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NCRMP_benthics_vignette.html
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NCRMP_benthics_vignette.html
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<meta name="author" content="Sarah Groves, Bethany Williams, Shay Viehman" />
<meta name="date" content="2024-03-04" />
<title>NCRMP Benthics statistical package in R</title>
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<h1 class="title toc-ignore">NCRMP Benthics statistical package in R</h1>
<h4 class="author">Sarah Groves, Bethany Williams, Shay Viehman</h4>
<h4 class="date">2024-03-04</h4>
<div id="TOC">
<ul>
<li><a href="#description">Description</a>
<ul>
<li><a href="#installation">Installation</a></li>
</ul></li>
<li><a href="#to-use-this-package-as-a-data-repository">To use this package as a data repository</a>
<ul>
<li><a href="#datasets">Datasets</a></li>
</ul></li>
<li><a href="#to-use-the-functions-in-this-package">To use the functions in this package</a></li>
<li><a href="#functions-for-specific-regions">Functions for specific regions</a>
<ul>
<li><a href="#benthic-cover">Benthic cover</a></li>
<li><a href="#coral-density">Coral density</a></li>
<li><a href="#species-richness-and-diversity">Species richness and diversity</a></li>
<li><a href="#mortality">Mortality</a></li>
<li><a href="#invertebrate-density">Invertebrate density</a></li>
<li><a href="#diseasebleaching-prevalence">Disease/bleaching prevalence</a></li>
</ul></li>
<li><a href="#functions-for-all-regions-and-years">Functions for all regions and years</a>
<ul>
<li><a href="#esa-listed-corals---presence-absence">ESA listed corals - presence absence</a></li>
<li><a href="#bleaching-abundance">Bleaching abundance</a></li>
</ul></li>
<li><a href="#frequently-asked-questions">Frequently asked questions:</a></li>
<li><a href="#ncrmp-species-filter-developed-in-status-report-meetings">NCRMP species filter developed in status report meetings</a></li>
<li><a href="#disclaimer">Disclaimer</a></li>
</ul>
</div>
<div id="description" class="section level1">
<h1>Description</h1>
<p>The ncrmp.benthic.analysis package is designed to use benthic coral reef data collected from the Atlantic regions of NOAA’s National Coral Reef Monitoring Program (NCRMP) to complete the following:</p>
<ol style="list-style-type: decimal">
<li><p>Compute summary statistics (e.g. coral density, percent cover, species richness, old and recent mortality, disease prevalence, <em>Diadema</em> density) from the site to the regional level as well as presence/absence of ESA species and bleaching abundance at the strata and site levels, respectively.</p></li>
<li><p>House all current NCRMP benthic data ranging from analysis ready data to regional domain estimates in a standardized format. This includes all metrics listed in item #1 calculated at the site, strata and regional level (as applicable) as well as the most recent sampling grids and NTOT files. There are over 250 dataframes stored in this package.</p></li>
</ol>
<p>Note:</p>
<ul>
<li><p>This package assumes some familiarity with NCRMP sampling methods and analysis ready (AR) data formats. NCEI and CoRIS archived AR data, data dictionaries and sampling protocols can be found on the NCRMP project page, <a href="https://coastalscience.noaa.gov/project/national-coral-reef-monitoring-program-biological-socioeconomic/" class="uri">https://coastalscience.noaa.gov/project/national-coral-reef-monitoring-program-biological-socioeconomic/</a> under “Products, Datasets and Reports”</p></li>
<li><p>This package currently uses NOAA’s NCRMP Atlantic benthic data only. See <a href="https://github.com/jeremiaheb/rvc#reef-visual-census-statistical-package-in-r" class="uri">https://github.com/jeremiaheb/rvc#reef-visual-census-statistical-package-in-r</a> for Atlantic fish R package.</p></li>
<li><p>Where indicated in file names by NCRMP_FRRP or NCRMP_DRM, NCRMP and Disturbrance Response Monitoring (DRM; formerly known as Florida Reef Resilience Program or FRRP) data from 2014 to present have been combined during co-sampling (even) years. DRM only data are used for non-NCRMP (odd) years. We are in the process of changing the naming convention from FRRP to DRM but at present DRM and FRRP are used interchangeably. Please be patient while we complete this transition.</p></li>
<li><p>SEFCRI 2014 coral demographics, benthic cover and invertebrates/ESA corals data, FLK 2014 benthic cover and invertebrates/ESA corals data, and Tortugas 2018 coral demographic data are 2 stage (2 transects per site). Site level metrics for these regions and years are the mean of transect 1 and transect 2. Strata and regional means are then calculated from these site level data.</p></li>
</ul>
<p>For questions, please contact the repository owner, Shay Viehman, <a href="mailto:shay.viehman@noaa.gov" class="email">shay.viehman@noaa.gov</a></p>
<div id="ncrmp-regions-and-sampling-years" class="section level3">
<h3>NCRMP Regions and sampling years</h3>
<ul>
<li>Florida:
<ul>
<li>Southeast Florida (SEFCRI; 2014, 2016, 2018, 2020, 2022)</li>
<li>Florida Keys (FLK; 2014, 2016, 2018, 2020*, 2022)</li>
<li>Dry Tortugas (Tortugas; 2014, 2016, 2018, 2020, 2022)</li>
</ul></li>
<li>Caribbean:
<ul>
<li>St. Thomas & St. John (STTSTJ; 2013, 2015, 2017, 2019, 2021)</li>
<li>St. Croix (STX; 2015, 2017, 2019, 2021)</li>
<li>Puerto Rico (PRICO; 2014, 2016, 2019, 2021)</li>
</ul></li>
<li>Gulf of Mexico:
<ul>
<li>Flower Garden Banks National Marine Sanctuary (GOM; 2013, 2015, 2018, 2022)</li>
</ul></li>
</ul>
<p>Upcoming data additions: Caribbean 2023 (in 2024)</p>
</div>
<div id="installation" class="section level2">
<h2>Installation</h2>
<ol style="list-style-type: decimal">
<li><p>Download the zipped R package using the green “Clone or download” button. Unzip the file.</p></li>
<li><p>In R studio, open the ncrmp.benthics.analysis R project in the folder of the same name and use the following code to install the pacakge. You must be in the in the ncrmp.benthics.analysis R project. If you aleady have devtools installed you can skip the first line.</p></li>
</ol>
<pre><code>install.packages('devtools')
devtools::install()</code></pre>
<ol start="3" style="list-style-type: decimal">
<li><p>Exit the ncrmp.benthics.analysis R project and check your R library to confirm the package installed. At this point you can either delete the files from the zipped folder or retain them if you are interested in the analysis code.</p></li>
<li><p>From your current project, use the following code to load and use the package.</p></li>
</ol>
<pre><code>library(ncrmp.benthics.analysis) </code></pre>
<ol start="5" style="list-style-type: decimal">
<li>To update the package (when new data is added), repeat steps 1-4. If you are having trouble, try deleting the old version first before intalling the new one.</li>
</ol>
<p>Note: Requires R version 3.4.1 or higher.</p>
<p>Currently, you cannot load the package directly from Git Hub using devtools::install_github(‘shgroves/NCRMP_benthics’). Future updates will inlcude this feature.</p>
<hr />
</div>
</div>
<div id="to-use-this-package-as-a-data-repository" class="section level1">
<h1>To use this package as a data repository</h1>
<p>To use this package as a data repository requires very little experience with R! You will need to have R version 3.4.1 or higher installed on your computer as well as R Studio desktop. Please see <a href="https://cran.r-project.org/doc/manuals/r-release/R-admin.html" class="uri">https://cran.r-project.org/doc/manuals/r-release/R-admin.html</a> and <a href="https://www.rstudio.com/products/rstudio/download/" class="uri">https://www.rstudio.com/products/rstudio/download/</a> for more information.</p>
<p>Because all datasets are stored in the R package, all that is required to access these dataframes is to load the package, and type the name of the dataset. You can then begin using it in your code or you can assign it to an object if you want to store it in your Global Environment.</p>
<p><strong>Examples:</strong></p>
<p><strong>Export a dataframe</strong></p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># require(ncrmp.benthics.analysis)</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co"># write.csv(NCRMP_DRM_FLK_2014_18_density_site, "Your file path\\NCRMP_DRM_FLK_14_16_density_site.csv", row.names = F)</span></span></code></pre></div>
