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<h1 data-toc-skip>raptr: Representative and Adequate Prioritization Toolkit in R</h1>
<small class="dont-index">Source: <a href="https://github.com/jeffreyhanson/raptr/blob/master/vignettes/raptr.Rmd"><code>vignettes/raptr.Rmd</code></a></small>
<div class="hidden name"><code>raptr.Rmd</code></div>
</div>
<div id="overview" class="section level1">
<h1 class="hasAnchor">
<a href="#overview" class="anchor"></a>Overview</h1>
<p>This vignette illustrates the basic usage of the <em>raptr R</em> package.</p>
<p>To load the <em>raptr R</em> package and learn more about the package, type the following code into R.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># load raptr R package</span>
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">raptr</span>)
<span class="co"># show package overview</span>
?<span class="no">raptr</span></pre></body></html></div>
<p>The <em>raptr R</em> package uses a range of S4 classes to store conservation planning data, parameters, and prioritizations (Table 1).</p>
<p>Table 1: Main classes in the <em>raptr R</em> package</p>
<table class="table">
<colgroup>
<col width="20%">
<col width="79%">
</colgroup>
<thead><tr class="header">
<th align="center"><strong>Class</strong></th>
<th align="left"><strong>Description</strong></th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center"><code>ManualOpts</code></td>
<td align="left">place-holder class for manually specified solutions</td>
</tr>
<tr class="even">
<td align="center"><code>GurobiOpts</code></td>
<td align="left">stores parameters for solving optimization problems using <em>Gurobi</em>
</td>
</tr>
<tr class="odd">
<td align="center"><code>RapUnreliableOpts</code></td>
<td align="left">stores control variables parameters for the unreliable problem formulation</td>
</tr>
<tr class="even">
<td align="center"><code>RapReliableOpts</code></td>
<td align="left">stores control variables for the reliable problem formulation</td>
</tr>
<tr class="odd">
<td align="center"><code>DemandPoints</code></td>
<td align="left">stores the coordinates and weights for a given species and attribute space</td>
</tr>
<tr class="even">
<td align="center"><code>PlanningUnitPoints</code></td>
<td align="left">stores the coordinates and ids for planning units that can be used to preserve a given species</td>
</tr>
<tr class="odd">
<td align="center"><code>AttributeSpace</code></td>
<td align="left">stores the coordinates for planning units and the demand points for each species</td>
</tr>
<tr class="even">
<td align="center"><code>RapData</code></td>
<td align="left">stores the all the planning unit, species, and attribute space data</td>
</tr>
<tr class="odd">
<td align="center"><code>RapUnsolved</code></td>
<td align="left">stores all the data, control variables, and parameters needed to generate prioritizations</td>
</tr>
<tr class="even">
<td align="center"><code>RapResults</code></td>
<td align="left">stores the prioritizations and summary statistics generated after solving a problem</td>
</tr>
<tr class="odd">
<td align="center"><code>RapSolved</code></td>
<td align="left">stores the input data and output results</td>
</tr>
</tbody>
</table>
</div>
<div id="getting-started" class="section level1">
<h1 class="hasAnchor">
<a href="#getting-started" class="anchor"></a>Getting started</h1>
<p>This tutorial is designed to provide users with an understanding of how to use the <em>raptr R</em> package to generate and compare solutions. This tutorial uses several additional packages, so first we will run the following code to load them.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># load packages for tutorial</span>
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">parallel</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">plyr</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">ggplot2</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">RandomFields</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">rgeos</span>)
<span class="co"># set seed for reproducibility</span>
<span class="fu"><a href="https://rdrr.io/r/base/Random.html">set.seed</a></span>(<span class="fl">500</span>)
<span class="co"># set number of threads for computation</span>
<span class="no">threads</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html">as.integer</a></span>(<span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">max</a></span>(<span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html">detectCores</a></span>() - <span class="fl">2</span>))</pre></body></html></div>
<p>Now we will check if the the Gurobi software suite and the <em>gurobi R</em> package are installed. To do this, run the following code.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="fu"><a href="../reference/is.GurobiInstalled.html">is.GurobiInstalled</a></span>(<span class="kw">verbose</span> <span class="kw">=</span> <span class="fl">TRUE</span>)</pre></body></html></div>
<pre><code>## [1] TRUE</code></pre>
</div>
<div id="simulated-examples" class="section level1">
<h1 class="hasAnchor">
<a href="#simulated-examples" class="anchor"></a>Simulated examples</h1>
<div id="data" class="section level2">
<h2 class="hasAnchor">
<a href="#data" class="anchor"></a>Data</h2>
<p>To investigate the behavior of the problem, we will generate prioritizations for three simulated species. We will use the unreliable formulation of the problem to understand the basics, and later move onto the reliable formulation. The first species (termed ‘uniform’) will represent a hyper-generalist. This species will inhabit all areas with equal probability. The second species (termed ‘normal’) will represent a species with a single range core. The third species (termed ‘bimodal’) will represent a species with two distinct ecotypes, each with their own range core. To reduce computational time for this example, we will use a 10 <span class="math inline">\(\times\)</span> 10 grid of square planning units.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make planning units</span>
<span class="no">sim_pus</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/sim.pus.html">sim.pus</a></span>(<span class="fl">100L</span>)
<span class="co"># simulate species distributions</span>
<span class="no">sim_spp</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span>(<span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="st">"uniform"</span>, <span class="st">"normal"</span>, <span class="st">"bimodal"</span>), <span class="no">sim.species</span>, <span class="kw">n</span> <span class="kw">=</span> <span class="fl">1</span>,
<span class="kw">x</span> <span class="kw">=</span> <span class="no">sim_pus</span>, <span class="kw">res</span> <span class="kw">=</span> <span class="fl">1</span>)</pre></body></html></div>
<p>Let’s see what these species’ distributions look like.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot species</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="fu"><a href="https://rdrr.io/r/utils/stack.html">stack</a></span>(<span class="no">sim_spp</span>),
<span class="kw">main</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="st">"Uniform species"</span>, <span class="st">"Normal species"</span>, <span class="st">"Bimodal species"</span>),
<span class="kw">addfun</span> <span class="kw">=</span> <span class="kw">function</span>() <span class="fu"><a href="https://rdrr.io/r/graphics/lines.html">lines</a></span>(<span class="no">sim_pus</span>), <span class="kw">nc</span> <span class="kw">=</span> <span class="fl">3</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-7-1.png" alt="_Distribution of three simulated species. Each square represents a planning unit. The color of each square denotes the probability that individuals from each species occupy it._" width="672"><p class="caption">
<em>Distribution of three simulated species. Each square represents a planning unit. The color of each square denotes the probability that individuals from each species occupy it.</em>
</p>
</div>
<p>Next, we will generate a set of demand points. To understand the effects of probabilities and weights on the demand points, we will generate the demand points in geographic space. These demand points will be the centroids of the planning units. Additionally, we will use the same set of demand points for each species and only vary the weights of the demand points between species. <strong>Note that we are only using the same distribution of demand points for different species for teaching purposes. It is strongly recommended to use different demand points for different species in real-world conservation planning exercises.</strong> See the case-study section of this tutorial for examples on how to generate suitable demand points.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># generate coordinates for pus/demand points</span>
