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<h2 id="toc-title">Table of contents</h2>
<ul>
<li><a href="#overview" id="toc-overview" class="nav-link active" data-scroll-target="#overview"><span class="header-section-number">1</span> Overview</a></li>
<li><a href="#species-map-blue-whale" id="toc-species-map-blue-whale" class="nav-link" data-scroll-target="#species-map-blue-whale"><span class="header-section-number">2</span> Species map (blue whale)</a>
<ul class="collapse">
<li><a href="#zoom-to-socal" id="toc-zoom-to-socal" class="nav-link" data-scroll-target="#zoom-to-socal"><span class="header-section-number">2.1</span> Zoom to SoCal</a></li>
</ul></li>
<li><a href="#environmental-preferences" id="toc-environmental-preferences" class="nav-link" data-scroll-target="#environmental-preferences"><span class="header-section-number">3</span> Environmental preferences</a></li>
<li><a href="#depth-gebco-for-socal" id="toc-depth-gebco-for-socal" class="nav-link" data-scroll-target="#depth-gebco-for-socal"><span class="header-section-number">4</span> Depth (GEBCO) for SoCal</a></li>
<li><a href="#ramp-depth-with-species-preference" id="toc-ramp-depth-with-species-preference" class="nav-link" data-scroll-target="#ramp-depth-with-species-preference"><span class="header-section-number">5</span> Ramp depth with species preference</a>
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<li><a href="#create-ramp_env-function" id="toc-create-ramp_env-function" class="nav-link" data-scroll-target="#create-ramp_env-function"><span class="header-section-number">5.1</span> Create <code>ramp_env()</code> function</a></li>
<li><a href="#apply-to-socal" id="toc-apply-to-socal" class="nav-link" data-scroll-target="#apply-to-socal"><span class="header-section-number">5.2</span> Apply to SoCal</a></li>
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<li><a href="#sdmpredictors" id="toc-sdmpredictors" class="nav-link" data-scroll-target="#sdmpredictors"><span class="header-section-number">6</span> <code>sdmpredictors</code></a></li>
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<li><a href="#next-steps" id="toc-next-steps" class="nav-link" data-scroll-target="#next-steps"><span class="header-section-number">9</span> Next Steps</a></li>
<li><a href="#links-from-gee" id="toc-links-from-gee" class="nav-link" data-scroll-target="#links-from-gee"><span class="header-section-number">10</span> Links from GEE</a></li>
<li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references"><span class="header-section-number">11</span> References</a></li>
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<div class="quarto-title-block"><div><h1 class="title">Downscaling AquaMaps</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
<p class="subtitle lead">v01: blue whale, GEBCO SoCal</p>
</div>
<div class="quarto-title-meta">
<div>
<div class="quarto-title-meta-heading">Author</div>
<div class="quarto-title-meta-contents">
<p>Benjamin D. Best <a href="mailto:[email protected]" class="email">[email protected]</a> </p>
</div>
</div>
<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
<p class="date">2023-08-14 15:59 (PDT)</p>
</div>
</div>
</div>
</header>
<section id="overview" class="level1" data-number="1">
<h1 data-number="1"><span class="header-section-number">1</span> Overview</h1>
<p><strong>Goal</strong>: Downscale <a href="https://aquamaps.org">AquaMaps.org</a> species distributions <span class="citation" data-cites="kaschnerAquaMapsPredictedRange2023 readyPredictingDistributionsMarine2010">(<a href="#ref-kaschnerAquaMapsPredictedRange2023" role="doc-biblioref">Kaschner et al. 2023</a>; <a href="#ref-readyPredictingDistributionsMarine2010" role="doc-biblioref">Ready et al. 2010</a>)</span> from 0.5 decimal degrees to 15 arc seconds (111.11 km to 0.46 km at the equator), using the R package <a href="https://raquamaps.github.io/aquamapsdata/index.html"><code>aquamapsdata</code></a> and the the General Bathymetric Chart of the Oceans <a href="https://www.gebco.net/">GEBCO</a>.</p>
<p>We start with the “Blue Whale” (<a href="https://aquamaps.org/preMap2.php?cache=1&SpecID=ITS-Mam-180528"><em>Balaenoptera musculus</em></a>) and Southern California.</p>
<p>Later we’ll iterate over species and expand to global, which will require large raster handling techniques using Cloud-Optimized GeoTIFFS (COGs; see <a href="https://www.cogeo.org">cogeo.org</a>).</p>
<p>All code and files (except the large global GEBCO grid) are found in this repository:</p>
<ul>
<li><a href="https://github.com/marinebon/aquamaps-downscaled">github.com/marinebon/aquamaps-downscaled</a></li>
</ul>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># packages ----</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="st">"librarian"</span> <span class="sc">%in%</span> <span class="fu">installed.packages</span>())</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">install.packages</span>(<span class="st">"librarian"</span>)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="st">"rcrypt"</span> <span class="sc">%in%</span> <span class="fu">installed.packages</span>())</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> devtools<span class="sc">::</span><span class="fu">install_bitbucket</span>(<span class="st">"bklamer/rcrypt"</span>) <span class="co"># dependency for aquamapsdata</span></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>librarian<span class="sc">::</span><span class="fu">shelf</span>(</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> bklamer<span class="sc">/</span>rcrypt,</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a> raquamaps<span class="sc">/</span>aquamapsdata,</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a> dplyr, ggplot2, glue, here, knitr, leaflet, </span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a> <span class="co"># </span><span class="al">TODO</span><span class="co">: migrate raster to terra</span></span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a> <span class="co"># terra, </span></span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a> raster, rnaturalearth, sf, stringr, tidyr,</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a> <span class="at">quiet =</span> T)</span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>select <span class="ot">=</span> dplyr<span class="sc">::</span>select</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="co"># initial run-once step required to install remote db locally</span></span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="co"># download_db(force = TRUE)</span></span>
<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="co"># aquamaps database ----</span></span>
<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>am_db <span class="ot"><-</span> <span class="fu">default_db</span>(<span class="st">"sqlite"</span>)</span>
<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a><span class="co"># paths ----</span></span>
<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a>dir_big <span class="ot"><-</span> <span class="st">"/Users/bbest/big"</span></span>