<p><strong>Use a dataframe in a ggplot</strong></p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="co">#> Loading required package: ncrmp.benthics.analysis</span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ggplot2)</span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Load weighted strata means</span></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>Hardcoral <span class="ot"><-</span> NCRMP_STTSTJ_2013_21_cover_region <span class="sc">%>%</span></span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> dplyr<span class="sc">::</span><span class="fu">ungroup</span>() <span class="sc">%>%</span></span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> <span class="co"># filter to just hardcoral</span></span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> dplyr<span class="sc">::</span><span class="fu">filter</span>(cover_group <span class="sc">==</span> <span class="st">"HARD CORALS"</span>) <span class="sc">%>%</span></span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a> <span class="co"># Change Year to factor so R sees it as a grouping variable and not a number</span></span>
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a> dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">YEAR =</span> <span class="fu">as.factor</span>(YEAR))</span>
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a><span class="co"># Create your plot</span></span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(Hardcoral, <span class="fu">aes</span>(<span class="at">x =</span> YEAR, <span class="at">y =</span> avCvr, <span class="at">group =</span> YEAR)) <span class="sc">+</span></span>
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="fu">aes</span>(<span class="at">fill =</span> YEAR), <span class="at">stat=</span><span class="st">"identity"</span>, <span class="at">colour=</span><span class="st">"black"</span>,</span>
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a> <span class="at">position=</span><span class="fu">position_dodge</span>(),</span>
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a> <span class="at">size=</span>.<span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a><span class="co"># Add error bars</span></span>
<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymax =</span> avCvr<span class="sc">+</span>SE, <span class="at">ymin =</span> avCvr<span class="sc">-</span>SE), <span class="at">width =</span> .<span class="dv">5</span>, <span class="at">position=</span><span class="fu">position_dodge</span>(<span class="dv">1</span>)) <span class="sc">+</span></span>
<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a><span class="co"># Set the y axis scale</span></span>
<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_y_continuous</span>(<span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">25</span>), <span class="at">expand =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">0</span>)) <span class="sc">+</span></span>
<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a><span class="co"># Add axis labels</span></span>
<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a> <span class="fu">xlab</span>(<span class="st">"Year"</span>) <span class="sc">+</span></span>
<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a> <span class="fu">ylab</span>(<span class="st">"Cover (%)"</span>) <span class="sc">+</span></span>
<span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"Hard coral cover"</span>) <span class="sc">+</span> </span>
<span id="cb4-26"><a href="#cb4-26" aria-hidden="true" tabindex="-1"></a><span class="co"># Remove legend </span></span>
<span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">legend.position=</span><span class="st">"none"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p><strong>Add dataframe to the Global Environment</strong></p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># require(ncrmp.benthics.analysis)</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="co">#</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co"># #The 'data' function will load a dataframe to the Global Environment keeping the existing name, </span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="co"># data("NCRMP_DRM_FLK_2014_22_density_site")</span></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="co">#</span></span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a><span class="co"># or you can give it a new name </span></span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co">#</span></span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a><span class="co"># dat <- NCRMP_DRM_FLK_2014_22_density_site</span></span></code></pre></div>
<div id="datasets" class="section level2">
<h2>Datasets</h2>
<p>Datasets contained within the package at the strata and regional level are created using the default function parameters:</p>
<ul>
<li>For Florida regions:
<ul>
<li>project = “NCRMP” (NCRMP only) or “NCRMP_DRM” (NCRMP and DRM combined)</li>
<li>species filter = FALSE (filter not applied)</li>
</ul></li>
<li>For all Carib regions:
<ul>
<li>project = “NCRMP”</li>
<li>species filter = FALSE</li>
</ul></li>
<li>For GOM:
<ul>
<li>project = “NCRMP”</li>
<li>species filter = FALSE</li>
</ul></li>
</ul>
<div id="analysis-ready-ar-data" class="section level3">
<h3>Analysis Ready (AR) data</h3>
<p>Analysis ready NCRMP datasets for all regions and years are contained within this package in the most recent NCRMP Atlantic archive format. AR datasets can be identified because they begin with the region code followed by the year and then the data type; benthic_cover, coral_demographics or inverts_ESAcorals. Two stage (2 transect) data are identified in the file name except for the Tortugas 2018 data. Tortugas 2018 coral demographics surveys were a joint mission between NCRMP and DRM where two transects were conducted at each site. The NCRMP and DRM data are combined and indistinguishable. All other datasets are single transect.</p>
<ul>
<li>Data format:
<ul>
<li>REGION_YEAR_datatype</li>
</ul></li>
<li>Examples:
<ul>
<li>FLK_2016_coral_demographics,</li>
<li>SEFCRI_2014_2stage_benthic_cover<br />
</li>
<li>FGBNMS_2013_inverts_ESAcorals</li>
</ul></li>
</ul>
<p>Additionally, there are two datasets that combine all regions and years into a single dataframe. * NCRMP_benthic_cover_All_regions_years * NCRMP_demo_All_regions_years</p>
<p>In addition, the following AR Florida DRM and Sanctuary Coral Reef Ecosystem Assessment Monitoring (SCREAM) datasets in NCRMP format are stored in the package. Combined DRM and SCREAM data are from 1999 to 2013 (not all regions are sampled each year) and individual DRM regions are 2014 to the most recent year of sampling.</p>
<ul>
<li>preNCRMP_SCREAM_DRM_coral_demographics<br />
</li>
<li>DRM_SEFCRI_2014_2022_2stage_coral_demographics - includes data from 2014-2022</li>
<li>DRM_FLK_2014_2022_2stage_coral_demographics - includes data from 2014-2022</li>
<li>DRM_Tort_2014_2022_2stage_coral_demographics - includes data from 2014-2022</li>
</ul>
</div>
<div id="summary-dataframes" class="section level3">
<h3>Summary dataframes</h3>
<p>Summary dataframes are provided for site, strata and weighted regional means. Every function listed below can produce several dataframes. Although there are too many to list individually, all provided dataframes follow a standard naming convention. Summary dataframes can be identified because they begin with the project or projects (NCRMP or NCRMP_DRM), followed by the region, year(s) and data type and level (site, strata, region). See associated functions used to create these dataframes (below) for more information. To view all of the data contained within this package you can 1. download the package from GitHub and view the ‘data’ folder or 2. use the following code to view all of the datasets in R.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="co"># data(package='ncrmp.benthics.analysis')</span></span></code></pre></div>
<ul>
<li>Data format:
<ul>
<li>PROJECT_REGION_YEAR_datatype_level</li>
</ul></li>
<li>Examples:
<ul>
<li>NCRMP_DRM_FLK_2014_22_dis_prev_site,</li>
<li>NCRMP_FGBNMS_2013_22_density_strata,<br />
</li>
<li>NCRMP_FLK_2014_22_rec_mort_region,</li>
<li>NCRMP_PRICO_2014_21_richness_strata,</li>
<li>NCRMP_STX_2015_21_cover_region</li>
</ul></li>
</ul>
<p>In addition, there are three dataframes that contain all regions and years</p>
<ul>
<li>NCRMP_AllRegions_Years_ESA_PresAbs_Site</li>
<li>NCRMP_AllRegions_Years_ESA_PresAbs_Strat</li>
<li>NCRMP_bleaching_abundance_Allyears_Allregions</li>
</ul>
</div>
<div id="shapefiles-and-ntot-files" class="section level3">
<h3>Shapefiles and NTOT files</h3>
<p>The most recent sampling grid shapefiles used for site allocation and all NTOT (grid summary) files are also contained in the package. Grids can be used in R or exported as shapefiles. The NTOT files are required to properly apply the weighting scheme to summary data and often differ between years. The Florida Keys National Marine Sanctuary (FKNMS) and the Flower Garden Banks National Marine Sanctuary (FGBNMS) boundary polygons are also contained in the package. More boundary polygons for other regions will be added in future updates.</p>
<ul>
<li>Sampling grids:
<ul>
<li>SEFCRI_2018_sample_frame</li>
<li>FLK_2020_coral_demographics</li>
<li>Tort_2020_sample_frame</li>
<li>STTSTJ_2021_sample_frame_WGS1984</li>
<li>STX_2021_sample_frame_WGS1984</li>
<li>PRICO_2021_sample_frame_WGS1984</li>