<span class="no">pu_coords</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/rgeos/man/topo-unary-gCentroid.html">gCentroid</a></span>(<span class="no">sim_pus</span>, <span class="kw">byid</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
<span class="co"># calculate weights</span>
<span class="no">sim_dps</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span>(<span class="no">sim_spp</span>, <span class="kw">function</span>(<span class="no">x</span>) <span class="fu">extract</span>(<span class="no">x</span>, <span class="no">pu_coords</span>))
<span class="co"># create demand point objects</span>
<span class="no">sim_dps</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span>(<span class="no">sim_dps</span>, <span class="kw">function</span>(<span class="no">x</span>) <span class="fu"><a href="../reference/DemandPoints.html">DemandPoints</a></span>(<span class="no">pu_coords</span>@<span class="kw">coords</span>,
<span class="kw">weights</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="no">x</span>)))</pre></body></html></div>
<p>Now, we will construct a <code>RapUnsolved</code> object to store our input data and parameters. This contains all the information to generate prioritizations.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co">## create RapUnreliableOpts object</span>
<span class="co"># this stores parameters for the unreliable formulation problem (ie. BLM)</span>
<span class="no">sim_ro</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/RapUnreliableOpts.html">RapUnreliableOpts</a></span>()
<span class="co">## create RapData object</span>
<span class="co"># create data.frame with species info</span>
<span class="no">species</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span>(<span class="kw">name</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="st">"uniform"</span>, <span class="st">"normal"</span>, <span class="st">"bimodal"</span>))
<span class="co">## create data.frame with species and space targets</span>
<span class="co"># amount targets at 20% (denoted with target=0)</span>
<span class="co"># space targets at 20% (denoted with target=1)</span>
<span class="no">targets</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/expand.grid.html">expand.grid</a></span>(<span class="kw">species</span> <span class="kw">=</span> <span class="fl">1</span>:<span class="fl">3</span>, <span class="kw">target</span> <span class="kw">=</span> <span class="fl">0</span>:<span class="fl">1</span>, <span class="kw">proportion</span> <span class="kw">=</span> <span class="fl">0.2</span>)
<span class="co"># calculate probability of each species in each pu</span>
<span class="no">pu_probabilities</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/calcSpeciesAverageInPus.html">calcSpeciesAverageInPus</a></span>(<span class="no">sim_pus</span>, <span class="fu"><a href="https://rdrr.io/r/utils/stack.html">stack</a></span>(<span class="no">sim_spp</span>))
<span class="co">## create AttributeSpace object</span>
<span class="co"># this stores the coordinates of the planning units in an attribute space</span>
<span class="co"># and the coordinates and weights of demand points in the space</span>
<span class="no">pu_points</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/PlanningUnitPoints.html">PlanningUnitPoints</a></span>(<span class="kw">coords</span> <span class="kw">=</span> <span class="no">pu_coords</span>@<span class="kw">coords</span>,
<span class="kw">ids</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/nrow.html">nrow</a></span>(<span class="no">sim_pus</span>@<span class="kw">data</span>)))
<span class="no">attr_spaces</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/AttributeSpaces.html">AttributeSpaces</a></span>(
<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="fu"><a href="../reference/AttributeSpace.html">AttributeSpace</a></span>(<span class="kw">planning.unit.points</span> <span class="kw">=</span> <span class="no">pu_points</span>,
<span class="kw">demand.points</span> <span class="kw">=</span> <span class="no">sim_dps</span><span class="kw">[[</span><span class="fl">1</span>]],
<span class="kw">species</span> <span class="kw">=</span> <span class="fl">1L</span>),
<span class="fu"><a href="../reference/AttributeSpace.html">AttributeSpace</a></span>(<span class="kw">planning.unit.points</span> <span class="kw">=</span> <span class="no">pu_points</span>,
<span class="kw">demand.points</span> <span class="kw">=</span> <span class="no">sim_dps</span><span class="kw">[[</span><span class="fl">2</span>]],
<span class="kw">species</span> <span class="kw">=</span> <span class="fl">2L</span>),
<span class="fu"><a href="../reference/AttributeSpace.html">AttributeSpace</a></span>(<span class="kw">planning.unit.points</span> <span class="kw">=</span> <span class="no">pu_points</span>,
<span class="kw">demand.points</span> <span class="kw">=</span> <span class="no">sim_dps</span><span class="kw">[[</span><span class="fl">3</span>]],
<span class="kw">species</span> <span class="kw">=</span> <span class="fl">3L</span>)),
<span class="kw">name</span> <span class="kw">=</span> <span class="st">"geographic"</span>)
<span class="co"># generate boundary data information</span>
<span class="no">boundary</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/calcBoundaryData.html">calcBoundaryData</a></span>(<span class="no">sim_pus</span>)
<span class="co">## create RapData object</span>
<span class="co"># this stores all the input data for the prioritization</span>
<span class="no">sim_rd</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/RapData.html">RapData</a></span>(<span class="no">sim_pus</span>@<span class="kw">data</span>, <span class="no">species</span>, <span class="no">targets</span>, <span class="no">pu_probabilities</span>,
<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">attr_spaces</span>), <span class="no">boundary</span>, <span class="fu"><a href="../reference/SpatialPolygons2PolySet.html">SpatialPolygons2PolySet</a></span>(<span class="no">sim_pus</span>))
<span class="co">## create RapUnsolved object</span>
<span class="co"># this stores all the input data and parameters needed to generate</span>
<span class="co"># prioritizations</span>
<span class="no">sim_ru</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/RapUnsolved.html">RapUnsolved</a></span>(<span class="no">sim_ro</span>, <span class="no">sim_rd</span>)</pre></body></html></div>
</div>
<div id="single-species-prioritizations" class="section level2">
<h2 class="hasAnchor">
<a href="#single-species-prioritizations" class="anchor"></a>Single-species prioritizations</h2>
<div id="amount-based-targets" class="section level3">
<h3 class="hasAnchor">
<a href="#amount-based-targets" class="anchor"></a>Amount-based targets</h3>
<p>To investigate the effects of space-based targets, we will generate a prioritization for each species using only amount-based targets and compare them to prioritizations generated using amount- and space-based targets. To start off, we will generate a prioritization for the uniform species using amount-based targets. To do this, we will generate a new <code>sim_ru</code> object by extracting out the data for the uniform species from the <code>sim_ru</code> object. Then, we will update the targets in the new object. Finally, we will solve the object to generate a prioritization that fulfills the targets for minimal cost.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># create new object with just the uniform species</span>
<span class="no">sim_ru_s1</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/spp.subset.html">spp.subset</a></span>(<span class="no">sim_ru</span>, <span class="st">"uniform"</span>)
<span class="co"># update amount targets to 20% and space targets to 0%</span>
<span class="no">sim_ru_s1</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_ru_s1</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fl">0.2</span>, <span class="kw">space.target</span> <span class="kw">=</span> <span class="fl">NA</span>,
<span class="kw">solve</span> <span class="kw">=</span> <span class="fl">FALSE</span>)</pre></body></html></div>
<div class="sourceCode"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/options.html">options</a></span>(<span class="kw">error</span> <span class="kw">=</span> <span class="kw">function</span>() {
<span class="fu"><a href="https://rdrr.io/r/base/traceback.html">traceback</a></span>()