<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>gebco_nc <span class="ot"><-</span> <span class="fu">glue</span>(<span class="st">"{dir_big}/gebco_2022_sub_ice_topo/GEBCO_2022_sub_ice_topo.nc"</span>)</span>
<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>gebco_socal_tif <span class="ot"><-</span> <span class="fu">here</span>(<span class="st">"data/gebco_socal.tif"</span>)</span>
<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a>land_socal_geo <span class="ot"><-</span> <span class="fu">here</span>(<span class="st">"data/land_socal.geojson"</span>)</span>
<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>bo_tif <span class="ot"><-</span> <span class="fu">here</span>(<span class="st">"data/bio-oracle.tif"</span>)</span>
<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a><span class="co"># custom functions ----</span></span>
<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a>add_ocean_basemap <span class="ot"><-</span> <span class="cf">function</span>(m){</span>
<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a> <span class="co"># m: leaflet() map</span></span>
<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a> m <span class="sc">|></span></span>
<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a> <span class="co"># add base: blue bathymetry and light brown/green topography</span></span>
<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a> <span class="fu">addProviderTiles</span>(</span>
<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a> <span class="st">"Esri.OceanBasemap"</span>,</span>
<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a> <span class="at">options =</span> <span class="fu">providerTileOptions</span>(</span>
<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a> <span class="at">variant =</span> <span class="st">"Ocean/World_Ocean_Base"</span>)) <span class="sc">|></span></span>
<span id="cb1-39"><a href="#cb1-39" aria-hidden="true" tabindex="-1"></a> <span class="co"># add reference: placename labels and borders</span></span>
<span id="cb1-40"><a href="#cb1-40" aria-hidden="true" tabindex="-1"></a> <span class="fu">addProviderTiles</span>(</span>
<span id="cb1-41"><a href="#cb1-41" aria-hidden="true" tabindex="-1"></a> <span class="st">"Esri.OceanBasemap"</span>,</span>
<span id="cb1-42"><a href="#cb1-42" aria-hidden="true" tabindex="-1"></a> <span class="at">options =</span> <span class="fu">providerTileOptions</span>(</span>
<span id="cb1-43"><a href="#cb1-43" aria-hidden="true" tabindex="-1"></a> <span class="at">variant =</span> <span class="st">"Ocean/World_Ocean_Reference"</span>))</span>
<span id="cb1-44"><a href="#cb1-44" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb1-45"><a href="#cb1-45" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-46"><a href="#cb1-46" aria-hidden="true" tabindex="-1"></a>add_am_raster <span class="ot"><-</span> <span class="cf">function</span>(</span>
<span id="cb1-47"><a href="#cb1-47" aria-hidden="true" tabindex="-1"></a> m, </span>
<span id="cb1-48"><a href="#cb1-48" aria-hidden="true" tabindex="-1"></a> r,</span>
<span id="cb1-49"><a href="#cb1-49" aria-hidden="true" tabindex="-1"></a> title,</span>
<span id="cb1-50"><a href="#cb1-50" aria-hidden="true" tabindex="-1"></a> <span class="at">cols =</span> <span class="fu">c</span>(<span class="st">"#FEB24C"</span>, <span class="st">"#FD8D3C"</span>, <span class="st">"#FC4E2A"</span>, <span class="st">"#E31A1C"</span>, <span class="st">"#B10026"</span>),</span>
<span id="cb1-51"><a href="#cb1-51" aria-hidden="true" tabindex="-1"></a> <span class="at">truncate_to_zero =</span> T){</span>
<span id="cb1-52"><a href="#cb1-52" aria-hidden="true" tabindex="-1"></a> <span class="co"># m: leaflet() map</span></span>
<span id="cb1-53"><a href="#cb1-53" aria-hidden="true" tabindex="-1"></a> <span class="co"># r: raster </span></span>
<span id="cb1-54"><a href="#cb1-54" aria-hidden="true" tabindex="-1"></a> <span class="co"># </span><span class="al">TODO</span><span class="co">: migrate to terra::rast()</span></span>
<span id="cb1-55"><a href="#cb1-55" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb1-56"><a href="#cb1-56" aria-hidden="true" tabindex="-1"></a> <span class="co"># r = r_gebco_bb</span></span>
<span id="cb1-57"><a href="#cb1-57" aria-hidden="true" tabindex="-1"></a> <span class="co"># title = "GEBCO depth (m)"</span></span>
<span id="cb1-58"><a href="#cb1-58" aria-hidden="true" tabindex="-1"></a> <span class="co"># cols = RColorBrewer::brewer.pal(7, "Blues")</span></span>
<span id="cb1-59"><a href="#cb1-59" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb1-60"><a href="#cb1-60" aria-hidden="true" tabindex="-1"></a> r <span class="ot"><-</span> leaflet<span class="sc">::</span><span class="fu">projectRasterForLeaflet</span>(r, <span class="at">method =</span> <span class="st">"bilinear"</span>)</span>
<span id="cb1-61"><a href="#cb1-61" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb1-62"><a href="#cb1-62" aria-hidden="true" tabindex="-1"></a> <span class="co"># truncate to 0 to prevent negative values </span></span>
<span id="cb1-63"><a href="#cb1-63" aria-hidden="true" tabindex="-1"></a> <span class="co"># that were generated by projecting the raster </span></span>
<span id="cb1-64"><a href="#cb1-64" aria-hidden="true" tabindex="-1"></a> <span class="co"># from geographic projection (decimal degrees) to Web Mercator (meters)</span></span>
<span id="cb1-65"><a href="#cb1-65" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (truncate_to_zero){</span>
<span id="cb1-66"><a href="#cb1-66" aria-hidden="true" tabindex="-1"></a> v <span class="ot"><-</span> <span class="fu">values</span>(r)</span>
<span id="cb1-67"><a href="#cb1-67" aria-hidden="true" tabindex="-1"></a> v[v<span class="sc"><</span><span class="dv">0</span>] <span class="ot"><-</span> <span class="dv">0</span></span>
<span id="cb1-68"><a href="#cb1-68" aria-hidden="true" tabindex="-1"></a> <span class="fu">values</span>(r) <span class="ot"><-</span> v</span>
<span id="cb1-69"><a href="#cb1-69" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb1-70"><a href="#cb1-70" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb1-71"><a href="#cb1-71" aria-hidden="true" tabindex="-1"></a> pal <span class="ot"><-</span> leaflet<span class="sc">::</span><span class="fu">colorBin</span>(</span>
<span id="cb1-72"><a href="#cb1-72" aria-hidden="true" tabindex="-1"></a> cols, <span class="fu">na.omit</span>(<span class="fu">unique</span>(<span class="fu">values</span>(r))), </span>
<span id="cb1-73"><a href="#cb1-73" aria-hidden="true" tabindex="-1"></a> <span class="at">bins =</span> <span class="fu">length</span>(cols), <span class="at">pretty =</span> <span class="cn">TRUE</span>, <span class="at">na.color =</span> <span class="st">"#00000000"</span>)</span>
<span id="cb1-74"><a href="#cb1-74" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb1-75"><a href="#cb1-75" aria-hidden="true" tabindex="-1"></a> e <span class="ot"><-</span> raster<span class="sc">::</span><span class="fu">extent</span>(r) <span class="sc">|></span> </span>
