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<h1 class="title toc-ignore">Biodiversity data</h1>
</div>
<div id="data-sources" class="section level2">
<h2>Data Sources</h2>
<p>To evaluate the potential tourist-vessel collision risk for five
cetacean species regularly observed in Colombia, we conducted habitat
modeling analyses for the following species:</p>
<ul>
<li><p>Atlantic spotted dolphin (<em>S. frontalis</em>) for the
Caribbean Sea only, as it is endemic to the Atlantic Ocean <span
class="citation">(<a href="#ref-Herzing2018"
role="doc-biblioref">Herzing and Perrin 2018</a>)</span>.</p></li>
<li><p>Humpback whale (<em>M. novaeangliae</em>) for the Pacific Ocean
only, as the Colombian Pacific coast serves as part of the breeding zone
for stock G <span class="citation">(<a href="#ref-Acevedo2017"
role="doc-biblioref">Acevedo et al. 2017</a>; <a href="#ref-Avila2020"
role="doc-biblioref">Ávila et al. 2020</a>)</span>.</p></li>
<li><p>Bottlenose dolphin (<em>T. truncatus</em>) for both Caribbean and
Pacific basins.</p></li>
<li><p>Pantropical spotted dolphin (<em>S. attenuata</em>) for both
basins, but occurrences were separated by subspecies distribution: the
offshore pantropical spotted (S. a. attenuata) was analyzed in both
basins, while the coastal pantropical spotted (<em>S. a. graffmani</em>)
was modeled for coastal records in the Pacific basin only, as it is
exclusive to the coastal waters of the eastern tropical Pacific <span
class="citation">(<a href="#ref-Jefferson2015"
role="doc-biblioref">Jefferson et al. 2015</a>; <a
href="#ref-Perrin2018" role="doc-biblioref">Perrin
2018</a>)</span>.</p></li>
<li><p>Spinner dolphin (<em>S. longirostris</em>) for both basins,
separated by subspecies distribution: the Gray’s spinner (<em>S. l.
longirostris</em>) for the Caribbean basin, the Eastern spinner (<em>S.
l. orientalis</em>) for coastal occurrences in the Pacific basin, and
the Central American spinner (S. l. centroamericana) for offshore
occurrences in the Pacific basin <span class="citation">(<a
href="#ref-Forney2012" role="doc-biblioref">Forney et al. 2012</a>; <a
href="#ref-Jefferson2015" role="doc-biblioref">Jefferson et al.
2015</a>)</span>.</p></li>
</ul>
<p>To locate information on the subspecies, a search was conducted using
the complete name of each subspecies. The following databases were used:
Atlas of Living Australia (ALA, <a href="http://www.ala.org.au/"
class="uri">http://www.ala.org.au/</a>), Berkeley Ecoengine (Ecoengine,
<a
href="https://ecoengine.berkeley.edu/">https://ecoengine.berkeley.edu/</a>),
Biodiversity Information Serving Our Nation (BISON, <a
href="https://bison.usgs.gov/">https://bison.usgs.gov/</a>), Global
Biodiversity Information Facility (GBIF,<a
href="https://www.gbif.org">https://www.gbif.org</a> ), Integrated
Digitized Biocollections (iDigBio, <a
href="https://www.idigbio.org/">https://www.idigbio.org/</a>),
iNaturalist (iNat, <a
href="http://www.inaturalist.org/">http://www.inaturalist.org/</a>), the
Ocean Biogeographic Information System (OBIS, <a
href="https://obis.org">https://obis.org</a>), Distributed Databases
with Backbone (VertNet, <a
href="http://vertnet.org/">http://vertnet.org/</a>), <em>Sistema de
Información Ambiental Marina</em> (SIAM, <a
href="http://siam.invemar.org.co">http://siam.invemar.org.co</a>)
operated by Colombia’s Marine and Coastal Research Institute
<em>INVEMAR</em>, and the <em>Red Nacional de Datos Abiertos sobre
Biodiversidad de Colombia</em> (<em>SiB Colombia</em>, <a
href="https://sibcolombia.net/">https://sibcolombia.net/</a>)</p>
<div class="figure" style="text-align: center">
<img src="SuppFig02.png" alt="**Figure S2.** Occurrences downloaded from the biodiversity information databases." width="75%" />
<p class="caption">
<strong>Figure S2.</strong> Occurrences downloaded from the biodiversity
information databases.