<span class="fu"><a href="https://rdrr.io/r/base/quit.html">q</a></span>(<span class="st">"no"</span>, <span class="fl">1</span>, <span class="fl">FALSE</span>)
})
<span class="co"># solve problem to identify prioritization</span>
<span class="no">sim_rs_s1_amount</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/solve.html">solve</a></span>(<span class="no">sim_ru_s1</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 1 rows, 100 columns and 100 nonzeros
## Model fingerprint: 0xe910d3ec
## Variable types: 0 continuous, 100 integer (100 binary)
## Coefficient statistics:
## Matrix range [5e-01, 5e-01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+01, 1e+01]
## Found heuristic solution: objective 20.0000000
## Presolve removed 1 rows and 100 columns
## Presolve time: 0.00s
## Presolve: All rows and columns removed
##
## Explored 0 nodes (0 simplex iterations) in 0.00 seconds
## Thread count was 1 (of 4 available processors)
##
## Solution count 1: 20
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 2.000000000000e+01, best bound 2.000000000000e+01, gap 0.0000%</code></pre>
<pre><code>## Warning in validityMethod(object): some species have space.held values less than
## 0, and thus are poorly represented</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co">## show summary</span>
<span class="co"># note the format for this is similar to that used by Marxan</span>
<span class="co"># see ?raptr::summary for details on this table</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_rs_s1_amount</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 20 20 20 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 42 168 10 0.1909091</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show amount held</span>
<span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_rs_s1_amount</span>)</pre></body></html></div>
<pre><code>## uniform
## 1 0.2</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show space held</span>
<span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_rs_s1_amount</span>)</pre></body></html></div>
<pre><code>## uniform (Space 1)
## 1 -0.2363636</code></pre>
<p>Now that we have generated a prioritization, let’s see what it looks like. We can use the <code>spp.plot</code> method to see how the prioritization overlaps with the uniform species’ distribution. Note that since all planning units have equal probabilities for this species, all planning units have the same fill color.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the prioritization and the uniform species' distribution</span>
<span class="fu"><a href="../reference/spp.plot.html">spp.plot</a></span>(<span class="no">sim_rs_s1_amount</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Uniform species"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-13-1.png" alt="_A prioritization for the uniformly distributed species generated using amount-based targets (20\%). Sqaures represent planning units. Planning units with a green border are selected for prioritization, and their colour denotes the probability they are inhabited by the species._" width="432"><p class="caption">
<em>A prioritization for the uniformly distributed species generated using amount-based targets (20%). Sqaures represent planning units. Planning units with a green border are selected for prioritization, and their colour denotes the probability they are inhabited by the species.</em>
</p>
</div>
<p>The prioritization for the uniform species appears to be just a random selection of planning units. This behavior is due to the fact that any prioritization with 20 planning units is optimal. By relying on just amount targets, this solution may preserve a section of the species’ range core, or just focus on the range margin, or some random part of its range–no emphasis is directed towards preserving different parts of the species’ range. This behavior highlights a fundamental limitation of just using amount-based targets. In the absence of additional criteria, conventional reserve selection problems do not contain any additional information to identify the most effective prioritization.</p>
<p>Now, we will generate a prioritization for the normally distributed species using amount-based targets. We will use a similar process to what we used for the uniformly distributed species, but for brevity, we will use code to generate solutions immediately after updating the object.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># create new object with just the normal species</span>
<span class="no">sim_ru_s2</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/spp.subset.html">spp.subset</a></span>(<span class="no">sim_ru</span>, <span class="st">"normal"</span>)</pre></body></html></div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># update amount targets to 20% and space targets to 0% and solve it</span>
<span class="no">sim_rs_s2_amount</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_ru_s2</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fl">0.2</span>, <span class="kw">space.target</span> <span class="kw">=</span> <span class="fl">NA</span>,
<span class="kw">solve</span> <span class="kw">=</span> <span class="fl">TRUE</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 1 rows, 100 columns and 100 nonzeros
## Model fingerprint: 0x3d4ec257
## Variable types: 0 continuous, 100 integer (100 binary)
## Coefficient statistics:
## Matrix range [7e-02, 7e-01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [7e+00, 7e+00]
## Found heuristic solution: objective 27.0000000
## Presolve removed 0 rows and 86 columns
## Presolve time: 0.00s
## Presolved: 1 rows, 14 columns, 14 nonzeros
## Variable types: 0 continuous, 14 integer (0 binary)
## Presolved: 1 rows, 14 columns, 14 nonzeros
##
##
## Root relaxation: objective 9.864476e+00, 6 iterations, 0.00 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## 0 0 9.86448 0 1 27.00000 9.86448 63.5% - 0s
## H 0 0 14.0000000 9.86448 29.5% - 0s
## H 0 0 10.0000000 9.86448 1.36% - 0s
## 0 0 9.86448 0 1 10.00000 9.86448 1.36% - 0s
##
## Explored 1 nodes (6 simplex iterations) in 0.00 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 3: 10 14 27
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 1.000000000000e+01, best bound 1.000000000000e+01, gap 0.0000%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summary</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_rs_s2_amount</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 10 10 10 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 12 192 16 0.05454545</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show amount held</span>
<span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_rs_s2_amount</span>)</pre></body></html></div>
<pre><code>## normal
## 1 0.2026153</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show space held</span>
<span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_rs_s2_amount</span>)</pre></body></html></div>
<pre><code>## normal (Space 1)
## 1 0.5909494</code></pre>
<p>Now let’s visualize the prioritization we made for the normal species.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the prioritization and the normal species' distribution</span>
<span class="fu"><a href="../reference/spp.plot.html">spp.plot</a></span>(<span class="no">sim_rs_s2_amount</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Normal species"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-17-1.png" alt="_A prioritization for the normally distributed species generated using amount-based targets (20\%). See Figure 3 caption for conventions._" width="432"><p class="caption">
<em>A prioritization for the normally distributed species generated using amount-based targets (20%). See Figure 3 caption for conventions.</em>
</p>
</div>
<p>The amount-based prioritization for the normal species focuses only on the species’ range core. This prioritization fails to secure any peripheral parts of the species’ distribution. As a consequence, it may miss out on populations with novel adaptations to environmental conditions along the species’ range margin.</p>
<p>Now, let’s generate an amount-based target for the bimodally distributed species view it.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># create new object with just the bimodal species</span>
<span class="no">sim_ru_s3</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/spp.subset.html">spp.subset</a></span>(<span class="no">sim_ru</span>, <span class="st">"bimodal"</span>)</pre></body></html></div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># update amount targets to 20% and space targets to 0% and solve it</span>