<li>FGBNMS_2018_sample_frame_WGS1984</li>
</ul></li>
<li>NTOT files:
<ul>
<li>USVI_2021_NTOT (STTSTJ & STX)</li>
<li>FLK: FLK_2014_NTOT, FLK_2016_NTOT, FLK_2018_NTOT, FLK_2020_NTOT, FLK_2022_NTOT</li>
<li>SEFCRI: SEFL_2014_NTOT, SEFL_2016_NTOT, SEFL_2018_NTOT, SEFL_2020_NTOT, SEFL_2020_NTOT</li>
<li>Tortugas: Tort_2014_NTOT, Tort_2016_NTOT, Tort_2018_NTOT, Tort_2020_NTOT, Tort_2022_NTOT</li>
<li>PRICO_2021_NTOT</li>
<li>FGBNMS_2022_NTOT</li>
</ul></li>
<li>Boundaries
<ul>
<li>FKNMS_boundary</li>
<li>FGBNMS_boundary</li>
</ul></li>
</ul>
</div>
<div id="additional-files" class="section level3">
<h3>Additional files</h3>
<ul>
<li>ncrmp_frrp_sppcodes: species code, species names and benthic categories for NCRMP and other Florida monitoring programs.</li>
</ul>
</div>
</div>
</div>
<div id="to-use-the-functions-in-this-package" class="section level1">
<h1>To use the functions in this package</h1>
<p>If you would like to run the AR data through the functions, apply preset species filters to certain functions or just know more about how NCRMP metrics are calculated, please feel free to use these functions or check out the code. Comments or suggestions on how to improve the package are welcome.</p>
</div>
<div id="functions-for-specific-regions" class="section level1">
<h1>Functions for specific regions</h1>
<div id="these-functions-create-outputs-that-contain-a-single-region-and-either-single-or-multiple-sampling-years.-they-all-use-a-stratified-random-sampling-weighting-scheme-to-calculate-regional-sampling-domain-level-metrics." class="section level3">
<h3>These functions create outputs that contain a single region and either single or multiple sampling years. They all use a stratified random sampling weighting scheme to calculate regional (sampling domain) level metrics.</h3>
</div>
<div id="benthic-cover" class="section level2">
<h2>Benthic cover</h2>
<div id="a.-ncrmp_calculate_cover" class="section level3">
<h3>1a. NCRMP_calculate_cover</h3>
<p><strong>Calculates percent cover from species to regional level for individual years</strong></p>
<ul>
<li>parameters:
<ul>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>project: “NCRMP”, or “MIR” Note - MIR option is in development.</p></li>
</ul></li>
<li>outputs (dataframes):
<ul>
<li><p>cover_group_key: a region specific key for species codes and major benthic categories</p></li>
<li><p>percent_cover_species: site level percent cover by species</p></li>
<li><p>percent_cover_site: site level percent cover by major benthic category</p></li>
<li><p>cover_strata: strata level mean percent cover of major benthic categories (unweighted)</p></li>
<li><p>Domain estimates: weighted regional (domain level) percent cover of major benthic categories</p></li>
<li><p>Domain_check: QAQC check that sums the regional percent cover. Should equal ~100</p></li>
<li><p>cover_check_site: QAQC check that sums the percent cover for each site. Should equal 100</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_LPI_data, load_NCRMP_benthic_cover_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function and assign names to dataframes </span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_calculate_cover</span>(<span class="at">region =</span> <span class="st">"SEFCRI"</span>, <span class="at">project =</span> <span class="st">"NCRMP"</span>)</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>NCRMP_SEFCRI_2014_22_percent_cover_species <span class="ot"><-</span> tmp<span class="sc">$</span>percent_cover_species</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>NCRMP_SEFCRI_2014_22_percent_cover_site <span class="ot"><-</span> tmp<span class="sc">$</span>percent_cover_site</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a>NCRMP_SEFCRI_2014_22_cover_group_key <span class="ot"><-</span> tmp<span class="sc">$</span>cover_group_key</span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>NCRMP_SEFCRI_2014_22_cover_strata <span class="ot"><-</span> tmp<span class="sc">$</span>cover_strata</span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>NCRMP_SEFCRI_2014_22_cover_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est</span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a>NCRMP_SEFCRI_2014_22_Domain_check <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_check </span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a>cover_check_site <span class="ot"><-</span> tmp<span class="sc">$</span>cover_check_site</span></code></pre></div>
</div>
<div id="b.-ncrmp_colony_percent_cover" class="section level3">
<h3>1b. NCRMP_colony_percent_cover</h3>
<p><strong>Calculates species-specific coral cover from at regional level (weighted) for individual years. Also produces a barplot of the most recent years data</strong></p>
<ul>
<li>parameters:
<ul>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>ptitle: a string indicating the title of the plot</p></li>
<li><p>file_path: a string indicating the file path of stored barplot of most recent years’ data</p></li>
</ul></li>
<li>outputs (dataframes):
<ul>
<li><p>region_means: weighted regional (domain level) percent cover of coral species</p></li>
<li><p>a barplot of weighted regional % cover by coral species for the most recent year’s data, exported to the specified filepath</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_LPI_data, load_NCRMP_benthic_cover_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function and assign names to dataframes </span></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span><span class="fu">NCRMP_colony_percent_cover</span>(<span class="at">region =</span> <span class="st">"Tortugas"</span>,</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="at">ptitle =</span> <span class="st">"The Dry Tortugas"</span>,</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="at">file_path =</span> <span class="st">"K:/_BioGeoProjects/NCRMP/Data Analysis/Analysis ready data/Figures"</span> )</span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_percent_cover_region_species <span class="ot"><-</span> tmp<span class="sc">$</span>region_means</span></code></pre></div>
</div>
<div id="ncrmp_make_weighted_lpi_data" class="section level3">
<h3>2. NCRMP_make_weighted_LPI_data</h3>
<p><strong>Applies weighting scheme to benthic cover data</strong></p>
<p>Analysis level strata varies between regions. In the Caribbean (USVI, PRICO), strata is represented by HABITAT CODE + DEPTH STRAT. In GOM, only one strata is used: FGBNMS, so essentially there is no weighting there. In FLorida, strata is a combination of STRAT + PROT. However, there is no PROT in SEFCRI. In FLK, PROT is always reset to 0 because the benthic sites are not allocated at the PROT level. In Tortugas, PROT is maintained as is. These are the recommended and default settings.</p>
<p>This function is called within NCRMP_calculate_cover so the initial parameters you provide for region and year. will be passed on.</p>
<p>Example of weighting application for Florida:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="co">#Calculate average cover (avcvr), stratum variance (svar), n and std at the strata and regional level using STRAT + PROT as the analysis strata. Inputdata = 1 stage site level cover for major benthic categories and ntot = the most recent NTOT file for the region, regardless of sampling year. </span></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co"># cover_est <- inputdata %>%</span></span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a><span class="co"># # group by analysis level strata</span></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(ANALYSIS_STRATUM = paste(STRAT, "/ PROT =", PROT, sep = " ")) %>%</span></span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(ANALYSIS_STRATUM, STRAT, PROT, cover_group) %>% </span></span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(</span></span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate mean cover</span></span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a><span class="co"># avcvr = mean(Percent_Cvr),</span></span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate stratum variance</span></span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a><span class="co"># svar = var(Percent_Cvr),</span></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate N</span></span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a><span class="co"># n = sum(n),</span></span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate mean stratum depth</span></span>
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a><span class="co"># MIN_DEPTH = mean(MIN_DEPTH),</span></span>
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a><span class="co"># MAX_DEPTH = mean(MAX_DEPTH),</span></span>
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a><span class="co"># DEPTH_M = (MIN_DEPTH+MAX_DEPTH)/2) %>%</span></span>