</p>
</div>
<p>In addition to the publicly available databases, our study also
utilized unpublished data from oceanographic cruises conducted by the
<em>National Maritime Directorate of Colombia</em> (<em>DIMAR</em>) as
part of the El Niño Southern Oscillation Regional Study
(<em>ERFEN</em>). The data was collected by the <em>Centro de
Investigaciones Oceanográficas e Hidrográficas del Pacífico</em>
(<em>CCCP</em>) and can be found in the supplementary table 1.</p>
<p>The removal of duplicate records and the filtering of the datasets
were performed using a script in the R programming language and packages
such as “<em>tidyverse</em>”(v. 1.2.0; <span class="citation">Wickham
and Wickham (<a href="#ref-Wickham2017"
role="doc-biblioref">2017</a>)</span>), “<em>dplyr</em>”(v. 1.0; <span
class="citation">Wickham et al. (<a href="#ref-Wickham2019"
role="doc-biblioref">2019</a>)</span>), “<em>anytime</em>” (v. 0.3.7;
<span class="citation">Eddelbuettel (<a href="#ref-Eddelbuettel2020"
role="doc-biblioref">2020</a>)</span>), “<em>rgdal</em>” (v. 1.5-12;
<span class="citation">Bivand et al. (<a href="#ref-Bivand2020"
role="doc-biblioref">2020</a>)</span>), “<em>gdata</em>” (v. 2.18.0;
<span class="citation">Warnes et al. (<a href="#ref-Warnes2012"
role="doc-biblioref">2012</a>)</span>), and “<em>devtools</em>” (v.
2.3.1; <span class="citation">Wickham et al. (<a href="#ref-Wickham2020"
role="doc-biblioref">2020</a>)</span>). This script can be accessed at
the following GitHub link: <a
href="https://github.com/ChrisBermudezR/Cetacean_Tourist_Vessel_Collision_Risk_Assessment/blob/main/02_Species_Occurrences/01_Occurrence_Data_Download.R">https://github.com/ChrisBermudezR/Cetacean_Tourist_Vessel_Collision_Risk_Assessment/blob/main/02_Species_Occurrences/01_Occurrence_Data_Download.R</a></p>
<div class="figure" style="text-align: center">
<img src="Figure_02.png" alt="**Figure 2.** Number and locations of cetacean species/subspecies records reported in the Colombian Caribbean and Pacific basins. Occurrences of humpback whale (*Megaptera novaeangliae*); pantropical spotted dolphin’s subspecies: offshore pantropical spotted (*Stenella attenuata attenuata*) and coastal pantropical spotted (*Stenella attenuata graffmani*); Atlantic spotted dolphins (*Stenella frontalis*); spinner dolphin’s subspecies: Central American spinner (*Stenella longirostris centroamericana*), Gray’s spinner (*Stenella longirostris longirostris*), and Eastern spinner (*Stenella longirostris orientalis*); and bottlenose dolphins (*Tursiops truncatus*)." width="100%" />
<p class="caption">
<strong>Figure 2.</strong> Number and locations of cetacean
species/subspecies records reported in the Colombian Caribbean and
Pacific basins. Occurrences of humpback whale (<em>Megaptera
novaeangliae</em>); pantropical spotted dolphin’s subspecies: offshore
pantropical spotted (<em>Stenella attenuata attenuata</em>) and coastal
pantropical spotted (<em>Stenella attenuata graffmani</em>); Atlantic
spotted dolphins (<em>Stenella frontalis</em>); spinner dolphin’s
subspecies: Central American spinner (<em>Stenella longirostris
centroamericana</em>), Gray’s spinner (<em>Stenella longirostris
longirostris</em>), and Eastern spinner (<em>Stenella longirostris
orientalis</em>); and bottlenose dolphins (<em>Tursiops truncatus</em>).
