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<h1 class="title toc-ignore">Environmental predictors</h1>
</div>
<div id="data-sources" class="section level2">
<h2>Data sources</h2>
<p>To create the topographic predictors, <em>General Bathymetric Chart
of the Oceans</em> (GEBCO, <a
href="https://www.gebco.net/">https://www.gebco.net/</a>) and the
<em>Seafloor Geomorphic Feature Map</em> (<a
href="https://www.bluehabitats.org/?page_id=9">https://www.bluehabitats.org/?page_id=9</a>)
<span class="citation">(<a href="#ref-Harris2014"
role="doc-biblioref">Harris et al. 2014</a>)</span> were used as spatial
data sources. These data were manually downloaded for this study.</p>
<p>The main data source to obtain the dynamic predictors was the
<em>Copernicus Marine Service Information</em> (<a
href="https://resources.marine.copernicus.eu/?option=com_csw&task=results"><https://resources.marine.copernicus.eu/?option=com_csw&task=results</a>).
The data were downloaded automatically through a <a
href="https://github.com/ChrisBermudezR/Cetacean_Tourist_Vessel_Collision_Risk_Assessment/blob/main/03_Predictors/01_NetCDF/01_Predictors_Download.py">script</a>
built in the Python programming language using the <a
href="https://docs.conda.io/en/latest/miniconda.html">MINICONDA</a>
frame and the <a
href="https://help.marine.copernicus.eu/en/articles/4796533-what-are-the-motu-apis#h_3d33beaafc">MOTU</a>
Client API of the <em>Copernicus Marine Service</em>.</p>
</div>
<div id="data-processing" class="section level2">
<h2>Data Processing</h2>
<div id="topographic-predictors" class="section level3">
<h3>Topographic predictors</h3>
<p>The GEBCO bathymetry data were used to obtain a bathymetric layer of
the study area, this was cut to the boundaries of this area with the
“Extract by Mask” tool of the Spatial Analyst extension of the ESRI
ArcGIS® program (v. 10.6; <span class="citation">ESRI (<a
href="#ref-ESRI2017" role="doc-biblioref">2017</a>)</span>). To obtain
the derived slope topographic layer from the GEBCO data, the “Slope”
tool of the Spatial Analyst extension of the ESRI ArcGIS®(v. 10.8.1;
<span class="citation">ESRI (<a href="#ref-ESRI2017"
role="doc-biblioref">2017</a>)</span>) program was used.</p>
<div class="figure" style="text-align: center">
<img src="SuppFig09.png" alt="**Figure S5**. Distribution of the main geomorphological (seamounts, shelf, trencehs and ridges) features of the study area." width="75%" />
<p class="caption">
<strong>Figure S5</strong>. Distribution of the main geomorphological
(seamounts, shelf, trencehs and ridges) features of the study area.
</p>
</div>
<p>To derive the layers based on the distance to the main
geomorphological features, such as ‘Distance to coast’ or ‘Distance to
continental shelf’, the “Euclidean Distance” tool of the Spatial Analyst
extension of ESRI ArcGIS® software (v. 10.8.1; <span
class="citation">ESRI (<a href="#ref-ESRI2017"
role="doc-biblioref">2017</a>)</span>) was used. To derive these layers
accurately and minimize calculation errors, first the original
projection of the geographic coordinate data (WGS84) was transformed to
planar coordinates with the projection Robinson coordinate system using
the “Project” tool of the Data Management Tools extension of ESRI’s
ArcGIS®(v. 10.8.1; <span class="citation">ESRI (<a href="#ref-ESRI2017"
role="doc-biblioref">2017</a>)</span>). The resulting distance layer was
then re-projected to geographic coordinates (WGS84) to align with the
projection of the other data sets (e.g., biodiversity and dynamic
data).</p>
<p><em>Information about the Robinson Projection used with ESRI’s
ArcGIS®</em></p>
<pre><code>World_Robinson
WKID: 54030 Authority: Esri
Projection: Robinson
False_Easting: 0.0
False_Northing: 0.0
Central_Meridian: 0.0
Linear Unit: Meter (1.0)
Geographic Coordinate System: GCS_WGS_1984
Angular Unit: Degree (0.0174532925199433)
Prime Meridian: Greenwich (0.0)
Datum: D_WGS_1984
Spheroid: WGS_1984
Semimajor Axis: 6378137.0
Semiminor Axis: 6356752.314245179
Inverse Flattening: 298.257223563
</code></pre>
<div class="figure" style="text-align: center">
<img src="Topographic_Predictors.png" alt="**Figure S6**. Topographic Enviromental Predictors" width="55%" />
<p class="caption">
<strong>Figure S6</strong>. Topographic Enviromental Predictors
</p>
</div>
</div>
<div id="dynamic-predictors" class="section level3">
<h3>Dynamic predictors</h3>
<p>For the dynamic layers, we selected variables based on the sensory
capabilities of cetacean species in the study area, using <span
class="citation">Torres (<a href="#ref-Torres2017"
role="doc-biblioref">2017</a>)</span>’s work as a guide. We chose
variables that stimulate somatosensory perception and chemoreception,
which can be sensed by dolphins and whales at a distance of more than 10
km.</p>
<p>To create the dynamic predictor layers, we obtained data from the