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a><span class="co"># # convert 0 for stratum variance so that the sqrt is a small # but not a 0</span></span>
<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(svar = dplyr::case_when(svar == 0 ~ 0.00000001,</span></span>
<span id="cb9-22"><a href="#cb9-22" aria-hidden="true" tabindex="-1"></a><span class="co"># TRUE ~ svar)) %>%</span></span>
<span id="cb9-23"><a href="#cb9-23" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(std = sqrt(svar))</span></span>
<span id="cb9-24"><a href="#cb9-24" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb9-25"><a href="#cb9-25" aria-hidden="true" tabindex="-1"></a><span class="co"># cover_est <- cover_est %>%</span></span>
<span id="cb9-26"><a href="#cb9-26" aria-hidden="true" tabindex="-1"></a><span class="co"># # Merge ntot with cover_est</span></span>
<span id="cb9-27"><a href="#cb9-27" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::full_join(., ntot) %>%</span></span>
<span id="cb9-28"><a href="#cb9-28" aria-hidden="true" tabindex="-1"></a><span class="co"># # stratum estimates</span></span>
<span id="cb9-29"><a href="#cb9-29" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(whavcvr = wh * avcvr,</span></span>
<span id="cb9-30"><a href="#cb9-30" aria-hidden="true" tabindex="-1"></a><span class="co"># whsvar = wh^2 * svar,</span></span>
<span id="cb9-31"><a href="#cb9-31" aria-hidden="true" tabindex="-1"></a><span class="co"># whstd = wh * std,</span></span>
<span id="cb9-32"><a href="#cb9-32" aria-hidden="true" tabindex="-1"></a><span class="co"># n = tidyr::replace_na(n, 0))</span></span>
<span id="cb9-33"><a href="#cb9-33" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb9-34"><a href="#cb9-34" aria-hidden="true" tabindex="-1"></a><span class="co"># # Reformat data</span></span>
<span id="cb9-35"><a href="#cb9-35" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb9-36"><a href="#cb9-36" aria-hidden="true" tabindex="-1"></a><span class="co"># # cover, unweighted by strata</span></span>
<span id="cb9-37"><a href="#cb9-37" aria-hidden="true" tabindex="-1"></a><span class="co"># cover_strata <- cover_est %>%</span></span>
<span id="cb9-38"><a href="#cb9-38" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::select(REGION, YEAR, ANALYSIS_STRATUM, STRAT, RUG_CD, PROT, DEPTH_M, NTOT, ngrtot, wh, cover_group, n, avcvr, svar, std) %>%</span></span>
<span id="cb9-39"><a href="#cb9-39" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(METHOD = "UNWEIGHTED") %>%</span></span>
<span id="cb9-40"><a href="#cb9-40" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(n = tidyr::replace_na(n, 0))</span></span>
<span id="cb9-41"><a href="#cb9-41" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb9-42"><a href="#cb9-42" aria-hidden="true" tabindex="-1"></a><span class="co"># # Calculate Domain Estimates</span></span>
<span id="cb9-43"><a href="#cb9-43" aria-hidden="true" tabindex="-1"></a><span class="co"># Domain_est <- cover_est %>%</span></span>
<span id="cb9-44"><a href="#cb9-44" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(REGION, YEAR, cover_group) %>%</span></span>
<span id="cb9-45"><a href="#cb9-45" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(avCvr = sum(whavcvr),</span></span>
<span id="cb9-46"><a href="#cb9-46" aria-hidden="true" tabindex="-1"></a><span class="co"># var = sum(whsvar, na.rm = T),</span></span>
<span id="cb9-47"><a href="#cb9-47" aria-hidden="true" tabindex="-1"></a><span class="co"># std = sqrt(var),</span></span>
<span id="cb9-48"><a href="#cb9-48" aria-hidden="true" tabindex="-1"></a><span class="co"># ngrtot = sum(NTOT) )</span></span></code></pre></div>
<p>Example of weighting application for Caribbean and GOM:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Calculate average cover (avcvr), stratum variance (svar), n and std at the strata and regional level using HABITAT + DEPTH_STRAT as the analysis strata. Inputdata = 1 stage site level cover for major benthic categories and ntot = the most recent NTOT file for the region, regardless of sampling year. </span></span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a><span class="co"># cover_est <- inputdata %>%</span></span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="co"># # group by analysis level strata</span></span>
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(YEAR, ANALYSIS_STRATUM, STRAT, cover_group) %>% </span></span>
<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(</span></span>
<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a><span class="co"># # compute average cover</span></span>
<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a><span class="co"># avcvr = mean(Percent_Cvr),</span></span>
<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a><span class="co"># # compute stratum variance</span></span>
<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a><span class="co"># svar = var(Percent_Cvr),</span></span>
<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate N</span></span>
<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a><span class="co"># n = sum(n),</span></span>
<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate mean stratum depth</span></span>
<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a><span class="co"># MIN_DEPTH = mean(MIN_DEPTH),</span></span>
<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a><span class="co"># MAX_DEPTH = mean(MAX_DEPTH),</span></span>
<span id="cb10-17"><a href="#cb10-17" aria-hidden="true" tabindex="-1"></a><span class="co"># DEPTH_M = (MIN_DEPTH+MAX_DEPTH)/2) %>%</span></span>
<span id="cb10-18"><a href="#cb10-18" aria-hidden="true" tabindex="-1"></a><span class="co"># # convert 0 for stratum variance so that the sqrt is a small # but not a 0</span></span>
<span id="cb10-19"><a href="#cb10-19" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(svar = dplyr::case_when(svar == 0 ~ 0.00000001,</span></span>
<span id="cb10-20"><a href="#cb10-20" aria-hidden="true" tabindex="-1"></a><span class="co"># TRUE ~ svar)) %>%</span></span>
<span id="cb10-21"><a href="#cb10-21" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(std = sqrt(svar))</span></span>
<span id="cb10-22"><a href="#cb10-22" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb10-23"><a href="#cb10-23" aria-hidden="true" tabindex="-1"></a><span class="co"># cover_est <- cover_est %>%</span></span>
<span id="cb10-24"><a href="#cb10-24" aria-hidden="true" tabindex="-1"></a><span class="co"># # Merge ntot with cover_est</span></span>
<span id="cb10-25"><a href="#cb10-25" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::full_join(., ntot) %>%</span></span>
<span id="cb10-26"><a href="#cb10-26" aria-hidden="true" tabindex="-1"></a><span class="co"># # stratum estimates</span></span>
<span id="cb10-27"><a href="#cb10-27" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(whavcvr = wh * avcvr,</span></span>
<span id="cb10-28"><a href="#cb10-28" aria-hidden="true" tabindex="-1"></a><span class="co"># whsvar = wh^2 * svar,</span></span>
<span id="cb10-29"><a href="#cb10-29" aria-hidden="true" tabindex="-1"></a><span class="co"># whstd = wh * std,</span></span>
<span id="cb10-30"><a href="#cb10-30" aria-hidden="true" tabindex="-1"></a><span class="co"># n = tidyr::replace_na(n, 0),</span></span>
<span id="cb10-31"><a href="#cb10-31" aria-hidden="true" tabindex="-1"></a><span class="co"># # Add the following to match FL format</span></span>
<span id="cb10-32"><a href="#cb10-32" aria-hidden="true" tabindex="-1"></a><span class="co"># PROT = NA,</span></span>
<span id="cb10-33"><a href="#cb10-33" aria-hidden="true" tabindex="-1"></a><span class="co"># RUG_CD = NA)</span></span>
<span id="cb10-34"><a href="#cb10-34" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb10-35"><a href="#cb10-35" aria-hidden="true" tabindex="-1"></a><span class="co"># # Reformat data</span></span>
<span id="cb10-36"><a href="#cb10-36" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb10-37"><a href="#cb10-37" aria-hidden="true" tabindex="-1"></a><span class="co"># # cover, unweighted by strata</span></span>
<span id="cb10-38"><a href="#cb10-38" aria-hidden="true" tabindex="-1"></a><span class="co"># cover_strata <- cover_est %>%</span></span>
<span id="cb10-39"><a href="#cb10-39" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::select(REGION, YEAR, ANALYSIS_STRATUM, STRAT, RUG_CD, PROT, DEPTH_M, NTOT, ngrtot, wh, cover_group, n, avcvr, svar, std) %>%</span></span>
<span id="cb10-40"><a href="#cb10-40" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(METHOD = "UNWEIGHTED") %>%</span></span>
<span id="cb10-41"><a href="#cb10-41" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(n = tidyr::replace_na(n, 0))</span></span>
<span id="cb10-42"><a href="#cb10-42" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb10-43"><a href="#cb10-43" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb10-44"><a href="#cb10-44" aria-hidden="true" tabindex="-1"></a><span class="co"># ## Calculate Domain Estimates</span></span>