<span id="cb1-76"><a href="#cb1-76" aria-hidden="true" tabindex="-1"></a> sf<span class="sc">::</span><span class="fu">st_bbox</span>() <span class="sc">|></span> </span>
<span id="cb1-77"><a href="#cb1-77" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_as_sfc</span>() <span class="sc">|></span> </span>
<span id="cb1-78"><a href="#cb1-78" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_as_sf</span>(<span class="at">crs=</span><span class="dv">3857</span>) <span class="sc">|></span> </span>
<span id="cb1-79"><a href="#cb1-79" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_transform</span>(<span class="dv">4326</span>) <span class="sc">|></span> </span>
<span id="cb1-80"><a href="#cb1-80" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_bbox</span>()</span>
<span id="cb1-81"><a href="#cb1-81" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb1-82"><a href="#cb1-82" aria-hidden="true" tabindex="-1"></a> m <span class="sc">|></span> </span>
<span id="cb1-83"><a href="#cb1-83" aria-hidden="true" tabindex="-1"></a> leaflet<span class="sc">::</span><span class="fu">addRasterImage</span>(</span>
<span id="cb1-84"><a href="#cb1-84" aria-hidden="true" tabindex="-1"></a> r, <span class="at">project =</span> F, <span class="at">colors =</span> pal, <span class="at">opacity =</span> <span class="fl">0.8</span>) <span class="sc">|></span> </span>
<span id="cb1-85"><a href="#cb1-85" aria-hidden="true" tabindex="-1"></a> <span class="fu">addLegend</span>(</span>
<span id="cb1-86"><a href="#cb1-86" aria-hidden="true" tabindex="-1"></a> <span class="at">values =</span> raster<span class="sc">::</span><span class="fu">values</span>(r), </span>
<span id="cb1-87"><a href="#cb1-87" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> title, <span class="at">pal =</span> pal) <span class="sc">|></span> </span>
<span id="cb1-88"><a href="#cb1-88" aria-hidden="true" tabindex="-1"></a> leaflet<span class="sc">::</span><span class="fu">fitBounds</span>(</span>
<span id="cb1-89"><a href="#cb1-89" aria-hidden="true" tabindex="-1"></a> <span class="at">lng1 =</span> e[[<span class="st">"xmin"</span>]], </span>
<span id="cb1-90"><a href="#cb1-90" aria-hidden="true" tabindex="-1"></a> <span class="at">lat1 =</span> e[[<span class="st">"ymin"</span>]], </span>
<span id="cb1-91"><a href="#cb1-91" aria-hidden="true" tabindex="-1"></a> <span class="at">lng2 =</span> e[[<span class="st">"xmax"</span>]], </span>
<span id="cb1-92"><a href="#cb1-92" aria-hidden="true" tabindex="-1"></a> <span class="at">lat2 =</span> e[[<span class="st">"ymax"</span>]])</span>
<span id="cb1-93"><a href="#cb1-93" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="species-map-blue-whale" class="level1" data-number="2">
<h1 data-number="2"><span class="header-section-number">2</span> Species map (blue whale)</h1>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># fuzzy search allows full text search operators AND, OR, NOT and +</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co"># see https://www.sqlitetutorial.net/sqlite-full-text-search/</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>sp_term <span class="ot"><-</span> <span class="st">"blue whale"</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>key <span class="ot"><-</span> <span class="fu">am_search_fuzzy</span>(<span class="at">search_term =</span> sp_term) <span class="sc">|></span> </span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">pull</span>(key) <span class="co"># "ITS-Mam-180528"</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a><span class="co"># get the identifier for the species</span></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>r <span class="ot"><-</span> <span class="fu">am_raster</span>(key)</span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="co"># show the native habitat map</span></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a>m <span class="ot"><-</span> <span class="fu">leaflet</span>() <span class="sc">|></span> </span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">add_ocean_basemap</span>() <span class="sc">|></span> </span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">add_am_raster</span>(r, <span class="at">title =</span> sp_term)</span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a>m </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="fig-blue_whale_map" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<div class="leaflet html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-49904896084077541b6b" style="width:100%;height:464px;"></div>
<script type="application/json" 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",[[87.75,-179.75],[-78.24999999999999,179.75]],0.8,null,null,null]},{"method":"addLegend","args":[{"colors":["#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],"labels":["0.0 – 0.2","0.2 – 0.4","0.4 – 0.6","0.6 – 0.8","0.8 – 1.0"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"topright","type":"bin","title":"blue whale","extra":null,"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[-78.24999999999999,87.75],"lng":[-179.75,179.75]},"fitBounds":[-78.24999999999999,-179.75,87.75,179.75,[]]},"evals":[],"jsHooks":[]}</script>
<figcaption class="figure-caption">Figure 1: Map of blue whale (<em>Balaenoptera musculus</em>) distribution from AquaMaps.</figcaption>
</figure>
</div>
</div>
<section id="zoom-to-socal" class="level2" data-number="2.1">
<h2 data-number="2.1" class="anchored" data-anchor-id="zoom-to-socal"><span class="header-section-number">2.1</span> Zoom to SoCal</h2>
<p>Notice the very large pixels, far bigger than useful for smaller planning purposes, such as for Sanctuaries or BOEM Wind Energy Areas.</p>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Southern California</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>bbox <span class="ot"><-</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">121</span>, <span class="dv">32</span>, <span class="sc">-</span><span class="dv">117</span>, <span class="dv">35</span>)</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>m <span class="sc">|></span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">fitBounds</span>(<span class="at">lng1 =</span> bbox[<span class="dv">1</span>], <span class="at">lat1 =</span> bbox[<span class="dv">2</span>], <span class="at">lng2 =</span> bbox[<span class="dv">3</span>], <span class="at">lat2 =</span> bbox[<span class="dv">4</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="fig-blue_whale_map_socal" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
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",[[87.75,-179.75],[-78.24999999999999,179.75]],0.8,null,null,null]},{"method":"addLegend","args":[{"colors":["#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],"labels":["0.0 – 0.2","0.2 – 0.4","0.4 – 0.6","0.6 – 0.8","0.8 – 1.0"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"topright","type":"bin","title":"blue whale","extra":null,"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[-78.24999999999999,87.75],"lng":[-179.75,179.75]},"fitBounds":[32,-121,35,-117,[]]},"evals":[],"jsHooks":[]}</script>