Copernicus Marine Service Information: <strong>Global Ocean Physics
Reanalysis Glorys 12V1 (PHYS 001-030)</strong> from two datasets:
<strong>cmems_mod_glo_bgc_my_0.083deg-lmtl_PT1D-i and
cmems_mod_glo_phy_my_0.083_P1M-m</strong>. The data had a monthly
temporal resolution from 1993-01-01 to 2020-05-31 and a spatial
resolution of 8.5 km (0.085°). We extracted data not only at the surface
but also at depths up to 200 meters to cover the entire epipelagic layer
where dolphins and whales species have the greatest foraging activity
<span class="citation">(<a href="#ref-Coram2021"
role="doc-biblioref">Coram et al. 2021</a>)</span>.</p>
<p>The selected variables were mass content of epipelagic micronekton
(mnkc_epi [g/m2]), euphotic zone depth (zeu [m]), sea water potential
temperature at sea floor (bottomT [°C]), sea surface height above geoid
(zos [m]), ocean mixed layer thickness defined by sigma theta (mlotst
[m]), sea water potential temperature (thetao [°C]), sea water salinity
(so [10-3]), eastward sea water velocity (uo [m/s]), and northward sea
water velocity (vo [m/s]). We calculated the mean and standard deviation
of all predictors in the study area using a script based on the UNIX
shell with the program Climate Data Operator - CDO through the Windows
Subsystem Linux (WSL version 2) using the Ubuntu 20.04 LTS distro. To
align the raster layers, we developed a script in R using the raster
package (v.3.4-10; <span class="citation">Hijmans et al. (<a
href="#ref-Hijmans2020" role="doc-biblioref">2020</a>)</span>), ensuring
that all grids had the same extent without losing the original
values.</p>
<div class="figure" style="text-align: center">
<img src="Dynamic_Predictors.png" alt="**Figure S7**. Dynamic Enviromental Predictors." width="110%" />
<p class="caption">
<strong>Figure S7</strong>. Dynamic Enviromental Predictors.
</p>
</div>
</div>
</div>
<div id="selection-of-environmental-predictors" class="section level2">
<h2>Selection of Environmental predictors</h2>
<p>As some cetacean species or subspecies considered in this study are
only found in one of the two ocean basins of Colombia, we divided the
predictor layers into two sets, one for each basin. We then conducted a
Pearson correlation test (r) on the environmental predictor layers to
identify and exclude any layers with high covariation. To perform this
test, we used the “layerstats” tool in the R program’s “raster” package
<span class="citation">(<a href="#ref-Hijmans2020"
role="doc-biblioref">Hijmans et al. 2020</a>)</span>, which can be found
in this script (<a
href="https://github.com/ChrisBermudezR/Cetacean_Tourist_Vessel_Collision_Risk_Assessment/blob/main/03_Predictors/01_Predictors_Alignment.R"
class="uri">https://github.com/ChrisBermudezR/Cetacean_Tourist_Vessel_Collision_Risk_Assessment/blob/main/03_Predictors/01_Predictors_Alignment.R</a>).
If two layers were found to have an r value greater than 0.8, we only
used one of them in the model run.</p>
<p>For the Caribbean basin, we detected five predictors out of the 22
considered that had correlations with other predictors with an r greater
than 0.75. We found that bathymetry and distance to the continental
shelf had a very strong negative correlation (<em>r</em> = -0.85), while
the standard deviation of sea water potential temperature had a strong
negative correlation (<em>r</em> = -0.85) with the mean sea water
potential temperature, as well as with the standard deviation
(<em>r</em> = -0.82) and mean (<em>r</em> = -0.92) of sea water
potential temperature at the sea floor. In addition, the mean sea water
potential temperature at the sea floor was found to have a very strong
correlation with the mean sea water potential temperature (<em>r</em> =
0.82) (see Figure S8). After analyzing these correlations, we decided to
only use bathymetry and standard deviation of sea water potential
temperature as final uncorrelated predictors in the model, as they can
be used as proxies for the other correlated variables.</p>
<div class="figure" style="text-align: center">
<img src="corr_Matrix_Caribbean.png" alt="**Figure S8**. Pearson correlation matrix of the 22 environmental predictors used to conduct the habitat suitability models in the Colombian Caribbean basin for *Stenella attenuata attenuata* (Caribbean occurrences), *S. frontalis* (Caribbean occurrences), *S. longirostris longirostris* (Caribbean occurrences) and *Tursiops truncatus* (Caribbean occurrences)." width="110%" />
<p class="caption">
<strong>Figure S8</strong>. Pearson correlation matrix of the 22
environmental predictors used to conduct the habitat suitability models
in the Colombian Caribbean basin for <em>Stenella attenuata
attenuata</em> (Caribbean occurrences), <em>S. frontalis</em> (Caribbean
occurrences), <em>S. longirostris longirostris</em> (Caribbean
occurrences) and <em>Tursiops truncatus</em> (Caribbean occurrences).