<span id="cb10-45"><a href="#cb10-45" aria-hidden="true" tabindex="-1"></a><span class="co"># Domain_est <- cover_est %>%</span></span>
<span id="cb10-46"><a href="#cb10-46" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(REGION, YEAR, cover_group) %>%</span></span>
<span id="cb10-47"><a href="#cb10-47" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(avCvr = sum(whavcvr),</span></span>
<span id="cb10-48"><a href="#cb10-48" aria-hidden="true" tabindex="-1"></a><span class="co"># var = sum(whsvar, na.rm = T),</span></span>
<span id="cb10-49"><a href="#cb10-49" aria-hidden="true" tabindex="-1"></a><span class="co"># std = sqrt(var),</span></span>
<span id="cb10-50"><a href="#cb10-50" aria-hidden="true" tabindex="-1"></a><span class="co"># ngrtot = sum(NTOT) )</span></span></code></pre></div>
</div>
</div>
<div id="coral-density" class="section level2">
<h2>Coral density</h2>
<div id="a.-ncrmp_drm_calculate_colony_density" class="section level3">
<h3>3a. NCRMP_DRM_calculate_colony_density</h3>
<p><strong>Calculates colony density from site to regional level for all sampling years</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>project: “NCRMP” (All regions), “NCRMP_DRM” (Florida regions only)</p></li>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>species_filter: A string indicating whether a region-specific preset species filter should be applied to density calculations (species codes listed) or all species should be included (FALSE; the default).</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>density_species: site level coral density by species</p></li>
<li><p>density_site: site level coral density</p></li>
<li><p>density_strata: strata level coral density (unweighted)</p></li>
<li><p>Domain estimates: weighted regional (domain level) coral density</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_demo_data, load_NCRMP_DRM_demo_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</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"># Run function </span></span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_DRM_calculate_colony_density</span>(<span class="at">project =</span> <span class="st">"NCRMP_DRM"</span>, </span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> <span class="at">region =</span> <span class="st">"FLK"</span>,</span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="at">species_filter =</span> <span class="st">"FALSE"</span>)</span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_FLK_2014_22_density_species <span class="ot"><-</span> tmp<span class="sc">$</span>density_species</span>
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_FLK_2014_22_density_site <span class="ot"><-</span> tmp<span class="sc">$</span>density_site</span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_FLK_2014_22_density_strata <span class="ot"><-</span> tmp<span class="sc">$</span>density_strata</span>
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_FLK_2014_22_density_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est</span></code></pre></div>
</div>
<div id="b.-ncrmp_drm_colony_density_cv_and_occurrence" class="section level3">
<h3>3b. NCRMP_DRM_colony_density_CV_and_occurrence</h3>
<p><strong>Calculates colony density and coefficient of variation (CV) by species at the regional level for all sampling years. Also produces a barplot of occurrence and CV by species for the most recent years data</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>project: “NCRMP” (All regions), “NCRMP_DRM” (Florida regions only)</p></li>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>species_filter: A string indicating whether a region-specific preset species filter should be applied to density calculations (species codes listed) or all species should be included (FALSE; the default).</p></li>
<li><p>year: a numeric indicating the year of interest for plotting, usually the most recent year</p></li>
<li><p>ptitle: a string indicating the figure title</p></li>
<li><p>file_path: a string indicating the file path to store the produced figure</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>region_means: weighted regional (domain level) coral density by species</p></li>
<li><p>region_means_cv20: weighted regional (domain level) coral density by species for species with a CV of 20 or less</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_demo_data, load_NCRMP_DRM_demo_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function </span></span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_DRM_colony_density_CV_and_occurrence</span>(<span class="at">region =</span> <span class="st">"Tortugas"</span>,</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> <span class="at">ptitle =</span> <span class="st">"Dry Tortugas"</span>,</span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> <span class="at">year =</span> <span class="dv">2022</span>,</span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> <span class="at">file_path =</span> <span class="st">"K:/_BioGeoProjects/NCRMP/Data Analysis/Analysis ready data/Figures"</span> , <span class="co">#must be connected to VPN if filepath is to a network drive</span></span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a> <span class="at">species_filter =</span> <span class="st">"NULL"</span>,</span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a> <span class="at">project =</span> <span class="st">"NCRMP"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_Occ_Den_CV <span class="ot"><-</span> tmp<span class="sc">$</span>region_means</span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_Occ_Den_CV_20 <span class="ot"><-</span> tmp<span class="sc">$</span>region_means_cv20</span></code></pre></div>
</div>
<div id="ncrmp_make_weighted_demo_data" class="section level3">
<h3>4. NCRMP_make_weighted_demo_data</h3>
<p><strong>Applies weighting scheme to coral demographic data (coral density, species richness, mortality, disease/bleaching prevalence).</strong></p>
<p>Analysis level strata varies between regions. In the Caribbean (USVI, PRICO), strata is represented by HABITAT CODE + DEPTH STRAT. In GOM, only one strata is used: FGBNMS, so essentially there is no weighting there. In FLorida, strata is a combination of STRAT + PROT. However, there is no PROT in SEFCRI. In FLK, PROT is always reset to 0 because the benthic sites are not allocated at the PROT level. In Tortugas, PROT is maintained as is. These are the recommended and default settings.</p>
<p>This function is called within NCRMP_DRM_calculate_colony_density so the initial parameters you provide for region and year will be passed on.</p>
<p>Example of weighting application for Florida:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Calculate average density (avden), stratum variance (svar), n and std at the strata and regional level using STRAT + PROT as the analysis strata. Inputdata = 1 stage site level coral density and ntot = the most recent NTOT file for the region, regardless of sampling year. </span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="co"># density_est <- inputdata %>%</span></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="co"># # group by analysis level strata</span></span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(ANALYSIS_STRATUM = paste(STRAT, "/ PROT =", PROT, sep = " ")) %>%</span></span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(YEAR, ANALYSIS_STRATUM, STRAT, PROT) %>% </span></span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(</span></span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a><span class="co"># # compute average density</span></span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a><span class="co"># avden = mean(DENSITY),</span></span>
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a><span class="co"># # compute stratum variance</span></span>
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a><span class="co"># svar = var(DENSITY),</span></span>
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate N</span></span>
<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a><span class="co"># n = length(DENSITY)) %>%</span></span>
<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a><span class="co"># # convert 0 for stratum variance so that the sqrt is a small # but not a 0</span></span>
<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(svar = dplyr::case_when(svar == 0 ~ 0.00000001,</span></span>
<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a><span class="co"># TRUE ~ svar)) %>%</span></span>
<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(std = sqrt(svar))</span></span>
<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a><span class="co"># density_est <- density_est %>%</span></span>
<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a><span class="co"># # Merge ntot with coral_est_spp</span></span>