<span class="no">sim_rs_s3_amount</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_ru_s3</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fl">0.2</span>,
<span class="kw">space.target</span> <span class="kw">=</span> <span class="fl">NA</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 1 rows, 100 columns and 100 nonzeros
## Model fingerprint: 0x1ce55139
## Variable types: 0 continuous, 100 integer (100 binary)
## Coefficient statistics:
## Matrix range [7e-03, 9e-01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [7e+00, 7e+00]
## Found heuristic solution: objective 21.0000000
## Presolve removed 0 rows and 75 columns
## Presolve time: 0.00s
## Presolved: 1 rows, 25 columns, 25 nonzeros
## Variable types: 0 continuous, 25 integer (0 binary)
## Presolved: 1 rows, 25 columns, 25 nonzeros
##
##
## Root relaxation: objective 7.919039e+00, 17 iterations, 0.00 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## 0 0 7.91904 0 1 21.00000 7.91904 62.3% - 0s
## H 0 0 16.0000000 7.91904 50.5% - 0s
## H 0 0 8.0000000 7.91904 1.01% - 0s
## 0 0 7.91904 0 1 8.00000 7.91904 1.01% - 0s
##
## Explored 1 nodes (17 simplex iterations) in 0.00 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 3: 8 16 21
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 8.000000000000e+00, best bound 8.000000000000e+00, gap 0.0000%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the prioritization and the bimodal species' distribution</span>
<span class="fu"><a href="../reference/spp.plot.html">spp.plot</a></span>(<span class="no">sim_rs_s3_amount</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Bimodal species"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-20-1.png" alt="_A prioritization for the bimodally distributed species generated using amount-based targets (20\%). See Figure 3 caption for conventions._" width="432"><p class="caption">
<em>A prioritization for the bimodally distributed species generated using amount-based targets (20%). See Figure 3 caption for conventions.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summary</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_rs_s3_amount</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 8 8 8 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 9 197 14 0.04090909</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show amount held</span>
<span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_rs_s3_amount</span>)</pre></body></html></div>
<pre><code>## bimodal
## 1 0.2018391</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show space held</span>
<span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_rs_s3_amount</span>)</pre></body></html></div>
<pre><code>## bimodal (Space 1)
## 1 0.1200278</code></pre>
<p>The amount-based prioritization for the bimodally distributed species only selects planning units in the bottom left corner of the study area. This prioritization only preserves individuals belonging to one of the two ecotypes. As a consequence, this prioritization may fail to preserve a representative sample of the genetic variation found inside this species.</p>
</div>
<div id="amount-based-and-space-based-targets" class="section level3">
<h3 class="hasAnchor">
<a href="#amount-based-and-space-based-targets" class="anchor"></a>Amount-based and space-based targets</h3>
<p>Now that we have generated a prioritization for each species using only amount-based targets, we will generate a prioritizations using both amount-based and space-targets. To do this we will update the space targets in our amount-based prioritizations to 85%, and store the new prioritizations in new objects.</p>
<p>First, let’s do this for the uniform species.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make new prioritization</span>
<span class="no">sim_rs_s1_space</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_rs_s1_amount</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fl">0.2</span>,
<span class="kw">space.target</span> <span class="kw">=</span> <span class="fl">0.85</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 10102 rows, 10100 columns and 40000 nonzeros
## Model fingerprint: 0x3f02f09a
## Variable types: 0 continuous, 10100 integer (10100 binary)
## Coefficient statistics:
## Matrix range [5e-01, 8e+01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+00, 1e+02]
## Found heuristic solution: objective 95.0000000
## Presolve removed 36 rows and 0 columns
## Presolve time: 2.09s
## Presolved: 10066 rows, 10100 columns, 40104 nonzeros
## Variable types: 0 continuous, 10100 integer (10100 binary)
## Presolved: 10066 rows, 10100 columns, 40104 nonzeros
##
##
## Root relaxation: objective 2.000000e+01, 621 iterations, 0.13 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## * 0 0 0 20.0000000 20.00000 0.00% - 2s
##
## Explored 0 nodes (1158 simplex iterations) in 2.40 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 2: 20 95
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 2.000000000000e+01, best bound 2.000000000000e+01, gap 0.0000%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summary</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_rs_s1_space</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 20 20 20 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 13 147 60 0.05909091</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show amount held</span>
<span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_rs_s1_space</span>)</pre></body></html></div>
<pre><code>## uniform
## 1 0.2</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show space held</span>
<span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_rs_s1_space</span>)</pre></body></html></div>
<pre><code>## uniform (Space 1)
## 1 0.909697</code></pre>
<p>Let’s take a look at the prioritization for the uniform species with amount-based and space-based targets. Then, let’s compare the solutions for the amount-based prioritization with the new prioritization using both amount and space targets.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the prioritization and the uniform species' distribution</span>
<span class="fu"><a href="../reference/spp.plot.html">spp.plot</a></span>(<span class="no">sim_rs_s1_space</span>, <span class="st">"uniform"</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Uniform species"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-24-1.png" alt="_A prioritization for the uniformly distributed species generated using amount-based targets (20\%) and space-based targets (85\%). See Figure 3 caption for conventions._" width="432"><p class="caption">
<em>A prioritization for the uniformly distributed species generated using amount-based targets (20%) and space-based targets (85%). See Figure 3 caption for conventions.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the difference between old and new prioritizations</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_rs_s1_amount</span>, <span class="no">sim_rs_s1_space</span>, <span class="fl">1</span>, <span class="fl">1</span>,
<span class="kw">main</span> <span class="kw">=</span> <span class="st">"Difference between solutions"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-25-1.png" alt="_Difference between two prioritizations for the uniformly distributed species. Prioritisation $X$ was generated using just amount-based targets (20\%), and prioritization $Y$ was generated using an additional space-based target (85\%)._" width="576"><p class="caption">
<em>Difference between two prioritizations for the uniformly distributed species. Prioritisation <span class="math inline">\(X\)</span> was generated using just amount-based targets (20%), and prioritization <span class="math inline">\(Y\)</span> was generated using an additional space-based target (85%).</em>
</p>
</div>
<p>Here we can see that by including a space-target, the prioritization is spread out evenly across the species’ distribution. Unlike the amount-based prioritization, this prioritization samples all the different parts of the species’ distribution.</p>
<p>Now, let’s generate a prioritization for the normally distributed species that considers amount-based and space-based targets. Then, let’s visualize the new prioritization and compare it to the old amount-based prioritization.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make new prioritization</span>
<span class="no">sim_rs_s2_space</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_rs_s2_amount</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fl">0.2</span>,
<span class="kw">space.target</span> <span class="kw">=</span> <span class="fl">0.85</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 10102 rows, 10100 columns and 40000 nonzeros
