMAFSnlNQUOUhJt7l8+bJ89dVXJqjNtlMXFxCAuohNUQgggAACCCDgX4GHHnpI+vXrJ+//5S8xT6JYsWKig4UKFYofYulodCcBaEGryT9abaVOxXThwoWox2GXHbmd/TnWdlF3lqKF8XVSVCi7RcArAnZNnV1z55XjcvM4bIPQMm0P+zV0XbT8oevz6j01n3klyX4QQCCvBJo2bSqffPJJXu3O0X6uvvpqeeedd7LlPXnypJQuXTrbcl1Q9b/T1p04cSJsvf25VKlSYcvT8SG9HQDSccaUiQACCCCAAAII+ERAg8ljx46Zfpuhh6z9P6+77rrQRcH3dgAa2UdUP2tzfqzANbgDF94QgLqATBEIIIAAAggggEBuBLSpXieifzek3+nBgwfl448/lnbt2kXdpdaa6kj5lStXhq3XUfOxtgnL6MIHmuBdQKYI7wtEa1bOafNztPz2mUfbv73Ofo23vZ0nL16dHIuTcvJqP07KIg8CCCCQqQKNGjUyQeOIESNkyZIlctVVV8nQoUOlTZs2cu+99xoWnZh+3Lhx0r9/fzNPqPYPHTZsmIwaNcpM36Tzh2q/VJ1T9NVXX/UEJTWgnrgMHAQCCCCAAAIIIBBdYMGCBdbTPwtLrVq1RKdWunTpksyZMyf4THidWknn99y9e3dwB4MHDxYdNNWrVy8TtM6aNUtmzJghdevWDeZJ55ssa0qB2M9xSueRebxsHZU2cuRImThxosePlMNDIPcCobWy1Hjm3pEtEUAgbwWKFy8u+pxzfcxkJiUNNDX+iDYtUyyHb775Rr788ku59tprY2VJy3Ka4NPCTqEIIIAAAggggEDOBHRC+pwmnRbKa8GnnoPvA1CtwNXRYTn9iyCnF5D8CGSiALWemXjVOWcEEEAg9QK+7AN6+PBh07FWpx/Q56NWrlzZzPZfpkwZady4sWhHXR0xRkIAAQQQQAABBBDwnoDvakB16oG2bduajre9e/eW2rVrm74QOuLr+PHjoiPBdJTY0qVL5a233pLrr7/ee+ocEQIIIIAAAgggkMECvgtAJ0+eLFrzuW7dOilatGjUSzd+/Hgz7YCOGnvmmWei5mEhAggggAACCCCAQHoEfNcEv23bNnnggQdiBp/KqFMV6FxYq1atSo8qpSKAAAIIIIAAAgjEFPBdAHrzzTfLpk2bYp6QvWL9+vVmriz7M68IIIAAAggggAAC3hDwXRN83759RYNQfZ5pv379TB/PChUqSIECBUwf0AMHDsiiRYtEHzelzfQkBBBAAAEEEEAAAW8J+C4AbdKkiXmUlM7w/+CDD8qVK1eyiXbq1EnWrFnjmeedZjtAFiCAAAIIIIAAAhks4LsAVK9VnTp1zAj3CxcuyOeffy5a63nx4kWpVq2a1KhRw0zJlMHXlFNHAAEEEEAAAQQ8LeDLANQW1TlAdZolnf+TiehtFV4RQAABBBBAAAFvC/huEJJyMhG9t28qjg4BBBBAAAEEEIgn4LsaUCaij3c5WYcAAggggAACCHhfwHc/h0g4AAAghElEQVQBKBPRe/+m4ggRQAABBBBAAIF4Ar5rgmci+niXk3UIIIAAAggggID3BXwXgDIRvfdvKo4QAQQQQAABBBCIJ+C7Jngmoo93OVmHAAIIIIAAAgh4X8B3ASgT0Xv/puIIEUAAAQQQQACBeAK+C0D1ZJiIPt4lZR0CCCCAAAIIIOBtAV8GoDapTkSvwaj+aDpz5ox5Hrw+G56EAAIIIIAAAggg4E0B3w1Cise4bNkyadq0abwsrEMAAQQQQAABBBBIs4DvakCff/558+z3aG67d+82taDDhg0zq5s1ayYDBgyIlpVlCCCAAAIIIIAAAmkS8F0AunXrVnnttdekXLlyUr169TC2kydPysWLF+Wdd94xy4sWLRq2ng8IIIAAAggggAAC6RfwXQC6aNEiadSokYwfP14eeOABGTFihBQo8J+eBAsXLpQf//jHsnPnzvTLcgQIIIAAAggggAACUQV81wdUg80nnnhCNmzYIHPnzpXbbrtN9PnwJAQQQAABBBBAAAF/CPguALVZb7rpJvnwww/lxhtvNDWir7zyir2KVwQQQAABBBBAAAEPC/iuCT7UskSJEjJ9+nTp3