<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::full_join(., ntot) %>%</span></span>
<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a><span class="co"># # stratum estimates</span></span>
<span id="cb14-24"><a href="#cb14-24" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(whavden = wh * avden,</span></span>
<span id="cb14-25"><a href="#cb14-25" aria-hidden="true" tabindex="-1"></a><span class="co"># whsvar = wh^2 * svar,</span></span>
<span id="cb14-26"><a href="#cb14-26" aria-hidden="true" tabindex="-1"></a><span class="co"># whstd = wh * std,</span></span>
<span id="cb14-27"><a href="#cb14-27" aria-hidden="true" tabindex="-1"></a><span class="co"># n = tidyr::replace_na(n, 0))</span></span>
<span id="cb14-28"><a href="#cb14-28" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb14-29"><a href="#cb14-29" aria-hidden="true" tabindex="-1"></a><span class="co"># # Reformat output</span></span>
<span id="cb14-30"><a href="#cb14-30" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb14-31"><a href="#cb14-31" aria-hidden="true" tabindex="-1"></a><span class="co"># density_strata <- density_est %>%</span></span>
<span id="cb14-32"><a href="#cb14-32" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::select(REGION, YEAR, ANALYSIS_STRATUM, STRAT, RUG_CD, PROT, NTOT, ngrtot, wh, n, avden, svar, std)</span></span>
<span id="cb14-33"><a href="#cb14-33" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb14-34"><a href="#cb14-34" aria-hidden="true" tabindex="-1"></a><span class="co"># # Calculate Domain Estimates</span></span>
<span id="cb14-35"><a href="#cb14-35" aria-hidden="true" tabindex="-1"></a><span class="co"># Domain_est <- density_est %>%</span></span>
<span id="cb14-36"><a href="#cb14-36" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(REGION, YEAR) %>%</span></span>
<span id="cb14-37"><a href="#cb14-37" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(avDen = sum(whavden),</span></span>
<span id="cb14-38"><a href="#cb14-38" aria-hidden="true" tabindex="-1"></a><span class="co"># var = sum(whsvar, na.rm = T),</span></span>
<span id="cb14-39"><a href="#cb14-39" aria-hidden="true" tabindex="-1"></a><span class="co"># std = sqrt(var),</span></span>
<span id="cb14-40"><a href="#cb14-40" aria-hidden="true" tabindex="-1"></a><span class="co"># ngrtot = sum(NTOT) )</span></span></code></pre></div>
<p>Example of weighting application for Caribbean:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Calculate average density (avden), stratum variance (svar), n and std at the strata and regional level using HABITAT + DEPTH_STRAT as the analysis strata. Inputdata = 1 stage site level coral density and ntot = the most recent NTOT file for the region, regardless of sampling year. </span></span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="co"># # Calculate avdns, svar, n and std</span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="co"># density_est <- inputdata %>%</span></span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a><span class="co"># # group by analysis level strata</span></span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(ANALYSIS_STRATUM = STRAT) %>%</span></span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(YEAR, ANALYSIS_STRATUM, STRAT) %>% </span></span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(# compute average density</span></span>
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a><span class="co"># avden = mean(DENSITY),</span></span>
<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a><span class="co"># # compute stratum variance</span></span>
<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a><span class="co"># svar = var(DENSITY),</span></span>
<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a><span class="co"># # calculate N</span></span>
<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a><span class="co"># n = length(DENSITY)) %>%</span></span>
<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a><span class="co"># # convert 0 for stratum variance so that the sqrt is a small # but not a 0</span></span>
<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(svar = dplyr::case_when(svar == 0 ~ 0.00000001,</span></span>
<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a><span class="co"># TRUE ~ svar)) %>%</span></span>
<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(std = sqrt(svar))</span></span>
<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb15-20"><a href="#cb15-20" aria-hidden="true" tabindex="-1"></a><span class="co"># density_est <- density_est %>%</span></span>
<span id="cb15-21"><a href="#cb15-21" aria-hidden="true" tabindex="-1"></a><span class="co"># # Merge ntot with coral_est_spp</span></span>
<span id="cb15-22"><a href="#cb15-22" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::full_join(., ntot) %>%</span></span>
<span id="cb15-23"><a href="#cb15-23" aria-hidden="true" tabindex="-1"></a><span class="co"># # stratum estimates</span></span>
<span id="cb15-24"><a href="#cb15-24" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::mutate(whavden = wh * avden,</span></span>
<span id="cb15-25"><a href="#cb15-25" aria-hidden="true" tabindex="-1"></a><span class="co"># whsvar = wh^2 * svar,</span></span>
<span id="cb15-26"><a href="#cb15-26" aria-hidden="true" tabindex="-1"></a><span class="co"># whstd = wh * std,</span></span>
<span id="cb15-27"><a href="#cb15-27" aria-hidden="true" tabindex="-1"></a><span class="co"># n = tidyr::replace_na(n, 0),</span></span>
<span id="cb15-28"><a href="#cb15-28" aria-hidden="true" tabindex="-1"></a><span class="co"># # Add the following to match FL format </span></span>
<span id="cb15-29"><a href="#cb15-29" aria-hidden="true" tabindex="-1"></a><span class="co"># PROT = NA, </span></span>
<span id="cb15-30"><a href="#cb15-30" aria-hidden="true" tabindex="-1"></a><span class="co"># RUG_CD = NA)</span></span>
<span id="cb15-31"><a href="#cb15-31" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb15-32"><a href="#cb15-32" aria-hidden="true" tabindex="-1"></a><span class="co"># # Reformat output</span></span>
<span id="cb15-33"><a href="#cb15-33" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb15-34"><a href="#cb15-34" aria-hidden="true" tabindex="-1"></a><span class="co"># density_strata <- density_est %>%</span></span>
<span id="cb15-35"><a href="#cb15-35" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::select(REGION, YEAR, ANALYSIS_STRATUM, STRAT, RUG_CD, PROT, NTOT, ngrtot, wh, n, avden, svar, std)</span></span>
<span id="cb15-36"><a href="#cb15-36" aria-hidden="true" tabindex="-1"></a><span class="co"># </span></span>
<span id="cb15-37"><a href="#cb15-37" aria-hidden="true" tabindex="-1"></a><span class="co"># # Calculate Domain Estimates</span></span>
<span id="cb15-38"><a href="#cb15-38" aria-hidden="true" tabindex="-1"></a><span class="co"># Domain_est <- density_est %>%</span></span>
<span id="cb15-39"><a href="#cb15-39" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::group_by(REGION, YEAR) %>%</span></span>
<span id="cb15-40"><a href="#cb15-40" aria-hidden="true" tabindex="-1"></a><span class="co"># dplyr::summarise(avDen = sum(whavden),</span></span>
<span id="cb15-41"><a href="#cb15-41" aria-hidden="true" tabindex="-1"></a><span class="co"># var = sum(whsvar, na.rm = T),</span></span>
<span id="cb15-42"><a href="#cb15-42" aria-hidden="true" tabindex="-1"></a><span class="co"># std = sqrt(var),</span></span>
<span id="cb15-43"><a href="#cb15-43" aria-hidden="true" tabindex="-1"></a><span class="co"># ngrtot = sum(NTOT) )</span></span></code></pre></div>
</div>
</div>
<div id="species-richness-and-diversity" class="section level2">
<h2>Species richness and diversity</h2>
<div id="ncrmp_drm_calculate_species_richness_diversity" class="section level3">
<h3>5. NCRMP_DRM_calculate_species_richness_diversity</h3>
<p><strong>Calculates species richness from site to regional level for all sampling years. Calculates Simpson, Inverse Simpson and Shannon–Wiener diversity indices from site to regional level for all sampling years using the R package vegan.</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>project: “NCRMP” (All regions), “NCRMP_DRM” (Florida regions only)</p></li>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>species_list: a list of a species present for all sampling years</p></li>
<li><p>richness_site: site level species richness. Includes juveniles. Note there is no accouting for transect length/effort</p></li>
<li><p>richness_strata: strata level mean species richness (unweighted). Includes juveniles. Note there is no accouting for transect length/effort</p></li>
<li><p>Domain estimates: weighted regional (domain level) species richness. Includes juveniles. Note there is no accouting for transect length/effort</p></li>