## Model fingerprint: 0xa48af59a
## Variable types: 0 continuous, 10100 integer (10100 binary)
## Coefficient statistics:
## Matrix range [7e-02, 4e+01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+00, 6e+01]
## Found heuristic solution: objective 91.0000000
## Presolve removed 220 rows and 0 columns (presolve time = 5s) ...
## Presolve removed 220 rows and 0 columns
## Presolve time: 5.22s
## Presolved: 9882 rows, 10100 columns, 41664 nonzeros
## Variable types: 0 continuous, 10100 integer (10100 binary)
## Presolved: 9882 rows, 10100 columns, 41664 nonzeros
##
##
## Root simplex log...
##
## Iteration Objective Primal Inf. Dual Inf. Time
## 0 1.0000000e+02 0.000000e+00 1.000000e+02 5s
## 4250 1.2325699e+01 0.000000e+00 0.000000e+00 6s
## 4250 1.2325699e+01 0.000000e+00 0.000000e+00 6s
##
## Root relaxation: objective 1.232570e+01, 4250 iterations, 0.56 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## 0 0 12.32570 0 293 91.00000 12.32570 86.5% - 5s
## H 0 0 13.0000000 12.32570 5.19% - 5s
## 0 0 12.32570 0 293 13.00000 12.32570 5.19% - 5s
##
## Explored 1 nodes (4527 simplex iterations) in 5.94 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 2: 13 91
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 1.300000000000e+01, best bound 1.300000000000e+01, gap 0.0000%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summary</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_rs_s2_space</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 13 13 13 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 5 173 42 0.02272727</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show amount held</span>
<span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_rs_s2_space</span>)</pre></body></html></div>
<pre><code>## normal
## 1 0.2099983</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show space held</span>
<span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_rs_s2_space</span>)</pre></body></html></div>
<pre><code>## normal (Space 1)
## 1 0.8533832</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the prioritization and the normal species' distribution</span>
<span class="fu"><a href="../reference/spp.plot.html">spp.plot</a></span>(<span class="no">sim_rs_s2_space</span>, <span class="st">"normal"</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Normal species"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-28-1.png" alt="_A prioritization for the normally distributed species generated using amount-based targets (20\%) and space-based targets (85\%). See Figure 3 caption for conventions._" width="432"><p class="caption">
<em>A prioritization for the normally distributed species generated using amount-based targets (20%) and space-based targets (85%). See Figure 3 caption for conventions.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the difference between old and new prioritizations</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_rs_s2_amount</span>, <span class="no">sim_rs_s2_space</span>, <span class="fl">1</span>, <span class="fl">1</span>,
<span class="kw">main</span> <span class="kw">=</span> <span class="st">"Difference between solutions"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-29-1.png" alt="_Difference between two prioritizations for the normally distributed species. See Figure 7 caption for conventions._" width="576"><p class="caption">
<em>Difference between two prioritizations for the normally distributed species. See Figure 7 caption for conventions.</em>
</p>
</div>
<p>We can see by using both amount-based and space-based targets we can obtain a prioritization that secures both the species’ range core and parts of its range margin. As a consequence, it may capture any novel adaptations found along the species’ range margin–unlike the amount-based prioritization.</p>
<p>Finally, let’s generate a prioritization for the bimodal species using amount-based and space-based targets.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make new prioritization</span>
<span class="no">sim_rs_s3_space</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_rs_s3_amount</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fl">0.2</span>,
<span class="kw">space.target</span> <span class="kw">=</span> <span class="fl">0.85</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 10102 rows, 10100 columns and 40000 nonzeros