r27GWxUvHjx0NW8RwABBBBAAAEEEPCggG9rQEMtNQDVfp9t2rQRfVISCQEEEEAAAQQQQMC7Ar6uAQ1lrVy5ssyZM8dMRB+6nPcIIIAAAggggAAC3hLIFzWgNikT0dsSvCKAAAIIIIAAAt4V8F0NKBPRe/dm4sgQQAABBBBAAAEnAr4LQFM9Ef3KlStl4sSJCe2uXLkix44dS5iPDAgggAACCCCAAALhAr4LQFM9Eb0+vnPs2LHhSlE+de7cWerVqxdlDYsQQAABBBBAAAEE4gn4LgC1J6LXALBfv37y5ptvyoIFC6RmzZrxztPxuipVqoj+JEqFCxeWsmXLJsrGegQQQAABBBBAAIEIAd8OQmIi+ogryUcEEEAAAQQQQMAnAr6rAQ11ZSL6UA3eI4AAAggggAAC/hDwbQ1oKC8T0Ydq8B4BBBBAAAEEEPC2gK9rQENpS5YsKY8//riULl1aLl++LAULFgxdzXsEEEAAAQQQQAABjwj4rgZ0ypQpMmbMmDC+CRMmSPny5aV58+ZmZHr9+vVlzZo1YXn4gAACCCCAAAIIIOANAd/VgO7YsUOOHj0a1Js3b56MHj1aOnbsKH379pVTp07J4sWLpVevXrJ582aeDR+U4g0CCCCAAAIIIOANAd8FoJFss2fPltatW8u6deuCqx577DHR+TynTp0q8+fPDy7nDQIIIIAAAggggED6BXzXBB9JdvLkSbnvvvsiF8vDDz8s27dvz7acBQgggAACCCCAAALpFfBlAHru3Dm5ePGikevZs6ccPnw4m6I+srNixYrZlrMAAQQQQAABBBBAIL0CvgtAs7KyZMOGDVKqVClp2bKlaKA5bdo0+eijj4zkoUOHZOjQobJw4ULp06dPenUpHQEEEEAAAQQQQCCbgO8C0FmzZplgc8aMGdKmTRs5f/68FCpUSPbt22dObvny5aLrtAl+4MCB2U6YBQgggAACCCCAAALpFfDdIKQiRYqYke1NmjSRBx98ULRGVNOlS5fMa48ePUQnpq9QoYIJTosXL26W8w8CCCCAAAIIIICANwR8VwOqbAsWLJBatWpJsWLFTC3opk2bTC2orqtWrZrUqFHDTMmkASoJAQQQQAABBBBAwFsCvgtA165da2o+a9asKSNHjjRzgt56663y4osvekuWo0EAAQQQQAABBBCIKuC7JviZM2dKly5dZNWqVeaExo4dKz//+c9l+PDhZmAStZ5RrzMLEUAAAQQQQAABzwj4LgA9ePCgqQG1BbUP6LPPPmue/64Dj6pXry6dOnWyV/OKAAIIIIAAAggg4DEB3zXBax/P9evXZ2McN26c3HvvvXL33XeLPq6ThAACCCCAAAIIIOBNAd8FoPq89zfeeMNMs2TP/WnTvvzyy9K+fXtp166d7Ny5017MKwIIIIAAAggggICHBHwXgN5zzz3ys5/9zEw0r5PNhyadD/T111+XXr16BecFDV3PewQQQAABBBBAAIH0C/iuD6iSjRkzxoyA1+fAR6aiRYvKvHnzZMiQIXLkyJHI1XxGAAEEEEAAAQQQSLOALwNQNdM5QKtWrRrkO3v2rOzatUtKly4ttWvXllatWgXX8QYBBBBAAAEEEEDAOwK+a4KfMmWKqQENJZwwYYKUL19emjdvLvXq1ZP69evLmjVrQrPwHgEEEEAAAQQQQMAjAr6rAdUR7kePHg3yaXP76NGjpWPHjubpR6dOnZLFixebfqCbN282j+0MZuYNAggggAACCCCAQNoFfBeARorNnj1bWrduLevWrQuueuyxx6RZs2YydepUmT9/fnA5bxBAAAEEEEAAAQTSL+C7JvhIMh2IdN9990UuNtM0bd++PdtyFiCAAAIIIIAAAgikV8CXAei5c+fk4sWLRq5nz55y+PDhbIpbt26VihUrZlvOAgQQQAABBBBAAIH0CvguANVHb27YsME8971ly5aigea0adPEnpT+0KFDMnToUDNPaJ8+fdKrS+kIIIAAAggggAAC2QR8F4DOmjXLBJszZsyQNm3ayPnz50UnoN+3b585ueXLl4uu0+fCDxw4MNsJswABBBBAAAEEEEAgvQK+G4RUpEgRM7K9SZMm0r9//6DepUuXzPsePXpI9+7dpUaNGsF1vEEAAQQQQAABBBDwjoDvAtBYdFoLqqlatWqxsrAcAQQQQAABBBBAwAMCvmuC94AZh4AAAggggAACCCCQhAABaBJ4bIoAAggggAACCCCQcwEC0JybsQUCCCCAAAIIIIBAEgIEoEngsSkCCCCAAAIIIIBAzgXyzSCknJ96cltcvnxZlixZImvXrk1uRy5uffbsWfn444+lfPnyLpZKUSdOnJCSJUtK4cKFwXBJQO/1K1euGHeXisz4YvQ78euvv+b7xeU7Qb9f6tatK2XKlHG55PQWZz+MJr1HQenJCGQFrJTMDjJ12zlz5sgvf/lLKViwoG8I9JfygQMHpGzZsr455vxwoPpLWQNQP90rfnfXp6XpV1uJEiX8fiq+OX4N+E+dOsX3i8tXTL9fqlevLqVLl3a55PQWV7NmTVm2bJno1IwkfwoQgPrzuuXqqD/77DPReVJ3796dq+3ZKHcCTZs2lXnz5pn5a3O3B7bKqcCkSZNEa4YmTpyY003Jn0sB/eP2tttuk/379+dyD2yWG4HWrVvLr3/9a2nVqlVuNmcbBNImQB/QtNFTMAIIIIAAAgggkJkCBKCZed05awQQQAABBBBAIG0CBKBpo6dgBBBAAAEEEEAgMwUIQDPzunPWCCCAAAIIIIBA2gQIQNNGT8EIIIAAAggggEBmChCAZuZ156wRQAABBBBAAIG0CRCApo2eghFAAAEEEEAAgcwUIADNzOvOWSOAAAIIIIAAAmkTYCL6tNG7X/CFCxfMJNH169d3v/AMLnHPnj1yzTXXSLFixTJYwd1TP3r0qOijIatWrepuwRlcmj4acd++fdKgQYMMVnD/1Pfu3WuehFS8eHH3C6dEBJIQIABNAo9NEUAAAQQQQAABBHIuQBN8zs3YAgEEEEAAAQQQQCAJAQLQJPDYFAEEEEAAAQQQQCDnAgSgOTdjCwQQQAABBBBAAIEkBAhAk8BjUwQQQAABBBBAAIGcCxCA5tyMLRBAAAEEEEAAAQSSECAATQKPTRFAAAEEEEAAAQRyLkAAmnMztkAAAQQQQAABBBBIQoAANAk8NkUAAQQQQAABBBDIuQABaM7N2AIBBBBAAAEEEEAgCQEC0CTw2BQBBBBAAAEEEEAg5wIEoDk3c2WLQCAQt5wrV67EXa8rneTRfInKSrTeyT40j9eTk/N0YuokT6KyEq1XSyd5vG7u5DzywtNJOXmZx+vuie6dvDLPq/0kOl6vezu9txJ55ZWDk/04yeMHd47RuwIEoB66Nv/617/ke9/7nlSoUEGuu+46GTlypBw+fDjsCBcvXizf/va3pWjRolKuXDm555575NChQznO46SsV155Rdq3by8lSpSQli1byttvvx1Wjn5wkifbRh5aoF/4Tz/9tFx//fVSqlQpuffee2XDhg1hR/jJJ59It27dpHTp0saiefPmsnbt2hzn0Wv5/e9/35Rz1VVXSadOnUT3HZo++ugj6devn7m2tWvXlrFjx4auNu+d5Mm2kccWJLr/zpw5IyNGjJBq1apJoUKFpGbNmjJu3Di5dOlS8EwuXrwov/rVr6R169ZSsmRJadeunbz//vvB9fabRPfo6dOnZdSoUVK3bl0pX768+T/41Vdf2ZubVyd5wjbw4Acn97qT75dVq1ZJ7969zX184403mmsQebpO9pPouug+neSJLNtrn/W7okWLFlK8eHH5zne+Iy+88ELYH49Ovl/+/ve/y09+8hO59tprpWrVqnL//ffL119/HfNU33nnHSlQoIBs3LgxLI+T7w4necJ2ygcEcitg/ZVD8oDAuXPnAtdcc02gVatWgfXr1wdee+21QL169QJWMBI8uj/96U9aLRr44Q9/GLC+WAJz5swJWIFToGHDhoHz58+bfE7yOCnL+gILFClSJPDSSy8Ftm3bFnjkkUcCxYoVM+/tA3KSx87r1ddHH300YAUvAesXXeDPf/5zwPoDIGAFIYF///vf5pCtQCRgBUEBK+gMWL9UA9Yvk0CPHj0CVlAU+Otf/+o4j/XL3+xDr9fvfve7gPVLPGD9UgrUqFEjYP0iMfuxgq6AFXQG+vbtG7B+CQTmz58fsIL/wLPPPhvkc5InmNmjb5zcf9Yv2ID1B1ZgwoQJASuoDDz11FPGXF/t9OKLLwbKlCkT+M1vfhN47733Aj/4wQ+Ml/UL3c4ScHKPDh8+PFCnTp3AW2+9FXj33XcDTZo0CTRu3Dig18xOTvLYeb36muhed/LdcfDgwYD1x29g6NChgb/85S+BX/7yl4HChQsHJk+eHDxtJ/txcl2c5AkW6tE3ek9ZgWDgf/7nf8z3hfUHpfle/eMf/2iO2Mn3i2a89dZbze+GlStXBv7whz8EbrjhhoBVKRD1rK0/lsz3iP6uUEM7OfnucJLH3h+vCCQroH+JkTwgMGvWLPML9x//+EfwaJYvX26CUqu2yCzr0qVLoFatWsH1+ub11183Qan9ReMkj5Oy9AsuNPjVsqya18CAAQP0rUlO8th5vfiqrvrLdN68ecHDO3v2rAnqNSDVNHfuXOOrv2ztdOrUKRO0Dhs2zCxykmfv3r1mPy+//LK9G/NHhP6SeOONN8yyn/3sZwGrljWgAZqdNPisWLFi4JtvvnGcx97Wq6+J7r+TJ0+aX9pWC0DYKVi1x4HKlSubZXqdrJqgwE9/+tNgHr0uVs1y4Be/+EVwWaJ7dPv27aYsOyDQDXft2mWulf6y1+Qkj8no4X+c3OtOvjseeugh8//j8uXLwbPVP9o0YLeTk/0kui66Lyd57DK9+nr77bcHunfvHnZ4gwYNClg1yGaZk++O1atXm/vxgw8+CO5HKyj0u+Ozzz4LLrPfaGWBVTNt1tu/F3Rdpny/2A68el+AANQj16ht27aBwYMHxz2aJUuWmJqz0EwaGOkXkdY6aHKSJ1FZGgTrPnVfoenJJ580v/R1mZM8odt68f20adMCVneHsJquyOP88MMPA9OnT49cHLC6SATuu+8+s9xJnmPHjplAx2oyDu5La7rV2f4lYTUlB/r06RNcr2+09lnzaM2cJid5TEYP/5Po/tNaoZkzZwasZsews3j88ccDVjNmwA5+vvzyy4AGnXbS2jmtmX7uuefMIif3qNbgac2+3YJg70tbH7TWU5OTPPZ2Xn11cq87+e7QP46sriRhp2k1K5vafXthov04uS5O8tjlefX1n//8p/m/q7WgsZKT7w6tiY/8vzB16lSz7/3794ftWsvSloN169aFfbdoJiffHU7yhBXIBwSSEKAPqPXb3Qvp888/F6spXawvb7nzzjtNv7bx48eL9cs2eHjaf9CqXQh+1jeLFi0yfeRuuukms9xJnkRlWbV1Zl/Vq1c3r/Y/2h/v6NGjZnCTkzz2dl59VQftT7tnzx6xanalWbNm0r9/f/niiy+Ch9y0aVOxmhuDn/XN5s2b5cCBA+Ya6WcnebRfr9W0LlOmTBGrxlWsPxhMny69bm3atNHdiJpaTfLmvf2PfQ20z6QmJ3nsbb36muj+036YVi2RWLX9wVOwfgmLVetj+iJr3zZNlSpVMv0Q9f+IXhOr5sf0n7777rvNeif3qF573Y/V3cRsY/+j9/qRI0fMRyd57O28+urkXnfy3WEF66Zfrp6nFfyYfrlqbzXvB0890X6cXBcneYIFevSNmmuy/pgRq/bRfF9oX3KrRjN4xE6+O7KysoL/F6w/uOT3v/+9WF0ezO8JHStgJ6vbkPke03V6/0YmJ98dTvJE7pfPCORWgAA0t3J5uJ31B4RogPG///u/pnO5BisFCxYUq8ZRrP6eMUvasmWLWDVF8qMf/SjqF45uGJnHSVn6JadJjyM06aAn/WWvAzSc5And1ovvNdA8ceKEGbxiNWWZPwB08IQOMtLl0ZLVR8r8stUBK1ZzZLQsEiuP1fRsBhdpsNuzZ08TxOo1t/rQmf2oqQZfoals2bLmo1Xb5zhP6PZee+/k/ot2zGPGjDED8iZOnJhttQb1OrjD6rIikyZNCvtlrZnj3cf6SzvSXLfRe90OQJ3k0W28nHJzr0d+d4Sen/4BpoPkrD65YvWJNt9boetD30fux8l3h5M8oWV48b39h6x+T1hN7eZ7xWrRMAMa9f99tBTru8PO27FjR7FaScSqiRar77O92LxaLQRi9WWWgQMHhi23P2TC94t9rrz6Q4AA1APXyerfJxcuXBCr2UT0C0pHfm7atMl8uS9cuFB0VGJk2rp1q/kLWGvtoo2U1vzR8jgpS