<li><p>species_diversity_site: site level species diversity using the Simpson, Inverse Simpson and Shannon-Weiner diversity indices. Based only on adults since the abundance of juveniles is not recorded. Note there is no accouting for transect length/effort</p></li>
<li><p>species_diversity_strata: strata level species diversity using the Simpson, Inverse Simpson and Shannon-Weiner diversity indices (unweighted). Based only on adults since the abundance of juveniles is not recorded. Note there is no accouting for transect length/effort</p></li>
<li><p>species_diversity_region: weighted regional (domain level) species diversity using the Simpson, Inverse Simpson and Shannon-Weiner diversity indices. Based only on adults since the abundance of juveniles is not recorded. Note there is no accouting for transect length/effort</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_demo_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function </span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_DRM_calculate_species_richness_diversity</span>(<span class="at">project =</span> <span class="st">"NCRMP"</span>,</span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a> <span class="at">region=</span> <span class="st">"Tortugas"</span>)</span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_spp_list <span class="ot"><-</span> tmp<span class="sc">$</span>species_list </span>
<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_richness_site <span class="ot"><-</span> tmp<span class="sc">$</span>richness_site</span>
<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_richness_strata <span class="ot"><-</span> tmp<span class="sc">$</span>richness_strata</span>
<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_richness_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est</span>
<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_diversity_site <span class="ot"><-</span> tmp<span class="sc">$</span>species_diversity_site</span>
<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_diversity_strata <span class="ot"><-</span> tmp<span class="sc">$</span>diversity_strata</span>
<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a>NCRMP_Tort_2014_22_diversity_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est_div</span></code></pre></div>
<p>Regional species richness and diversity are calculated using the same weighting scheme as coral density. See NCRMP_make_weighted_demo_data and NCRMP_make_weighted_demo_data_RC for example code. Only the default analysis strat settings are available for species diversity. See <a href="https://cran.r-project.org/web/packages/vegan/vegan.pdf" class="uri">https://cran.r-project.org/web/packages/vegan/vegan.pdf</a> for more information on the diversity indices.</p>
</div>
</div>
<div id="mortality" class="section level2">
<h2>Mortality</h2>
<div id="ncrmp_drm_calculate_mortality" class="section level3">
<h3>6. NCRMP_DRM_calculate_mortality</h3>
<p><strong>Calculates old and recent mortality (percent of colony surface) from species to regional level for all sampling years</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>project: “NCRMP” (All regions), “NCRMP_DRM” (Florida regions only)</p></li>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>species_filter: A string indicating whether a region-specific preset species filter should be applied to density calculations (species codes in a list) or all species should be included (FALSE; the default). Generally not used as species-specific estimates are also provided by default.</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>old_mortality_site: site level old mortality (across all corals)</p></li>
<li><p>recent_mortality_site: site level recent mortality (across all corals)</p></li>
<li><p>old_mortality_strata: strata level mean old mortality (unweighted, across all corals)</p></li>
<li><p>rec_mortality_strata: strata level mean recent mortality (unweighted, across all corals)</p></li>
<li><p>old_mortality_species_strata: strata level mean old mortality (unweighted, by species)</p></li>
<li><p>rec_mortality_species_strata: strata level mean recent mortality (unweighted, by species)</p></li>
<li><p>Domain_est_old_mort: weighted regional (domain level) old mortality (across all species)</p></li>
<li><p>Domain_est_rec_mort: weighted regional (domain level) recent mortality (across all species)</p></li>
<li><p>Domain_est_old_mort_species: weighted regional (domain level) old mortality, by species. Not species-specific mortality weighting is based on strata where species is PRESENT</p></li>
<li><p>Domain_est_rec_mort_species: weighted regional (domain level) recent mortality, by species. Not species-specific mortality weighting is based on strata where species is PRESENT</p></li>
<li><p>ntot_check_rec_mort_species: QAQC check to ensure species-specific NTOT’s add up to 1</p></li>
<li><p>ntot_check_old_mort_species: QAQC check to ensure species-specific NTOT’s add up to 1</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_demo_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function</span></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_DRM_calculate_mortality</span>(<span class="at">project =</span> <span class="st">"NCRMP"</span>,</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="at">region =</span> <span class="st">"GOM"</span>,</span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> <span class="at">species_filter =</span> <span class="st">"FALSE"</span>)</span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename</span></span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_old_mort_site <span class="ot"><-</span> tmp<span class="sc">$</span>old_mortality_site</span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_rec_mort_site <span class="ot"><-</span> tmp<span class="sc">$</span>recent_mortality_site</span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_old_mort_strata <span class="ot"><-</span> tmp<span class="sc">$</span>old_mortality_strata</span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_rec_mort_strata <span class="ot"><-</span> tmp<span class="sc">$</span>rec_mortality_strata</span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_old_mort_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est_old_mort</span>
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_rec_mort_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est_rec_mort</span>
<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_old_mort_species_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est_old_mort_species</span>
<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a>NCRMP_FGBNMS_2013_22_rec_mort_species_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est_rec_mort_species</span>
<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a><span class="co"># check ntot weighting for species-specific mortality - should only be values of 1 (sum of weights across strata for each species in each year)</span></span>
<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a><span class="fu">unique</span>(tmp<span class="sc">$</span>ntot_check_rec_mort_species<span class="sc">$</span>wh_sum)</span>
<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a><span class="fu">unique</span>(tmp<span class="sc">$</span>ntot_check_old_mort_species<span class="sc">$</span>wh_sum)</span></code></pre></div>
<p>Regional old and recent mortality are calculated using the same weighting scheme as coral density. See NCRMP_make_weighted_demo_data for example code. Species-specific mortality estimates are done based on strata a species is PRESENT. See load_NTOT_species for code.</p>
</div>
</div>
<div id="invertebrate-density" class="section level2">
<h2>Invertebrate density</h2>
<div id="ncrmp_calculate_invert_density" class="section level3">
<h3>7. NCRMP_calculate_invert_density</h3>
<p><strong>Calculates <em>Diadema antillarum</em> density from site to regional level for all sampling years</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>project: “NCRMP” (default) or “MIR” (in development)</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>diadema_density_site: site level Diadema density</p></li>
<li><p>invert_strata: strata level mean Diadema density (unweighted)</p></li>
<li><p>Domain_est: weighted regional (domain level) Diadema density</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_invert_density</p>
<p>Example:</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function </span></span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_calculate_invert_density</span>(<span class="at">region =</span> <span class="st">"PRICO"</span>, <span class="at">project =</span> <span class="st">"NCRMP"</span>)</span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a>NCRMP_PRICO_2014_21_invert_den_site <span class="ot"><-</span> tmp<span class="sc">$</span>diadema_density_site</span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>NCRMP_PRICO_2014_21_invert_den_strata <span class="ot"><-</span> tmp<span class="sc">$</span>invert_strata <span class="co"># Right now just Diadema</span></span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a>NCRMP_PRICO_2014_21_invert_den_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est</span></code></pre></div>