<figcaption class="figure-caption">Figure 2: Map of blue whale (<em>Balaenoptera musculus</em>) distribution from AquaMaps zoomed into Southern California. Notice the very large pixels, far bigger than useful for smaller planning purposes, such as for Sanctuaries or BOEM Wind Energy Areas.</figcaption>
</figure>
</div>
</div>
</section>
</section>
<section id="environmental-preferences" class="level1" data-number="3">
<h1 data-number="3"><span class="header-section-number">3</span> Environmental preferences</h1>
<p>Here are the environmental preferences for the species in the database.</p>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>sp_env <span class="ot"><-</span> <span class="fu">am_hspen</span>() <span class="sc">|></span> </span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(SpeciesID <span class="sc">==</span> key) <span class="sc">|></span> </span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">head</span>(<span class="dv">1</span>) <span class="sc">|></span> </span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">collect</span>()</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>sp_env <span class="sc">|></span> </span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">everything</span>(), as.character)) <span class="sc">|></span> </span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(<span class="fu">everything</span>()) <span class="sc">|></span> </span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="tbl-blue_whale_env" class="anchored">
<table class="table table-sm table-striped small">
<caption>Table 1: Table of blue whale (<em>Balaenoptera musculus</em>) environmental suitability parameters from Aquamaps.</caption>
<colgroup>
<col style="width: 18%">
<col style="width: 81%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">name</th>
<th style="text-align: left;">value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">SpeciesID</td>
<td style="text-align: left;">ITS-Mam-180528</td>
</tr>
<tr class="even">
<td style="text-align: left;">Speccode</td>
<td style="text-align: left;">69007</td>
</tr>
<tr class="odd">
<td style="text-align: left;">LifeStage</td>
<td style="text-align: left;">adults</td>
</tr>
<tr class="even">
<td style="text-align: left;">FAOAreas</td>
<td style="text-align: left;">18, 21, 27, 31, 34, 41, 47, 48, 51, 57, 58, 61, 67, 71, 77, 81, 87, 88</td>
</tr>
<tr class="odd">
<td style="text-align: left;">FAOComplete</td>
<td style="text-align: left;">NA</td>
</tr>
<tr class="even">
<td style="text-align: left;">NMostLat</td>
<td style="text-align: left;">90</td>
</tr>
<tr class="odd">
<td style="text-align: left;">SMostLat</td>
<td style="text-align: left;">-90</td>
</tr>
<tr class="even">
<td style="text-align: left;">WMostLong</td>
<td style="text-align: left;">-180</td>
</tr>
<tr class="odd">
<td style="text-align: left;">EMostLong</td>
<td style="text-align: left;">180</td>
</tr>
<tr class="even">
<td style="text-align: left;">DepthYN</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">DepthMin</td>
<td style="text-align: left;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">DepthPrefMin</td>
<td style="text-align: left;">1000</td>
</tr>
<tr class="odd">
<td style="text-align: left;">DepthPrefMax</td>
<td style="text-align: left;">4000</td>
</tr>
<tr class="even">
<td style="text-align: left;">DepthMax</td>
<td style="text-align: left;">8000</td>
</tr>
<tr class="odd">
<td style="text-align: left;">MeanDepth</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">Pelagic</td>
<td style="text-align: left;">0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">TempYN</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">TempMin</td>
<td style="text-align: left;">-1.8</td>
</tr>
<tr class="odd">
<td style="text-align: left;">TempPrefMin</td>
<td style="text-align: left;">-1.3</td>
</tr>
<tr class="even">
<td style="text-align: left;">TempPrefMax</td>
<td style="text-align: left;">27.87</td>
</tr>
<tr class="odd">
<td style="text-align: left;">TempMax</td>
<td style="text-align: left;">32.07</td>
</tr>
<tr class="even">
<td style="text-align: left;">SalinityYN</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">SalinityMin</td>
<td style="text-align: left;">3.58</td>
</tr>
<tr class="even">
<td style="text-align: left;">SalinityPrefMin</td>
<td style="text-align: left;">32.57</td>
</tr>
<tr class="odd">
<td style="text-align: left;">SalinityPrefMax</td>
<td style="text-align: left;">35.49</td>
</tr>
<tr class="even">
<td style="text-align: left;">SalinityMax</td>
<td style="text-align: left;">38.84</td>
</tr>
<tr class="odd">
<td style="text-align: left;">PrimProdYN</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">PrimProdMin</td>
<td style="text-align: left;">0.1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">PrimProdPrefMin</td>
<td style="text-align: left;">1.4</td>
</tr>
<tr class="even">
<td style="text-align: left;">PrimProdPrefMax</td>
<td style="text-align: left;">16.07</td>
</tr>
<tr class="odd">
<td style="text-align: left;">PrimProdMax</td>
<td style="text-align: left;">119.58</td>
</tr>
<tr class="even">
<td style="text-align: left;">IceConYN</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">IceConMin</td>
<td style="text-align: left;">-0.88</td>
</tr>
<tr class="even">
<td style="text-align: left;">IceConPrefMin</td>
<td style="text-align: left;">0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">IceConPrefMax</td>
<td style="text-align: left;">0.49</td>
</tr>
<tr class="even">
<td style="text-align: left;">IceConMax</td>
<td style="text-align: left;">0.96</td>
</tr>
<tr class="odd">
<td style="text-align: left;">OxyYN</td>
<td style="text-align: left;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">OxyMin</td>
<td style="text-align: left;">1.1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">OxyPrefMin</td>
<td style="text-align: left;">116.82</td>
</tr>
<tr class="even">
<td style="text-align: left;">OxyPrefMax</td>
<td style="text-align: left;">275.01</td>
</tr>
<tr class="odd">
<td style="text-align: left;">OxyMax</td>
<td style="text-align: left;">408.48</td>
</tr>
<tr class="even">
<td style="text-align: left;">LandDistYN</td>
<td style="text-align: left;">0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">LandDistMin</td>
<td style="text-align: left;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">LandDistPrefMin</td>
<td style="text-align: left;">17</td>
</tr>
<tr class="odd">
<td style="text-align: left;">LandDistPrefMax</td>
<td style="text-align: left;">733</td>
</tr>
<tr class="even">
<td style="text-align: left;">LandDistMax</td>
<td style="text-align: left;">1740</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Remark</td>
<td style="text-align: left;">FAO areas,bounding box and/or pelagic flag based on last review.</td>
</tr>
<tr class="even">
<td style="text-align: left;">DateCreated</td>
<td style="text-align: left;">2019-08-07 00:00:00</td>
</tr>
<tr class="odd">
<td style="text-align: left;">DateModified</td>
<td style="text-align: left;">NA</td>
</tr>
<tr class="even">
<td style="text-align: left;">expert_id</td>
<td style="text-align: left;">NA</td>
</tr>