</p>
</div>
</div>
<div id="data-thinning-and-bias-removal." class="section level2">
<h2>Data thinning and bias removal.</h2>
<p>Spatial data thinning and bias removal are crucial steps in
developing accurate species distribution models. Public and online
datasets that provide occurrence data often display strong spatial
biases, which can affect the reliability of these models <span
class="citation">(<a href="#ref-Fourcade2014"
role="doc-biblioref">Fourcade et al. 2014</a>)</span>.</p>
<p>Spatial thinning is a method used to reduce spatial autocorrelation
and clustering in occurrence records. This technique involves removing
some of the occurrence records from the dataset to create a more even
distribution of records across the study area. A random subset of the
records can be selected or a clustering algorithm can be applied to
group records that are too close together. These methods help to
mitigate the impact of spatial biases and improve the accuracy of
species distribution models.</p>
<p>To detect the clustering of occurrence data and identify areas of
highest density, we used kernel density estimation and visualized the
results using the “<strong>ggplot2</strong>” package (v. 3.4.1; <span
class="citation">Wickham (<a href="#ref-Wickham2016"
role="doc-biblioref">2016</a>)</span>) in R (see Figure S3).</p>
<div class="figure" style="text-align: center">
<img src="SuppFig03.png" alt="**Figure S3.** Two-dimensional estimation of the density of cetacean species occurrences using an axis-aligned bivariate normal kernel, evaluated on a square grid, using the "*ggplot2*" (v. 3.4.1; @Wickham2016) R package." width="120%" />
<p class="caption">
<strong>Figure S3.</strong> Two-dimensional estimation of the density of
cetacean species occurrences using an axis-aligned bivariate normal
kernel, evaluated on a square grid, using the “<em>ggplot2</em>” (v.
3.4.1; <span class="citation">Wickham (<a href="#ref-Wickham2016"
role="doc-biblioref">2016</a>)</span>) R package.
</p>
</div>
<p>For all datasets of species occurrence records (Figure S3), a spatial
thinning analysis was performed using the R package “<em>spThin</em>”
<span class="citation">(<a href="#ref-Aiello-Lammens2015"
role="doc-biblioref">Aiello-Lammens et al. 2015</a>)</span>, the
analysis was performed using thinning parameter of 10 km for minimal
separation of the occurrence data and three repetitions of the thinning
procedure on each the dataset.</p>
<p>The table S1 shows the results of the spatial thinning analysis for
the species/subspecies in different basins. The “Occurrences” column
displays the number of occurrence records available for each
species/subspecies in each basin. The “Thinned 01”, “Thinned 02”, and
“Thinned 03” columns represent the number of occurrence records
remaining after spatial thinning was applied in a different repetition.
The “Data Conservation” column shows the percentage of occurrence
records that were retained after thinning. For example, for <em>M.
novaeangliae</em> in the Pacific basin, only 8% of the original
occurrence records were retained after thinning. In contrast, for S. a.