## Model fingerprint: 0xd59e1e6d
## Variable types: 0 continuous, 10100 integer (10100 binary)
## Coefficient statistics:
## Matrix range [7e-03, 9e+01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+00, 7e+01]
## Found heuristic solution: objective 87.0000000
## Presolve removed 589 rows and 15 columns
## Presolve time: 4.77s
## Presolved: 9513 rows, 10085 columns, 51461 nonzeros
## Variable types: 0 continuous, 10085 integer (10085 binary)
## Presolved: 9513 rows, 10085 columns, 51461 nonzeros
##
##
## Root simplex log...
##
## Iteration Objective Primal Inf. Dual Inf. Time
## 1437 1.3630431e+01 0.000000e+00 7.958402e+03 5s
## 6104 8.9807917e+00 0.000000e+00 0.000000e+00 5s
## 6104 8.9807917e+00 0.000000e+00 0.000000e+00 5s
##
## Root relaxation: objective 8.980792e+00, 6104 iterations, 0.69 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## 0 0 8.98079 0 75 87.00000 8.98079 89.7% - 5s
## H 0 0 10.0000000 8.98079 10.2% - 5s
## 0 0 cutoff 0 10.00000 10.00000 0.00% - 5s
##
## Cutting planes:
## Gomory: 2
## MIR: 1
## Zero half: 2
##
## Explored 1 nodes (6445 simplex iterations) in 5.84 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 2: 10 87
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 1.000000000000e+01, best bound 1.000000000000e+01, gap 0.0000%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summary</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_rs_s3_space</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 10 10 10 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 7 187 26 0.03181818</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show amount held</span>
<span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_rs_s3_space</span>)</pre></body></html></div>
<pre><code>## bimodal
## 1 0.2226414</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show space held</span>
<span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_rs_s3_space</span>)</pre></body></html></div>
<pre><code>## bimodal (Space 1)
## 1 0.8562891</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the prioritization and the bimodal species' distribution</span>
<span class="fu"><a href="../reference/spp.plot.html">spp.plot</a></span>(<span class="no">sim_rs_s3_space</span>, <span class="st">'bimodal'</span>, <span class="kw">main</span><span class="kw">=</span><span class="st">'Bimodal species'</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-32-1.png" alt="_A prioritization for the normally distributed species generated using amount-based targets (20\%) and space-based targets (85\%). See Figure 3 caption for conventions._" width="432"><p class="caption">
<em>A prioritization for the normally distributed species generated using amount-based targets (20%) and space-based targets (85%). See Figure 3 caption for conventions.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot the difference between old and new prioritizations</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_rs_s3_amount</span>, <span class="no">sim_rs_s3_space</span>, <span class="fl">1</span>, <span class="fl">1</span>,
<span class="kw">main</span> <span class="kw">=</span> <span class="st">"Difference between solutions"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-33-1.png" alt="_Difference between two prioritizations for the bimodally distributed species. See Figure 7 caption for conventions._" width="576"><p class="caption">
<em>Difference between two prioritizations for the bimodally distributed species. See Figure 7 caption for conventions.</em>
</p>
</div>
<p>Earlier we found that the amount-based prioritization only preserved individuals from a single ecotype, and would have failed to adequately preserve the intra-specific variation for this species. However, here we can see that by including space-based targets, we can develop prioritizations that secure individuals belonging to both ecotypes. This new prioritization is much more effective at sampling the intra-specific variation for this species.</p>
<p>Overall, these results demonstrate that under the simplest of conditions, the use of space-based targets can improve prioritizations. However, these prioritizations were each generated for a single species. Prioritizations generated using multiple species may do a better job at preserving the intra-specific variation for individuals species by preserving them in different parts of their range. We will investigate this in the next section.</p>
</div>
</div>
<div id="multi-species-prioritizations" class="section level2">
<h2 class="hasAnchor">
<a href="#multi-species-prioritizations" class="anchor"></a>Multi-species prioritizations</h2>
<div id="effects-of-including-space-based-targets" class="section level3">
<h3 class="hasAnchor">
<a href="#effects-of-including-space-based-targets" class="anchor"></a>Effects of including space-based targets</h3>
<p>So far we have generated prioritizations using only a single species at a time. However, real world prioritizations are often generated using multiple species to ensure that they preserve a comprehensive set of biodiversity. Here, we will generate multi-species prioritizations that preserve all three of the simulated species. First, we will generate a prioritization using amount-based targets that only aims to preserve 20% of the area they occupy. Then, we will generate a prioritization that also incorporate space-based targets to also preserve 85% of their geographic distribution. We will then compare the two prioritizations.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make prioritizations</span>
<span class="no">sim_mrs_amount</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_ru</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>),
<span class="kw">space.target</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">0</span>), <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 30306 rows, 30100 columns and 120000 nonzeros
## Model fingerprint: 0x5ebf0f26
## Variable types: 0 continuous, 30100 integer (30100 binary)
## Coefficient statistics:
## Matrix range [7e-03, 9e+01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+00, 8e+02]
## Found heuristic solution: objective 98.0000000
## Presolve time: 2.03s
## Presolved: 30306 rows, 30100 columns, 120000 nonzeros
## Variable types: 0 continuous, 30100 integer (30100 binary)
## Presolved: 30306 rows, 30100 columns, 120000 nonzeros
##
##
## Root relaxation: objective 2.000000e+01, 2299 iterations, 0.97 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## H 0 0 20.0000000 0.00000 100% - 4s
## 0 0 - 0 20.00000 20.00000 0.00% - 4s
##
## Explored 0 nodes (5871 simplex iterations) in 4.06 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 2: 20 98
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 2.000000000000e+01, best bound 2.000000000000e+01, gap 0.0000%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="no">sim_mrs_space</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_ru</span>, <span class="kw">amount.target</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>),
<span class="kw">space.target</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="fl">0.85</span>, <span class="fl">0.85</span>, <span class="fl">0.85</span>),
<span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 30306 rows, 30100 columns and 120000 nonzeros
## Model fingerprint: 0xba2fdd45
## Variable types: 0 continuous, 30100 integer (30100 binary)
## Coefficient statistics:
## Matrix range [7e-03, 9e+01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+00, 1e+02]
## Found heuristic solution: objective 99.0000000
## Presolve removed 360 rows and 15 columns (presolve time = 5s) ...
## Presolve removed 360 rows and 15 columns
## Presolve time: 6.18s
## Presolved: 29946 rows, 30085 columns, 131504 nonzeros
## Variable types: 0 continuous, 30085 integer (30085 binary)
## Presolve removed 67 rows and 1 columns
## Presolved: 29879 rows, 30084 columns, 127194 nonzeros
##
##
## Root simplex log...
##
## Iteration Objective Primal Inf. Dual Inf. Time
## 0 1.0000000e+02 0.000000e+00 1.000000e+02 7s
## 5346 2.0000000e+01 0.000000e+00 0.000000e+00 8s
## 5346 2.0000000e+01 0.000000e+00 0.000000e+00 8s
##
## Root relaxation: objective 2.000000e+01, 5346 iterations, 1.76 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## 0 0 20.00000 0 21 99.00000 20.00000 79.8% - 8s
## H 0 0 21.0000000 20.00000 4.76% - 8s
##
## Explored 1 nodes (10151 simplex iterations) in 8.42 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 2: 21 99
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 2.100000000000e+01, best bound 2.000000000000e+01, gap 4.7619%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summaries</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_mrs_amount</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 20 20 20 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 14 148 58 0.06363636</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_mrs_space</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 21 21 21 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 6 137 77 0.02727273</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show amount held for each prioritization</span>
<span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_mrs_amount</span>)</pre></body></html></div>
<pre><code>## uniform normal bimodal
## 1 0.2 0.2472436 0.2953565</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="fu"><a href="../reference/amount.held.html">amount.held</a></span>(<span class="no">sim_mrs_space</span>)</pre></body></html></div>
<pre><code>## uniform normal bimodal
## 1 0.21 0.2393104 0.2269545</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show space held for each prioritization</span>
<span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_mrs_amount</span>)</pre></body></html></div>
<pre><code>## uniform (Space 1) normal (Space 1) bimodal (Space 1)
## 1 0.8848485 0.8631116 0.9096401</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="fu"><a href="../reference/space.held.html">space.held</a></span>(<span class="no">sim_mrs_space</span>)</pre></body></html></div>
<pre><code>## uniform (Space 1) normal (Space 1) bimodal (Space 1)
## 1 0.929697 0.9170622 0.9278573</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot multi-species prioritization with amount-based targets</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_mrs_amount</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Amount-based targets"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-36-1.png" alt="_A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using just amount-based targets (20\%). Squares represent planning units. Dark green planning units are selected for preservation._" width="432"><p class="caption">
<em>A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using just amount-based targets (20%). Squares represent planning units. Dark green planning units are selected for preservation.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot multi-species prioritization with amount- and space-based targets</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_mrs_space</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Amount and space-based targets"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-37-1.png" alt="_A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using amount-based targets (20\%) and space-based targets (85\%). See Figure 12 caption for conventions._" width="432"><p class="caption">
<em>A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using amount-based targets (20%) and space-based targets (85%). See Figure 12 caption for conventions.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># difference between the two prioritizations</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_mrs_amount</span>, <span class="no">sim_mrs_space</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Difference between solutions"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-38-1.png" alt="_Difference between two multi-species prioritizations. See Figure 7 caption for conventions.)" width="576"><p class="caption">
_Difference between two multi-species prioritizations. See Figure 7 caption for conventions.)