<tr class="odd">
<td style="text-align: left;">DateExpert</td>
<td style="text-align: left;">NA</td>
</tr>
<tr class="even">
<td style="text-align: left;">Layer</td>
<td style="text-align: left;">s</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Rank</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">MapOpt</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">ExtnRuleYN</td>
<td style="text-align: left;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">Reviewed</td>
<td style="text-align: left;">1</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<p>Now let’s convert all variables having <code>{Var}YN == 1</code> into the relative environmental suitability rhomboids <span class="citation" data-cites="kaschnerMappingWorldwideDistributions2006">(<a href="#ref-kaschnerMappingWorldwideDistributions2006" role="doc-biblioref">Kaschner et al. 2006</a>)</span>.</p>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>var <span class="ot"><-</span> <span class="st">"Depth"</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>d_probs <span class="ot"><-</span> <span class="fu">tribble</span>(</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="sc">~</span>prob_name, <span class="sc">~</span>prob_value,</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> <span class="st">"Min"</span> , <span class="dv">0</span>,</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"PrefMin"</span> , <span class="dv">1</span>,</span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"PrefMax"</span> , <span class="dv">1</span>,</span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"Max"</span> , <span class="dv">0</span>)</span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>vars_yes <span class="ot"><-</span> sp_env <span class="sc">|></span> </span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="fu">ends_with</span>(<span class="st">"YN"</span>)) <span class="sc">|></span> </span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(</span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">everything</span>()) <span class="sc">|></span> </span>
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(value <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">|></span> </span>
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">pull</span>(name) <span class="sc">|></span> </span>
<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">str_replace</span>(<span class="st">"YN"</span>,<span class="st">""</span>)</span>
<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a>d <span class="ot"><-</span> sp_env <span class="sc">|></span> </span>
<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="fu">starts_with</span>(vars_yes)) <span class="sc">|></span></span>
<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">!</span><span class="fu">ends_with</span>(<span class="st">"YN"</span>)) <span class="sc">|></span> </span>
<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(</span>
<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a> <span class="fu">everything</span>(),</span>
<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a> <span class="at">values_to =</span> <span class="st">"var_value"</span>) <span class="sc">|></span> </span>
<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a> <span class="fu">separate_wider_regex</span>(</span>
<span id="cb5-25"><a href="#cb5-25" aria-hidden="true" tabindex="-1"></a> name,</span>
<span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">var =</span> <span class="fu">paste</span>(vars_yes, <span class="at">collapse =</span> <span class="st">"|"</span>), <span class="co"># "",</span></span>
<span id="cb5-27"><a href="#cb5-27" aria-hidden="true" tabindex="-1"></a> <span class="at">prob_name =</span> <span class="fu">paste</span>(d_probs<span class="sc">$</span>prob_name, <span class="at">collapse =</span> <span class="st">"|"</span>))) <span class="sc">|></span> </span>
<span id="cb5-28"><a href="#cb5-28" aria-hidden="true" tabindex="-1"></a> <span class="fu">left_join</span>(</span>
<span id="cb5-29"><a href="#cb5-29" aria-hidden="true" tabindex="-1"></a> d_probs,</span>
<span id="cb5-30"><a href="#cb5-30" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="st">"prob_name"</span>)</span>
<span id="cb5-31"><a href="#cb5-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-32"><a href="#cb5-32" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(d)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="tbl-blue_whale_env_yes" class="anchored">
<table class="table table-sm table-striped small">
<caption>Table 2: Table environmental suitability parameters from Aquamaps that are applicable to blue whale (<em>Balaenoptera musculus</em>), i.e. <code>{Var}YN == 1</code> in <a href="#tbl-blue_whale_env">Table 1</a>.</caption>
<thead>
<tr class="header">
<th style="text-align: left;">var</th>
<th style="text-align: left;">prob_name</th>
<th style="text-align: right;">var_value</th>
<th style="text-align: right;">prob_value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Depth</td>
<td style="text-align: left;">Min</td>
<td style="text-align: right;">0.00</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">Depth</td>
<td style="text-align: left;">PrefMin</td>
<td style="text-align: right;">1000.00</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Depth</td>
<td style="text-align: left;">PrefMax</td>
<td style="text-align: right;">4000.00</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">Depth</td>
<td style="text-align: left;">Max</td>
<td style="text-align: right;">8000.00</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Temp</td>
<td style="text-align: left;">Min</td>
<td style="text-align: right;">-1.80</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">Temp</td>
<td style="text-align: left;">PrefMin</td>
<td style="text-align: right;">-1.30</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Temp</td>
<td style="text-align: left;">PrefMax</td>
<td style="text-align: right;">27.87</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">Temp</td>
<td style="text-align: left;">Max</td>
<td style="text-align: right;">32.07</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Salinity</td>
<td style="text-align: left;">Min</td>
<td style="text-align: right;">3.58</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">Salinity</td>
<td style="text-align: left;">PrefMin</td>
<td style="text-align: right;">32.57</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Salinity</td>
<td style="text-align: left;">PrefMax</td>
<td style="text-align: right;">35.49</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">Salinity</td>
<td style="text-align: left;">Max</td>
<td style="text-align: right;">38.84</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">PrimProd</td>
<td style="text-align: left;">Min</td>
<td style="text-align: right;">0.10</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">PrimProd</td>
<td style="text-align: left;">PrefMin</td>
<td style="text-align: right;">1.40</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">PrimProd</td>
<td style="text-align: left;">PrefMax</td>