0cda7JrmsyHkM96rE7y2Nt59VVHke7YsUOsvpxiDUAy7lY/K7GazkwNQ+Rx60ho/WW7b98+UxunMxFEplh5NIDUa6XBpjUISaz+heYXkjXoTD7++GOzGzXVPzxCk9Z+6I/VROw4T+j2Xnvv5P6LPGat0dHWAKtvZ7DWOTSP/kK2BoSZP8S05lSDUE1O7lH1jrzPdVs11/tck5M8JqOH/8npvR7tuyP09DSot/rGitW0b76f9A+A0NYaO2+0/Ti5Lk7y2GV49dUOoo8fP25qi61Bc+aPTp05QIPFyBTruyM0nzXw1PyhZQ2UE6vfrfmO1/U60v63v/2tzJ49OzR72PtM+H4JO2E+eF6AANQDl0in59ApgG6++WapX79+8Ii0yVaTBkWhSYOlDh06SIMGDWTFihVmeo/Q9fo+Vh4nZek0H5oiawHtz3qsTvKYnXj4nypVqphAQ5vd7dSoUSPRXxBW31p7kXnVX+A6bZL+MaBNaBpMRqZ4eayBRvLpp5+aXxI6hc0dd9xharx1G60d0aSmtrG9b2tAjpmyRaeAcprH3taLr07uv9Dj1ppPa6CRPPPMMzJ69OjQVcH32gypXRmsfnHGVae50eTkHr366quzmeu26m6bO8mj23g55eRej/XdEXp++h2g/1esfrLmjwOd/ipyCqxY+3FyXZzkCT0eL763BsyZw9Jp1ewuHvqqXRS0qTv0/3q8747Qc9OgU5vxly1bJtZAPHn11VfNq9Z66h9iWmuvNc76x4EmneLpb3/7m3mfCd8v5kT5xzcCBKAeuVTaZ0ebtEKT9gfU2pnQmgXtb9W1a1fTb3DNmjViTUMTuol5nyhPorLsL3+7CdIuQD9rc6X+YnaSx97Oq6/qoHOcaoARmnTOSe1zaCetyejcubOpvdC5UO0+m/Z6fU2UR38Z6x8MoX9gaNnap9eeq0+PI5q57t/uD+kkj+b3ckp0/9nHrsGnzv2pgaX2oQtNWluk10Kbx0OTBvY636o1iMXRPar3sTVALOx66/70Omhgq8lJHpPRw/84vdcTfXdoTfPu3bvDzlTNNWngY6d4+3Hy3eEkj12WV1/VXJPOMRya9PtFk/29nui7Q/uS6vdHaNLvfWvAkDHXe1Xvd2uGDfPdpN9P1pRkJrv2X9cuP5qcfHc4yWN2xj8I5IVAEgOY2DQPBXQEvM4RaU2uHdyrzhdpXeOA1Yxlllk1aAGr5iHQq1evgNU8GMwX+sZJnkRl6ahLK1gK6LyBoUnnnbvrrrvMIid5Qrf14nt7pKjOM2knnd5HR1pbE5ObRXqet9xyi5kLNNqUJ5rJSR6dQUDn9LSnU9LtrF9AAWuQkZlXVD/rHJfWL97gKG9d9vzzz5uphXRqIk1O8piMHv4n0f2nh/7rX/86YDWDB0KnrQo9Javbgvm/oT6hyepba0a1WxPWm+uS6D7WKZb0/5j18IHgbqz+jWaZ1aRpljnJE9zYo2+c3OtOvjt0eh+dqzg0WbVwxkvnp9SUaD9Ovjuc5Ak9Bi++15kV9P+zzt4QmqxBcmaeTl3m5LvDelBGwOoGEjYSXr8P9HtK5wzW3wX6/yH0R6d20/tap5OzR9A7+e5wkif0XHiPQDICTMOUjF4ebmvVKpgvmYcffjig08tYtTtmbj39sreDUqumwXzpWP18Atbo97Afq1+iORoneZyUpZN865yKb775ZkCDMp0+SKerCQ3CnOTJQ6KU7MpqSg9YtZIBqy9oQKfx0Un+9bw16NCkAZB+keuUPJHmeo2c5rFGUptrZzW/BawajYA1gjhgDR4z+9b5XjVZT7Qy0wjpLyyrZs9Mrm41nZog1GRwmMfO69XXRPefNXjDzLOqE/VHmutn+/+DTv+jE/nrwwGs2RnMZOg6KfoTTzwRPHUn96j1BKWA1YRvron+37NaGAJWLVLY9FxO8gQL9eibRPe6k+8Oe/ofnZrK6ttoJkXX/z8amNp/XDnZj5Pr4iSPR6mDh6UPUrD6Xgb0jxmrmd1M6aZ/iFqD6UweJ98v+p2r22jFg77X7yqdrk33Gzo/cbBQ640+jEG/t+wp3nRdpny/hDrw3tsCBKAeuj46h5v1qDVT86NfLrfddpv5xaqHqL+U9Qsl1o8+FclJHvt045WlefSXvAZIehxaE6WTQuuTeUKTkzyh+b34XifotprXzTnqeWottD0BuR6vzlkZy1wn3HaaR/NpoKT71/1pWdZADjPRvdnJf/+xRsea5bre6u4Q0EmrtTYvNDnJE5rfi+/j3X9a+xnLXJfrL3JNOh+lBju2pz65S4PP0NYBJ/eo/uGh8x/qfjSA1cnDtTYpNDnJE5rfi+/j3etOvzu0xs7qGmEe4KD3qJqpl/5BpcnpfpxcFyd5TKEe/kdbOXQCeK2tVC9rVotA6AMWnHy/6OnpZPRW033w/4U+Nc+e+zna6UcLQDWfk+8OJ3milckyBHIqkKUbWF8iJA8J6LPdtZ+lPQgilYeWqCwdtawjuPUZxLGSkzyxtvXKcu1LqAMBIufhTMXx6Sh7HdWufcGsX0rZitD/klYzsFi/ZIIjuSMzOckTuY0XPye6/5wcs44y1vlptQ+1PaVV5HZO7lHdh454jzYtk70/J3nsvF59zYt7Xe9fK+g0/Qqj9UN3eu5OrouTPE7LS1c+7e+p/6e1X7HeY7lJ+n9e+4PquAD9bshtcvLd4SRPbstnOwRsAQJQW4JXBBBAAAEEEEAAAVcEGAXvCjOFIIAAAggggAACCNgCBKC2BK8IIIAAAggggAACrggQgLrCTCEIIIAAAggggAACtgABqC3BKwIIIIAAAggggIArAgSgrjBTCAIIIIAAAggggIAtQABqS/CKAAIIIIAAAggg4IoAAagrzBSCAAIIIIAAAgggYAsQgNoSvCKAAAIIIIAAAgi4IkAA6gozhSCAAAIIIIAAAgjYAgSgtgSvCCCAAAIIIIAAAq4IEIC6wkwhCCCAAAIIIIAAArYAAagtwSsCCCCAAAIIIICAKwIEoK4wUwgCCCCAAAIIIICALUAAakvwigACCCCAAAIIIOCKAAGoK8wUggACCCCAAAIIIGALEIDaErwigAACCCCAAAIIuCJAAOoKM4UggAACCCCAAAII2AIEoLYErwgggAACCCCAAAKuCBCAusJMIQgggAACCCCAAAK2AAGoLcErAggggAACCCCAgCsCBKCuMFMIAggggAACCCCAgC1AAGpL8IoAAggggAACCCDgigABqCvMFIIAAggggAACCCBgCxCA2hK8IoAAAggggAACCLgiQADqCjOFIIAAAggggAACCNgCBKC2BK8IIIAAAggggAACrggQgLrCTCEIIIAAAggggAACtgABqC3BKwIIIIAAAggggIArAgSgrjBTCAIIIIAAAggggIAtQABqS/CKAAIIIIAAAggg4IoAAagrzBSCAAIIIIAAAgggYAsQgNoSvCKAAAIIIIAAAgi4IkAA6gozhSCAAAIIIIAAAgjYAgSgtgSvCCCAAAIIIIAAAq4IEIC6wkwhCCCAAAIIIIAAArYAAagtwSsCCCCAAAIIIICAKwIEoK4wUwgCCCCAAAIIIICALUAAakvwigACCCCAAAIIIOCKAAGoK8wUggACCCCAAAIIIGALEIDaErwigAACCCCAAAIIuCJAAOoKM4UggAACCCCAAAII2AIEoLYErwgggAACCCCAAAKuCBCAusJMIQgggAACCCCAAAK2AAGoLcErAggggAACCCCAgCsCBKCuMFMIAggggAACCCCAgC1AAGpL8IoAAggggAACCCDgigABqCvMFIIAAggggAACCCBgCxCA2hK8IoAAAggggAACCLgiQADqCjOFIIAAAggggAACCNgCBKC2BK8IIIAAAggggAACrggQgLrCTCEIIIAAAggggAACtgABqC3BKwIIIIAAAggggIArAgSgrjBTCAIIIIAAAggggIAtQABqS/CKAAIIIIAAAggg4IoAAagrzBSCAAIIIIAAAgggYAsQgNoSvCKAAAIIIIAAAgi4IkAA6gozhSCAAAIIIIAAAgjYAgSgtgSvCCCAAAIIIIAAAq4IEIC6wkwhCCCAAAIIIIAAArYAAagtwSsCCCCAAAIIIICAKwIEoK4wUwgCCCCAAAIIIICALfB/R03z3iPf8+wAAAAASUVORK5CYII=" width="100%" />
<p>Similarly, we can calculate the <em>effective Distance</em> for each