<p>Regional <em>Diadema</em> density is calculated using the same weighting scheme as coral density. See NCRMP_make_weighted_invert_density for example code. This function is currently only calculating <em>Diadema</em> density. Future updates will include conch and lobster; but until these updates are complete, please see the code in this function for guidance on how to calculate density for the other invertebrates.</p>
</div>
</div>
<div id="diseasebleaching-prevalence" class="section level2">
<h2>Disease/bleaching prevalence</h2>
<div id="a.-ncrmp_drm_calculate_disease_prevalence_colonies" class="section level3">
<h3>8a. NCRMP_DRM_calculate_disease_prevalence_colonies</h3>
<p><strong>Calculates disease and bleaching prevalence at the site (% colonies per site), site and species (% colonies per site per species), strata (mean of % colonies per site), and regional (weighted mean of % colonies per site) level for all sampling years</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>project: “NCRMP_DRM” (Florida regions only), “NCRMP” (All regions)</p></li>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>species_filter: A string indicating whether a region-specific preset species filter should be applied to density calculations (species codes in a list) or all species should be included (FALSE; the default). Note the species filter is not used as much as species-specific domain estimates are also provided.</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>dis_ble_prev_species: site and species level disease and bleaching prevalence</p></li>
<li><p>dis_ble_prev_site: site level disease and bleaching prevalence</p></li>
<li><p>dis_prev_strata: strata level disease prevalence (unweighted)</p></li>
<li><p>ble_prev_strata: strata level bleaching prevalence (unweighted)</p></li>
<li><p>Domain_est: weighted regional (domain level) disease and bleaching prevalence</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_demo_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function </span></span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_DRM_calculate_disease_prevalence_colonies</span>(<span class="at">project =</span> <span class="st">"NCRMP_DRM"</span>,</span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a> <span class="at">region =</span> <span class="st">"SEFCRI"</span>,</span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a> <span class="at">species_filter =</span> <span class="st">"FALSE"</span>)</span>
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_SEFCRI_2014_22_dis_ble_prev_species <span class="ot"><-</span> tmp<span class="sc">$</span>dis_ble_prev_species</span>
<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_SEFCRI_2014_22_dis_ble_prev_site <span class="ot"><-</span> tmp<span class="sc">$</span>dis_ble_prev_site</span>
<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_SEFCRI_2014_22_dis_prev_strata <span class="ot"><-</span> tmp<span class="sc">$</span>dis_prev_strata</span>
<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_SEFCRI_2014_22_ble_prev_strata <span class="ot"><-</span> tmp<span class="sc">$</span>ble_prev_strata</span>
<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a>NCRMP_DRM_SEFCRI_2014_22_dis_ble_prev_region <span class="ot"><-</span> tmp<span class="sc">$</span>Domain_est</span></code></pre></div>
<p>Regional disease prevalence are calculated using the same weighting scheme as coral density. See NCRMP_make_weighted_demo_data for example code.</p>
<p>Note: Disease was not sampled in the Flower Garden Banks National Marine Sanctuary (FGBNMS) in 2013</p>
</div>
<div id="b.-ncrmp_drm_calculate_dis_ble_prevalence_species_domain" class="section level3">
<h3>8b. NCRMP_DRM_calculate_dis_ble_prevalence_species_domain</h3>
<p><strong>Calculates disease and bleaching prevalence by species at the regional (weighted mean of % colonies per site) level for all sampling years. Note weighting is specific to the strata each species is present in, because disease prevalence is NA (not 0) in a strata where a species is absent</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>project: “NCRMP_DRM” (Florida regions only), “NCRMP” (All regions)</p></li>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>species_filter: A string indicating whether a region-specific preset species filter should be applied to density calculations (species codes in a list) or all species should be included (FALSE; the default). Note the species filter is not used as much as species-specific domain estimates are also provided.</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>DomainEst_dis: weighted regional (domain level) disease prevalence by species</p></li>
<li><p>DomainEst_bl: weighted regional (domain level) bleaching prevalence by species</p></li>
<li><p>wh_total: QAQC check to ensure species-specific NTOT’s add up to 1</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_demo_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function </span></span>
<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_DRM_calculate_dis_ble_prevalence_species_domain</span>(<span class="at">project=</span><span class="st">"NCRMP"</span>,</span>
<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a> <span class="at">region =</span> <span class="st">"STX"</span>)</span>
<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a><span class="co"># ntot check - wh_total should always be 1 (sum of weights across strata for each species in each year)</span></span>
<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a><span class="fu">unique</span>(tmp<span class="sc">$</span>ntot_check<span class="sc">$</span>wh_total)</span>
<span id="cb20-10"><a href="#cb20-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-11"><a href="#cb20-11" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb20-12"><a href="#cb20-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb20-13"><a href="#cb20-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb20-14"><a href="#cb20-14" aria-hidden="true" tabindex="-1"></a>NCRMP_STX_2015_21_dis_prev_species_region <span class="ot"><-</span> tmp<span class="sc">$</span>DomainEst_dis</span>
<span id="cb20-15"><a href="#cb20-15" aria-hidden="true" tabindex="-1"></a>NCRMP_STX_2015_21_ble_prev_species_region <span class="ot"><-</span> tmp<span class="sc">$</span>DomainEst_bl</span></code></pre></div>
</div>
<div id="c.-ncrmp_drm_calculate_disease_prevalence_sites" class="section level3">
<h3>8c. NCRMP_DRM_calculate_disease_prevalence_sites</h3>
<p><strong>Calculates number of sites with disease and bleaching at the strata and regional level for all sampling years</strong></p>
<ul>
<li><p>parameters:</p>
<ul>
<li><p>project: “NCRMP_DRM” (Florida regions only), “NCRMP” (All regions)</p></li>
<li><p>region: “SEFCRI”, “FLK”, “Tortugas”, “STTSTJ”, “STX”, “PRICO”, “GOM”</p></li>
<li><p>species_filter: A string indicating whether a region-specific preset species filter should be applied to density calculations (species codes in a list) or all species should be included (FALSE; the default). Note the species filter is not used as much as species-specific domain estimates are also provided.</p></li>
</ul></li>
<li><p>outputs (dataframes):</p>
<ul>
<li><p>dis_ble_prev_site: site level disease and bleaching presence</p></li>
<li><p>dis_ble_prev_strata: number of sites in each strata with disease and bleaching</p></li>
<li><p>disease_prev_region: number of sites across the region with disease and bleaching</p></li>
</ul></li>
</ul>
<p>Associated functions: NCRMP_make_weighted_demo_data</p>
<p>Example:</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a><span class="fu">require</span>(ncrmp.benthics.analysis)</span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run function </span></span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>tmp <span class="ot"><-</span> <span class="fu">NCRMP_DRM_calculate_disease_prevalence_sites</span>(<span class="at">project =</span> <span class="st">"NCRMP"</span>,</span>
<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a> <span class="at">region =</span> <span class="st">"STTSTJ"</span>,</span>
<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a> <span class="at">species_filter =</span> <span class="st">"FALSE"</span>)</span>
<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a><span class="co"># The function will store all of the function outputs as a list in tmp</span></span>
<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a><span class="co"># Convert list objects to dataframes and rename </span></span>
<span id="cb21-12"><a href="#cb21-12" aria-hidden="true" tabindex="-1"></a></span>