</p>
</div>
<p>Here we can see that the inclusion of space-based targets changes which planning units are selected for prioritization, but also the number of planning units that are selected. The amount-based prioritization is comprised of 20 units, and the space-based prioritization is comprised of 21 units. This result suggests that an adequate and representative prioritization can be achieved for only a minor increase in cost.</p>
</div>
<div id="uncertainty-in-species-distributions" class="section level3">
<h3 class="hasAnchor">
<a href="#uncertainty-in-species-distributions" class="anchor"></a>Uncertainty in species’ distributions</h3>
<p>The unreliable formulation does not consider the probability that the planning units are occupied by features when calculating how well a given solution secures a representative sample of an attribute space. Thus solutions identified using the unreliable formulation may select regions of an attribute space for a species using planning units that only have a small chance of being inhabited. As a consequence, if the prioritization is implemented, it may fail to secure regions of an attribute space if individuals do not inhabit these planning units, and ultimately fail to fulfill the space-based targets.</p>
<p>A simple solution to this issue would be to ensure that planning units cannot be assigned to demand points if they have a low probability of occupancy. This can be achieved by setting a probability threshold for planning units, such that planning units with a probability of occupancy below the threshold are effectively set to zero.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make new prioritization with probability threshold of 0.1 for each species</span>
<span class="no">sim_mrs_space2</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/solve.html">solve</a></span>(<span class="fu"><a href="../reference/prob.subset.html">prob.subset</a></span>(<span class="no">sim_mrs_space</span>, <span class="kw">species</span> <span class="kw">=</span> <span class="fl">1</span>:<span class="fl">3</span>,
<span class="kw">threshold</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/pkg/RandomFields/man/QMath.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">0.1</span>, <span class="fl">0.1</span>)),
<span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 30306 rows, 30100 columns and 119974 nonzeros
## Model fingerprint: 0xb1d3b678
## Variable types: 0 continuous, 30100 integer (30100 binary)
## Coefficient statistics:
## Matrix range [7e-03, 9e+01]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+00, 1e+02]
## Found heuristic solution: objective 99.0000000
## Presolve removed 329 rows and 15 columns (presolve time = 5s) ...
## Presolve removed 360 rows and 15 columns
## Presolve time: 6.63s
## Presolved: 29946 rows, 30085 columns, 131478 nonzeros
## Variable types: 0 continuous, 30085 integer (30085 binary)
## Presolve removed 67 rows and 1 columns
## Presolved: 29879 rows, 30084 columns, 127168 nonzeros
##
##
## Root simplex log...
##
## Iteration Objective Primal Inf. Dual Inf. Time
## 0 1.0000000e+02 0.000000e+00 1.000000e+02 7s
## 5257 2.0000000e+01 0.000000e+00 0.000000e+00 8s
## 5257 2.0000000e+01 0.000000e+00 0.000000e+00 8s
##
## Root relaxation: objective 2.000000e+01, 5257 iterations, 1.68 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## 0 0 20.00000 0 22 99.00000 20.00000 79.8% - 9s
## H 0 0 21.0000000 20.00000 4.76% - 9s
##
## Explored 1 nodes (12431 simplex iterations) in 9.79 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 2: 21 99
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 2.100000000000e+01, best bound 2.000000000000e+01, gap 4.7619%</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summary</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_mrs_space2</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 21 21 21 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 9 139 72 0.04090909</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot prioritization</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_mrs_space2</span>, <span class="fl">1</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-42-1.png" alt="_A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using amount-based targets (20\%) and space-based targets (85\%). This prioritization was generated to be robust against low occupancy probabilities, by preventing planning units with low probabilities from being used to represent demand points. See Figure 12 caption for conventions._" width="432"><p class="caption">
<em>A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using amount-based targets (20%) and space-based targets (85%). This prioritization was generated to be robust against low occupancy probabilities, by preventing planning units with low probabilities from being used to represent demand points. See Figure 12 caption for conventions.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># difference between prioritizations that use and do not use thresholds</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_mrs_space2</span>, <span class="no">sim_mrs_space</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Difference between solutions"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-43-1.png" alt="_Difference between two multi-species prioritizations. See Figure 7 caption for conventions._" width="576"><p class="caption">
<em>Difference between two multi-species prioritizations. See Figure 7 caption for conventions.</em>
</p>
</div>
<p>But this method requires setting somewhat arbitrary thresholds. A more robust solution to this issue is to actually use the probability that species occupy planning units to generate the prioritizations. This is what the reliable formulation does. First we will try and generate a solution using existing targets and the reliable formulation. To reduce computational time, we will set the maximum backup <span class="math inline">\(R\)</span>-level to 1.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make new prioritization using reliable formulation</span>
<span class="no">sim_mrs_space3</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/try.html">try</a></span>(<span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_mrs_space</span>, <span class="kw">formulation</span> <span class="kw">=</span> <span class="st">"reliable"</span>,
<span class="kw">max.r.level</span> <span class="kw">=</span> <span class="fl">1L</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>))</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 364206 rows, 181900 columns and 3847200 nonzeros
## Model fingerprint: 0xf44c8001
## Variable types: 0 continuous, 60700 integer (60700 binary)
## Semi-Variable types: 121200 continuous, 0 integer
## Coefficient statistics:
## Matrix range [8e-04, 1e+02]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [7e-03, 1e+02]
## Presolve removed 338506 rows and 156703 columns
## Presolve time: 4.18s
##
## Explored 0 nodes (0 simplex iterations) in 4.72 seconds
## Thread count was 1 (of 4 available processors)
##
## Solution count 0
##
## Model is infeasible
## Best objective -, best bound -, gap -
## Error in .local(a, b, ...) :
## No solution found because the problem cannot be solved because space-based targets are too high. Try setting lower space-based targets. See ?maximum.targets</code></pre>
<p>However, this fails. The reason why we cannot generate a prioritization that fulfills these targets is because even the solution that contains all the planning units is still insufficient when we consider probabilities. The negative maximum targets printed in the error message indicate that planning units have low probabilities of occupancy. To fulfill the targets, we must obtain more planning units with higher probabilities of occupancy. We also could attempt resolving the problem using a higher <span class="math inline">\(R\)</span>-level. Instead, we will set lower targets and generate solution.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># make new prioritization using reliable formulation and reduced targets</span>
<span class="no">sim_mrs_space3</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_mrs_space</span>, <span class="kw">formulation</span> <span class="kw">=</span> <span class="st">"reliable"</span>,
<span class="kw">max.r.level</span> <span class="kw">=</span> <span class="fl">1L</span>, <span class="kw">space.target</span> <span class="kw">=</span> -<span class="fl">1000</span>,
<span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 364206 rows, 181900 columns and 3847200 nonzeros