patch (<em>eD</em>):</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>eD <span class="ot"><-</span> <span class="fu">effectiveDistance</span>(habitats_lech, cckernel)</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>eD</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co">#> patch eD</span></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 1 25.12378155</span></span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co">#> 2 2 4.56275168</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="co">#> 3 3 0.00000000</span></span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co">#> 4 4 10.52994343</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="co">#> 5 5 0.00000000</span></span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a><span class="co">#> 6 6 8.73928558</span></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a><span class="co">#> 7 7 4.73756397</span></span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a><span class="co">#> 8 8 0.09489125</span></span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a><span class="co">#> 9 9 15.59165370</span></span></code></pre></div>
<p>We may wish to have your results a raster file for further analysis
instead of a simple table. No problem, we just call the respective
functions with <em>summarize=FALSE</em> option and will get a raster
where each patch is assigned the respective value:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>eDr <span class="ot"><-</span> <span class="fu">effectiveDistance</span>(habitats_lech, cckernel, <span class="at">summarize=</span><span class="cn">FALSE</span>)</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(eDr)</span></code></pre></div>
<img 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" width="100%" />
<p>Finally we want a summary of our metrics and the total
<em>colonization Potential</em> of our riverscape:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>cP <span class="ot"><-</span> <span class="fu">colonizationPotential</span>(habitats_lech, cckernel)</span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>cP</span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="co">#> habitats threshold cP cP_rsd eD eDm_rsd nCm nCm_rsd cCm cCm_rsd</span></span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="co">#> 1 cc_clumped_lech_ehd 0 0.7765052 1.347085 7.708875 1.094959 2 0.8291562 0.3262841 1.007004</span></span></code></pre></div>
<p>So, our riverscape has a <em>colonization Potential</em> of 0.776
(~78%) for <em>Chondrilla chondrilloides</em>. The average <em>effective
Distance</em> a patch has is 7.7m, clearly within the species dispersal
distance. On average, each patch has 2 connections with a capacity of
0.33. However, the relative standard deviation of all parameters is
quite high, indicating an unequal distribution of the connectivity.
Note, that the average <em>effective Distance</em> (<em>eDm</em>) does
only consider connected patches. <em>nCm</em> gives us the average
<em>number of Connections</em> a patch has, and <em>cCm</em> tells us
the average <em>connection Capacity</em> (relative amount of seeds) that
can be exchanged by a connection.</p>
<h2 id="final-note">Final Note</h2>
<p>The basic functions of this package, <em>effectiveConnections</em>
and <em>effectiveSeedrain</em> provide spatially explicit data if
needed. Though currently we do neither consider the actual occupancy of
habitats or barriers, both can easily be combined with our functions.
The respective data can be used for further modelling. We do not include
long distance dispersal here, because here the mechanisms are different
and more complex. AA package suitable for this is currently under
development.</p>
<h2 id="references">References</h2>
<p>Woellner, R., Bräuchler, C., Kollmann, J., & Wagner, T. C.
(2022). Biological Flora of Central Europe: Chondrilla chondrilloides
(Ard.) H. Karst. Perspectives in Plant Ecology, Evolution and
Systematics, 54, 125657.</p>
<p>Wagner, T. C., Woellner, R. (2022). Effective Connectivity and
effective Habitat Distance - A new metric to quantify habitat
connectivity for plant species in riverscapes. Ecological
Indicators.</p>
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