<td style="text-align: right;">16.07</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">PrimProd</td>
<td style="text-align: left;">Max</td>
<td style="text-align: right;">119.58</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="odd">
<td style="text-align: left;">IceCon</td>
<td style="text-align: left;">Min</td>
<td style="text-align: right;">-0.88</td>
<td style="text-align: right;">0</td>
</tr>
<tr class="even">
<td style="text-align: left;">IceCon</td>
<td style="text-align: left;">PrefMin</td>
<td style="text-align: right;">0.00</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="odd">
<td style="text-align: left;">IceCon</td>
<td style="text-align: left;">PrefMax</td>
<td style="text-align: right;">0.49</td>
<td style="text-align: right;">1</td>
</tr>
<tr class="even">
<td style="text-align: left;">IceCon</td>
<td style="text-align: left;">Max</td>
<td style="text-align: right;">0.96</td>
<td style="text-align: right;">0</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>g <span class="ot"><-</span> <span class="fu">ggplot</span>(d, <span class="fu">aes</span>(var_value, prob_value)) <span class="sc">+</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_area</span>() <span class="sc">+</span></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(</span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">vars</span>(var), </span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> <span class="at">scales =</span> <span class="st">"free"</span>) <span class="sc">+</span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> sp_term,</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a> <span class="at">subtitle =</span> <span class="st">"environmental envelope"</span>,</span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"probability of presence"</span>)</span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>g</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-blue_whale_env_yes" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<p><img src="index_files/figure-html/fig-blue_whale_env_yes-1.png" class="img-fluid figure-img" width="672"></p>
<figcaption class="figure-caption">Figure 3: Plots of environmental suitability parameters from Aquamaps that are applicable to blue whale (<em>Balaenoptera musculus</em>) from <a href="#tbl-blue_whale_env_yes">Table 2</a>.</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="depth-gebco-for-socal" class="level1" data-number="4">
<h1 data-number="4"><span class="header-section-number">4</span> Depth (GEBCO) for SoCal</h1>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># limit to bounding box for now</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>ply_bb <span class="ot"><-</span> <span class="fu">extent</span>(</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(bbox[<span class="dv">1</span>], bbox[<span class="dv">3</span>], bbox[<span class="dv">2</span>], bbox[<span class="dv">4</span>])) <span class="sc">|></span> </span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_bbox</span>() <span class="sc">|></span> </span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_as_sfc</span>() <span class="sc">|></span> </span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_as_sf</span>(<span class="at">crs =</span> <span class="dv">4326</span>)</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a><span class="co"># land</span></span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">file.exists</span>(land_socal_geo)){</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a> ply_land <span class="ot"><-</span> <span class="fu">ne_download</span>(</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a> <span class="at">scale =</span> <span class="dv">10</span>, <span class="co"># 110/50/10: high spatial resolution (10 m)</span></span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a> <span class="at">type =</span> <span class="st">"land"</span>, </span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> <span class="at">category =</span> <span class="st">"physical"</span>,</span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a> <span class="at">returnclass =</span> <span class="st">"sf"</span>)</span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a> <span class="co"># plot(ply_land)</span></span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a> ply_land_bb <span class="ot"><-</span> ply_land <span class="sc">|></span> </span>
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">st_intersection</span>(ply_bb)</span>
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a> <span class="co"># plot(ply_land_bb)</span></span>
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">write_sf</span>(ply_land_bb, land_socal_geo)</span>
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a>ply_land_bb <span class="ot"><-</span> <span class="fu">read_sf</span>(land_socal_geo)</span>
<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a><span class="co"># plot(ply_land_bb)</span></span>
<span id="cb7-23"><a href="#cb7-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-24"><a href="#cb7-24" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">file.exists</span>(gebco_socal_tif)){</span>
<span id="cb7-25"><a href="#cb7-25" aria-hidden="true" tabindex="-1"></a> <span class="co"># read large GEBCO netcdf file outside repo</span></span>
<span id="cb7-26"><a href="#cb7-26" aria-hidden="true" tabindex="-1"></a> r_gebco <span class="ot"><-</span> <span class="fu">raster</span>(gebco_nc)</span>
<span id="cb7-27"><a href="#cb7-27" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb7-28"><a href="#cb7-28" aria-hidden="true" tabindex="-1"></a> <span class="co"># crop to SoCal bounding box</span></span>
<span id="cb7-29"><a href="#cb7-29" aria-hidden="true" tabindex="-1"></a> r_gebco_bb <span class="ot"><-</span> r_gebco <span class="sc">|></span> </span>
<span id="cb7-30"><a href="#cb7-30" aria-hidden="true" tabindex="-1"></a> <span class="fu">crop</span>(ply_bb)</span>
<span id="cb7-31"><a href="#cb7-31" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb7-32"><a href="#cb7-32" aria-hidden="true" tabindex="-1"></a> <span class="co"># mask out land, ie > 0 </span></span>
<span id="cb7-33"><a href="#cb7-33" aria-hidden="true" tabindex="-1"></a> r_gebco_bb <span class="ot"><-</span> r_gebco_bb <span class="sc">|></span> </span>
<span id="cb7-34"><a href="#cb7-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">mask</span>(r_gebco_bb <span class="sc"><=</span> <span class="dv">0</span>, <span class="at">maskvalue =</span> <span class="dv">0</span>) <span class="sc">*</span> <span class="sc">-</span><span class="dv">1</span></span>
<span id="cb7-35"><a href="#cb7-35" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb7-36"><a href="#cb7-36" aria-hidden="true" tabindex="-1"></a> <span class="co"># write to TIF</span></span>
<span id="cb7-37"><a href="#cb7-37" aria-hidden="true" tabindex="-1"></a> <span class="fu">writeRaster</span>(r_gebco_bb, gebco_socal_tif, <span class="at">overwrite =</span> T)</span>