## Model fingerprint: 0x197cd333
## Variable types: 0 continuous, 60700 integer (60700 binary)
## Semi-Variable types: 121200 continuous, 0 integer
## Coefficient statistics:
## Matrix range [8e-04, 1e+02]
## Objective range [1e+00, 1e+00]
## Bounds range [1e+00, 1e+00]
## RHS range [7e-03, 8e+05]
## Presolve removed 333703 rows and 151800 columns (presolve time = 5s) ...
## Presolve removed 333903 rows and 151800 columns
## Presolve time: 6.65s
## Presolved: 30303 rows, 30100 columns, 90300 nonzeros
## Variable types: 0 continuous, 30100 integer (30100 binary)
## Found heuristic solution: objective 27.0000000
## Presolved: 30303 rows, 30100 columns, 90300 nonzeros
##
##
## Root simplex log...
##
## Iteration Objective Primal Inf. Dual Inf. Time
## 0 1.0000000e+02 0.000000e+00 1.000000e+02 8s
## 129 2.0000000e+01 0.000000e+00 0.000000e+00 8s
## 129 2.0000000e+01 0.000000e+00 0.000000e+00 8s
##
## Root relaxation: objective 2.000000e+01, 129 iterations, 0.46 seconds
##
## Nodes | Current Node | Objective Bounds | Work
## Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
##
## 0 0 20.00000 0 3 27.00000 20.00000 25.9% - 7s
## H 0 0 20.0000000 20.00000 0.00% - 7s
## 0 0 20.00000 0 3 20.00000 20.00000 0.00% - 7s
##
## Explored 1 nodes (129 simplex iterations) in 7.98 seconds
## Thread count was 2 (of 4 available processors)
##
## Solution count 2: 20 27
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 2.000000000000e+01, best bound 2.000000000000e+01, gap 0.0000%</code></pre>
<pre><code>## Warning in validityMethod(object): some species have space.held values less than
## 0, and thus are poorly represented</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># show summary</span>
<span class="fu"><a href="../reference/summary.html">summary</a></span>(<span class="no">sim_mrs_space3</span>)</pre></body></html></div>
<pre><code>## Run_Number Status Score Cost Planning_Units Connectivity_Total
## 1 1 OPTIMAL 20 20 20 220
## Connectivity_In Connectivity_Edge Connectivity_Out Connectivity_In_Fraction
## 1 24 160 36 0.1090909</code></pre>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># plot prioritization</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_mrs_space3</span>, <span class="fl">1</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-47-1.png" alt="_A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using amount-based targets (20\%) and space-based targets (85\%). This prioritization was generated to be robust against low occupancy probabilities, by explicitly using the probability of occupancy data when deriving a solution. See Figure 12 caption for conventions._" width="432"><p class="caption">
<em>A multi-species prioritization for the uniformly, normally, and bimodally distributed species generated using amount-based targets (20%) and space-based targets (85%). This prioritization was generated to be robust against low occupancy probabilities, by explicitly using the probability of occupancy data when deriving a solution. See Figure 12 caption for conventions.</em>
</p>
</div>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># difference between prioritizations based on unreliable and reliable formulation</span>
<span class="fu"><a href="../reference/plot.html">plot</a></span>(<span class="no">sim_mrs_space3</span>, <span class="no">sim_mrs_space</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">main</span> <span class="kw">=</span> <span class="st">"Difference between solutions"</span>)</pre></body></html></div>
<div class="figure" style="text-align: center">
<img src="raptr_files/figure-html/unnamed-chunk-48-1.png" alt="_Difference between two multi-species prioritizations. See Figure 7 caption for conventions._" width="576"><p class="caption">
<em>Difference between two multi-species prioritizations. See Figure 7 caption for conventions.</em>
</p>
</div>
<p>An additional planning unit was selected using the reliable formulation. The prioritization based on the unreliable formulation had 21 planning units, but the prioritization based on the reliable formulation has 20 planning units. This difference occurs because the reliable formulation needs to ensure that all selected planning units with a low chance of being occupied have a suitable backup planning unit. While the reliable formulation can deliver more robust prioritizations, it takes much longer to solve conservation planning problems expressed using this formulation than the unreliable formulation. As a consequence, the reliable formulation is only feasible for particularly small problems, such as those involving few features and less than several hundred planning units.</p>
</div>
<div id="fragmentation" class="section level3">
<h3 class="hasAnchor">
<a href="#fragmentation" class="anchor"></a>Fragmentation</h3>
<p>Fragmentation is an important consideration in real-world planning situations. Up until now, we haven’t considered the effects of fragmentation on the viability of the prioritization. As a consequence, our prioritizations have tended to contain planning units without any neighbors. We can use the <code>BLM</code> parameter to penalize fragmented solutions.</p>
<p>Let’s generate a new prioritization that heavily penalises fragmentation. Here, we will update the <code>sim_mrs_amount</code> object with <code>BLM</code> of 100.</p>
<div class="sourceCode"><html><body><pre class="r"><span class="co"># update prioritization</span>
<span class="no">sim_mrs_amount_blm</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/update.html">update</a></span>(<span class="no">sim_mrs_amount</span>, <span class="kw">BLM</span> <span class="kw">=</span> <span class="fl">100</span>, <span class="kw">Threads</span> <span class="kw">=</span> <span class="no">threads</span>)</pre></body></html></div>
<pre><code>## Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (linux64)
## Optimize a model with 30666 rows, 30280 columns and 120720 nonzeros
## Model fingerprint: 0x20b44ac4
## Variable types: 0 continuous, 30280 integer (30280 binary)
## Coefficient statistics:
## Matrix range [7e-03, 9e+01]
## Objective range [1e+02, 4e+02]
## Bounds range [1e+00, 1e+00]
## RHS range [1e+00, 8e+02]
## Found heuristic solution: objective 4498.0000000
## Presolve time: 2.66s
## Presolved: 30666 rows, 30280 columns, 120720 nonzeros
## Variable types: 0 continuous, 30280 integer (30280 binary)
## Presolved: 30666 rows, 30280 columns, 120720 nonzeros
##