</p>
</div>
<p>Of the 22 predictors considered for the Pacific basin, 13 were found
to have correlations with other predictors with an r greater than 0.75
(see Supplementary Figure XXX). The mean sea water potential temperature
at the sea floor exhibited three strong positive correlations with
bathymetry (<em>r</em> = 0.76), the standard deviation of sea water
potential temperature at the sea floor (<em>r</em> = 0.90), and the mean
sea water potential temperature (<em>r</em> = 0.87). Only the standard
deviation of sea water potential temperature (<em>r</em> = -0.85) and
the mean sea water salinity (<em>r</em> = -0.75) presented a strong
negative correlation with this predictor.</p>
<p>The distance to the continental shelf showed a strong negative
correlation with the mean and standard deviation of the mass content of
epipelagic micronekton (<em>r</em> = -0.81 and <em>r</em> = -0.76,
respectively) and a strong positive correlation with the distance to the
trenches (<em>r</em> = 0.76). The mean and standard deviation of ocean
mixed layer thickness exhibited a very strong positive correlation with
each other (<em>r</em> = 0.97), and the mean sea water potential
temperature showed a strong negative correlation (<em>r</em> = -0.86)
with the mean sea water potential temperature (see Figure S9).</p>
<div class="figure" style="text-align: center">
<img src="corr_Matrix_Pacific.png" alt="**Figure S9**. Pearson correlation matrix of the 22 environmental predictors used to conduct the habitat suitability models in the Colombian Caribbean basin for *Megaptera novaeangliae*, *Stenella attenuata attenuata* (Pacific occurrences), *S. attenuata graffmani*, *S. longirostris centroamericana*, *S. longirostris orientalis* and *Tursiops truncatus* (Pacific occurrences)." width="110%" />
<p class="caption">
<strong>Figure S9</strong>. Pearson correlation matrix of the 22
environmental predictors used to conduct the habitat suitability models
in the Colombian Caribbean basin for <em>Megaptera novaeangliae</em>,
<em>Stenella attenuata attenuata</em> (Pacific occurrences), <em>S.
attenuata graffmani</em>, <em>S. longirostris centroamericana</em>,
<em>S. longirostris orientalis</em> and <em>Tursiops truncatus</em>
(Pacific occurrences).
</p>
</div>
<p>Finally, we selected the remaining predictors to perform habitat
suitability modeling based on previous studies, the ecology of each
cetacean species/subspecies, and their natural history (refer to
<strong>Table 1</strong>) <span class="citation">(<a
href="#ref-Redfern2017" role="doc-biblioref">Redfern et al.
2017</a>)</span>.</p>
<p>Moreover, as we hypothesized that marine features and dynamic
oceanographic conditions influence the distribution of marine cetaceans,
we selected environmental predictors that remained within the boundaries
of the meso- and macro-scales of sense of dolphins and baleen whales
(<em>i.e.</em>, distances ranging from 10 km to 1,000 km) <span
class="citation">(<a href="#ref-Torres2017" role="doc-biblioref">Torres
2017</a>)</span>.”</p>
</div>
<div id="references" class="section level1">
<h1>REFERENCES</h1>
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-Coram2021" class="csl-entry">
Coram A, Abreo NAS, Ellis RP, Thompson KF (2021) Contribution of social
media to cetacean research in southeast asia: Illuminating populations
vulnerable to litter. Biodiversity and Conservation 30:2341–2359. <a
href="https://doi.org//10.1007/s10531-021-02196-6">https://doi.org//10.1007/s10531-021-02196-6</a>
</div>
<div id="ref-ESRI2017" class="csl-entry">
ESRI (2017) <span>ArcGIS Desktop: Release 10. Environmental Systems
Research Institute. Redlands, CA.</span>
</div>
<div id="ref-Harris2014" class="csl-entry">
Harris PT, Macmillan-Lawler M, Rupp J, Baker EK (2014) <span
class="nocase">Geomorphology of the oceans</span>. Marine Geology
352:4–24. https://doi.org/<a
href="https://doi.org/10.1016/j.margeo.2014.01.011">https://doi.org/10.1016/j.margeo.2014.01.011</a>
</div>
<div id="ref-Hijmans2020" class="csl-entry">
Hijmans RJ, Van Etten J, Cheng J, et al (2020) <span
class="nocase">raster: Geographic Data Analysis and Modeling.R package
version 3.3</span>
</div>
<div id="ref-Redfern2017" class="csl-entry">
Redfern VJ, Hatch LT, Caldow C, et al (2017) <span
class="nocase">Assessing the risk of chronic shipping noise to baleen
whales off Southern California, USA</span>. Endangered Species Research
32:153–167. <a
href="https://doi.org/10.3354/esr00797">https://doi.org/10.3354/esr00797</a>
</div>
<div id="ref-Torres2017" class="csl-entry">
Torres LG (2017) <span class="nocase">A sense of scale: Foraging
cetaceans’ use of scale-dependent multimodal sensory systems</span>.
Marine Mammal Science 33:1170–1193. <a
href="https://doi.org/10.1111/mms.12426">https://doi.org/10.1111/mms.12426</a>
</div>
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