<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb7-39"><a href="#cb7-39" aria-hidden="true" tabindex="-1"></a>r_gebco_bb <span class="ot"><-</span> <span class="fu">raster</span>(gebco_socal_tif)</span>
<span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-41"><a href="#cb7-41" aria-hidden="true" tabindex="-1"></a>m <span class="ot"><-</span> <span class="fu">leaflet</span>() <span class="sc">|></span> </span>
<span id="cb7-42"><a href="#cb7-42" aria-hidden="true" tabindex="-1"></a> <span class="fu">add_ocean_basemap</span>() <span class="sc">|></span> </span>
<span id="cb7-43"><a href="#cb7-43" aria-hidden="true" tabindex="-1"></a> <span class="fu">add_am_raster</span>(</span>
<span id="cb7-44"><a href="#cb7-44" aria-hidden="true" tabindex="-1"></a> <span class="at">r =</span> r_gebco_bb, </span>
<span id="cb7-45"><a href="#cb7-45" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"GEBCO depth (m)"</span>, </span>
<span id="cb7-46"><a href="#cb7-46" aria-hidden="true" tabindex="-1"></a> <span class="at">cols =</span> RColorBrewer<span class="sc">::</span><span class="fu">brewer.pal</span>(<span class="dv">7</span>, <span class="st">"Blues"</span>))</span>
<span id="cb7-47"><a href="#cb7-47" aria-hidden="true" tabindex="-1"></a>m </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="fig-depth_socal" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
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",[[34.99999999999999,-121],[31.99999999999998,-117]],0.8,null,null,null]},{"method":"addLegend","args":[{"colors":["#EFF3FF","#BED8EC","#8BBFDD","#549DCC","#2977B8","#084594"],"labels":["0 – 1,000","1,000 – 2,000","2,000 – 3,000","3,000 – 4,000","4,000 – 5,000","5,000 – 6,000"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"topright","type":"bin","title":"GEBCO depth (m)","extra":null,"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[31.99999999999998,34.99999999999999],"lng":[-121,-117]},"fitBounds":[31.99999999999998,-121,34.99999999999999,-117,[]]},"evals":[],"jsHooks":[]}</script>
<figcaption class="figure-caption">Figure 4: Map of depth from GEBCO zoomed into Southern California. Notice the much higher resolution compared to <a href="#fig-blue_whale_map_socal">Figure 2</a>.</figcaption>
</figure>
</div>
</div>
</section>
<section id="ramp-depth-with-species-preference" class="level1" data-number="5">
<h1 data-number="5"><span class="header-section-number">5</span> Ramp depth with species preference</h1>
<section id="create-ramp_env-function" class="level2" data-number="5.1">
<h2 data-number="5.1" class="anchored" data-anchor-id="create-ramp_env-function"><span class="header-section-number">5.1</span> Create <code>ramp_env()</code> function</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>ramp_env <span class="ot"><-</span> <span class="cf">function</span>(v, min, min_pref, max, max_pref){</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> x <span class="ot"><-</span> <span class="fu">c</span>(min, min_pref, max, max_pref)</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> y <span class="ot"><-</span> <span class="fu">c</span>( <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">0</span>)</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">approx</span>(</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> x, y, </span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> <span class="at">xout =</span> v, </span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> <span class="at">yleft =</span> <span class="dv">0</span>,</span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a> <span class="at">yright =</span> <span class="dv">0</span>,</span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a> <span class="at">rule =</span> <span class="dv">2</span>,</span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="st">"linear"</span>)<span class="sc">$</span>y</span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a>p <span class="ot"><-</span> d <span class="sc">|></span> </span>
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(var <span class="sc">==</span> <span class="sc">!!</span>var)</span>
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a>p <span class="ot"><-</span> <span class="fu">setNames</span>(p<span class="sc">$</span>var_value, p<span class="sc">$</span>prob_name) <span class="sc">|></span> <span class="fu">as.list</span>()</span>
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a><span class="co"># p</span></span>
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a><span class="co"># Min PrefMin PrefMax Max </span></span>
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a><span class="co"># 0 1000 4000 8000</span></span>
<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a>x_pref <span class="ot"><-</span> <span class="fu">c</span>(p<span class="sc">$</span>Min, p<span class="sc">$</span>PrefMin, p<span class="sc">$</span>PrefMax, p<span class="sc">$</span>Max)</span>
<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a>y_pref <span class="ot"><-</span> <span class="fu">c</span>( <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">1</span>, <span class="dv">0</span>)</span>
<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a>x_new <span class="ot"><-</span> <span class="fu">seq</span>(<span class="sc">-</span><span class="dv">200</span>, <span class="dv">10000</span>, <span class="at">by=</span><span class="dv">100</span>)</span>
<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a>y_new <span class="ot"><-</span> <span class="fu">ramp_env</span>(x_new, p<span class="sc">$</span>Min, p<span class="sc">$</span>PrefMin, p<span class="sc">$</span>PrefMax, p<span class="sc">$</span>Max)</span>
<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(</span>
<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a> x_pref,</span>
<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a> y_pref, </span>
<span id="cb8-30"><a href="#cb8-30" aria-hidden="true" tabindex="-1"></a> <span class="at">xlim =</span> <span class="fu">range</span>(<span class="fu">c</span>(x_pref, x_new), <span class="at">na.rm=</span>T), </span>
<span id="cb8-31"><a href="#cb8-31" aria-hidden="true" tabindex="-1"></a> <span class="at">ylim =</span> <span class="fu">range</span>(<span class="fu">c</span>(y_pref, y_new), <span class="at">na.rm=</span>T),</span>
<span id="cb8-32"><a href="#cb8-32" aria-hidden="true" tabindex="-1"></a> <span class="at">xlab =</span> var,</span>
<span id="cb8-33"><a href="#cb8-33" aria-hidden="true" tabindex="-1"></a> <span class="at">ylab =</span> sp_term)</span>
<span id="cb8-34"><a href="#cb8-34" aria-hidden="true" tabindex="-1"></a><span class="fu">points</span>(x_new, y_new, <span class="at">col =</span> <span class="dv">2</span>, <span class="at">pch =</span> <span class="st">"*"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-ramp_env" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<p><img src="index_files/figure-html/fig-ramp_env-1.png" class="img-fluid figure-img" width="672"></p>
<figcaption class="figure-caption">Figure 5: Plot of original depth preferences for 4 points (black circles) and interpolated values (red asterisks) using new <code>ramp_env()</code> function.</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="apply-to-socal" class="level2" data-number="5.2">