attenuata in the Pacific basin, 67% of the original occurrence records
were retained after thinning.</p>
<p><strong>Table S1</strong>. Number of occurrences of cetacean
species/subspecies obtained in the automatic query and Number of
occurrences reduced through the spatial thinning technique with the R
package “<em>spThin</em>” <span class="citation">(<a
href="#ref-Aiello-Lammens2015" role="doc-biblioref">Aiello-Lammens et
al. 2015</a>)</span>.</p>
<table>
<colgroup>
<col width="25%" />
<col width="9%" />
<col width="13%" />
<col width="13%" />
<col width="12%" />
<col width="12%" />
<col width="13%" />
</colgroup>
<thead>
<tr class="header">
<th align="center"><strong>Specie/Subspecie</strong></th>
<th align="center"><strong>Basin</strong></th>
<th align="center"><strong>Ocurrences</strong></th>
<th align="center"><strong>Thinned 01</strong></th>
<th align="center"><strong>Thinned 02</strong></th>
<th align="center"><strong>Thinned 03</strong></th>
<th align="center"><strong>Data Conservation</strong></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center"><em>M. novaeangliae</em></td>
<td align="center"><strong>Pacific</strong></td>
<td align="center">7129</td>
<td align="center">586</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">8%</td>
</tr>
<tr class="even">
<td align="center"><em>S. a. attenuata</em></td>
<td align="center"><strong>Caribbean</strong></td>
<td align="center">175</td>
<td align="center">107</td>
<td align="center">107</td>
<td align="center">-</td>
<td align="center">61%</td>
</tr>
<tr class="odd">
<td align="center"><em>S. a. attenuata</em></td>
<td align="center"><strong>Pacific</strong></td>
<td align="center">298</td>
<td align="center">201</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">67%</td>
</tr>
<tr class="even">
<td align="center"><em>S. a. graffmani</em></td>
<td align="center"><strong>Pacific</strong></td>
<td align="center">983</td>
<td align="center">215</td>
<td align="center">215</td>
<td align="center">-</td>
<td align="center">22%</td>
</tr>
<tr class="odd">
<td align="center"><em>S. frontalis</em></td>
<td align="center"><strong>Caribbean</strong></td>
<td align="center">189</td>
<td align="center">82</td>
<td align="center">82</td>
<td align="center">-</td>
<td align="center">43%</td>
</tr>
<tr class="even">
<td align="center"><em>S. l. centroamericana</em></td>
<td align="center"><strong>Pacific</strong></td>
<td align="center">125</td>
<td align="center">28</td>
<td align="center">28</td>
<td align="center">28</td>
<td align="center">22%</td>
</tr>
<tr class="odd">
<td align="center"><em>S. l. longirostris</em></td>
<td align="center"><strong>Caribbean</strong></td>
<td align="center">53</td>
<td align="center">35</td>
<td align="center">35</td>
<td align="center">35</td>
<td align="center">66%</td>
</tr>
<tr class="even">
<td align="center"><em>S. l. orientalis</em></td>
<td align="center"><strong>Pacific</strong></td>
<td align="center">94</td>
<td align="center">46</td>
<td align="center">46</td>
<td align="center">46</td>
<td align="center">46%</td>
</tr>
<tr class="odd">
<td align="center"><em>T. truncatus</em></td>
<td align="center"><strong>Caribbean</strong></td>
<td align="center">165</td>
<td align="center">82</td>
<td align="center">82</td>
<td align="center">82</td>
<td align="center">50%</td>
</tr>
<tr class="even">
<td align="center"><em>T. truncatus</em></td>
<td align="center"><strong>Pacific</strong></td>
<td align="center">690</td>
<td align="center">263</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">38%</td>
</tr>
</tbody>
</table>
<p>To detect the effect of spatial thinning, again, kernel density
estimation was used and the results were visualized using the
“<strong>ggplot2</strong>” package (v. 3.4.1; <span
class="citation">Wickham (<a href="#ref-Wickham2016"
role="doc-biblioref">2016</a>)</span>) in R (Figure S4).</p>
<div class="figure" style="text-align: center">
<img src="SuppFig04.png" alt="**Figure S4.** Two-dimensional estimation of the density of cetacean species occurrences after the spatial thinning, using an axis-aligned bivariate normal kernel, evaluated on a square grid, using the "*ggplot2*" (v. 3.4.1; @Wickham2016) R package." width="120%" />
<p class="caption">
<strong>Figure S4.</strong> Two-dimensional estimation of the density of
cetacean species occurrences after the spatial thinning, using an
axis-aligned bivariate normal kernel, evaluated on a square grid, using
the “<em>ggplot2</em>” (v. 3.4.1; <span class="citation">Wickham (<a
href="#ref-Wickham2016" role="doc-biblioref">2016</a>)</span>) R
package.
</p>
</div>
<p>All the analysis in R are available on this link: <a
href="https://github.com/ChrisBermudezR/Cetacean_Tourist_Vessel_Collision_Risk_Assessment/blob/main/02_Species_Occurrences/02_Occ_Bias_Elimination.R">https://github.com/ChrisBermudezR/Cetacean_Tourist_Vessel_Collision_Risk_Assessment/blob/main/02_Species_Occurrences/02_Occ_Bias_Elimination.R</a></p>
</div>
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