<h2 data-number="5.2" class="anchored" data-anchor-id="apply-to-socal"><span class="header-section-number">5.2</span> Apply to SoCal</h2>
<p>Apply the <code>ramp_env()</code> function to the SoCal depth using blue whale preferences.</p>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>r_sp_depth_bb <span class="ot"><-</span> terra<span class="sc">::</span><span class="fu">app</span>(</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> terra<span class="sc">::</span><span class="fu">rast</span>(r_gebco_bb), </span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> <span class="at">fun =</span> ramp_env, </span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="at">min =</span> p<span class="sc">$</span>Min, </span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> <span class="at">min_pref =</span> p<span class="sc">$</span>PrefMin, </span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> <span class="at">max_pref =</span> p<span class="sc">$</span>PrefMax,</span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> <span class="at">max =</span> p<span class="sc">$</span>Max) <span class="sc">|></span> </span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">raster</span>()</span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a>m <span class="ot"><-</span> <span class="fu">leaflet</span>() <span class="sc">|></span> </span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">add_ocean_basemap</span>() <span class="sc">|></span> </span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">add_am_raster</span>(</span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a> <span class="at">r =</span> r_sp_depth_bb, </span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="fu">glue</span>(<span class="st">"{sp_term}, {var} only"</span>))</span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a>m </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="fig-map_sp_depth_bb" class="quarto-figure quarto-figure-center anchored">
<figure class="figure">
<div class="leaflet html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-26586c51045fe997417e" style="width:100%;height:464px;"></div>
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",[[34.99999999999999,-121],[31.99999999999998,-117]],0.8,null,null,null]},{"method":"addLegend","args":[{"colors":["#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],"labels":["0.0 – 0.2","0.2 – 0.4","0.4 – 0.6","0.6 – 0.8","0.8 – 1.0"],"na_color":null,"na_label":"NA","opacity":0.5,"position":"topright","type":"bin","title":"blue whale, Depth only","extra":null,"layerId":null,"className":"info legend","group":null}]}],"limits":{"lat":[31.99999999999998,34.99999999999999],"lng":[-121,-117]},"fitBounds":[31.99999999999998,-121,34.99999999999999,-117,[]]},"evals":[],"jsHooks":[]}</script>
<figcaption class="figure-caption">Figure 6: Map of depth preference for <code>r sp_term</code> applied to SoCal depth with the <code>ramp_env()</code> function.</figcaption>
</figure>
</div>
</div>
</section>
</section>
<section id="sdmpredictors" class="level1" data-number="6">
<h1 data-number="6"><span class="header-section-number">6</span> <code>sdmpredictors</code></h1>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>librarian<span class="sc">::</span><span class="fu">shelf</span>(</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a> sdmpredictors, skimr)</span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="co"># exploring the marine datasets </span></span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>datasets <span class="ot"><-</span> <span class="fu">list_datasets</span>(<span class="at">terrestrial =</span> <span class="cn">FALSE</span>, <span class="at">marine =</span> <span class="cn">TRUE</span>)</span>
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a><span class="fu">kable</span>(datasets)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 0%">
<col style="width: 1%">
<col style="width: 1%">
<col style="width: 0%">
<col style="width: 2%">
<col style="width: 72%">
<col style="width: 21%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;"></th>
<th style="text-align: left;">dataset_code</th>
<th style="text-align: left;">terrestrial</th>
<th style="text-align: left;">marine</th>
<th style="text-align: left;">url</th>
<th style="text-align: left;">description</th>
<th style="text-align: left;">citation</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">2</td>
<td style="text-align: left;">Bio-ORACLE</td>
<td style="text-align: left;">FALSE</td>
<td style="text-align: left;">TRUE</td>
<td style="text-align: left;">https://bio-oracle.org/</td>
<td style="text-align: left;">Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms at a spatial resolution 5 arcmin (9.2 km) in the ESRI ascii and tif format.</td>
<td style="text-align: left;">Tyberghein L., Verbruggen H., Pauly K., Troupin C., Mineur F. & De Clerck O. Bio-ORACLE: a global environmental dataset for marine species distribution modeling. Global Ecology and Biogeography. doi: 10.1111/j.1466-8238.2011.00656.x</td>
</tr>
<tr class="even">
<td style="text-align: left;">3</td>
<td style="text-align: left;">MARSPEC</td>
<td style="text-align: left;">FALSE</td>
<td style="text-align: left;">TRUE</td>
<td style="text-align: left;">http://marspec.org/</td>
<td style="text-align: left;">MARSPEC is a set of high resolution climatic and geophysical GIS data layers for the world ocean. Seven geophysical variables were derived from the SRTM30_PLUS high resolution bathymetry dataset. These layers characterize the horizontal orientation (aspect), slope, and curvature of the seafloor and the distance from shore. Ten “bioclimatic” variables were derived from NOAA’s World Ocean Atlas and NASA’s MODIS satellite imagery and characterize the inter-annual means, extremes, and variances in sea surface temperature and salinity. These variables will be useful to those interested in the spatial ecology of marine shallow-water and surface-associated pelagic organisms across the globe. Note that, in contrary to the original MARSPEC, all layers have unscaled values.</td>
<td style="text-align: left;">Sbrocco, EJ and Barber, PH (2013) MARSPEC: Ocean climate layers for marine spatial ecology. Ecology 94: 979. doi: 10.1890/12-1358.1</td>
</tr>
</tbody>
</table>
</div>
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># exploring the marine layers </span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>layers <span class="ot"><-</span> <span class="fu">list_layers</span>(datasets)</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="co"># names(layers)</span></span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a><span class="co"># skim(layers)</span></span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a><span class="co"># table(layers$dataset_code)</span></span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Bio-ORACLE MARSPEC </span></span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a><span class="co"># 918 42 </span></span>