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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Remote Sens.</journal-id>
<journal-title>Frontiers in Remote Sensing</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Remote Sens.</abbrev-journal-title>
<issn pub-type="epub">2673-6187</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="publisher-id">1404323</article-id>
<article-id pub-id-type="doi">10.3389/frsen.2024.1404323</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Remote Sensing</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Spatial and temporal patterns of diffuse attenuation coefficient in Gu&#xe1;nica, Puerto Rico: 15 years after development of a watershed management plan</article-title>
<alt-title alt-title-type="left-running-head">Guzm&#xe1;n-Morales et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/frsen.2024.1404323">10.3389/frsen.2024.1404323</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Guzm&#xe1;n-Morales</surname>
<given-names>Alba L.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<contrib contrib-type="author">
<name>
<surname>Armstrong</surname>
<given-names>Roy A.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Cruz-Motta</surname>
<given-names>Juan J.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Hern&#xe1;ndez</surname>
<given-names>William J.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<aff id="aff1">
<sup>1</sup>
<institution>Bio-optical Oceanography Laboratory</institution>, <institution>Department of Marine Sciences</institution>, <institution>University of Puerto Rico</institution>, <addr-line>Mayag&#xfc;ez</addr-line>, <addr-line>PR</addr-line>, <country>United States</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Laboratory of Experimental Ecology</institution>, <institution>Department of Marine Sciences</institution>, <institution>University of Puerto Rico</institution>, <addr-line>Mayag&#xfc;ez</addr-line>, <addr-line>PR</addr-line>, <country>United States</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1703577/overview">Srinivas Kolluru</ext-link>, Florida Atlantic University, United States</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2719014/overview">Vassiliki Metheniti</ext-link>, Foundation for Research and Technology Hellas (FORTH), Greece</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2719765/overview">Jessica S Turner</ext-link>, University of Connecticut, United States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2719856/overview">Premkumar R.</ext-link>, Indian National Centre for Ocean Information Services, India</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2721761/overview">Chandanlal Parida</ext-link>, Curtin University, Australia</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Alba L. Guzm&#xe1;n-Morales, <email>alba.guzman2@upr.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>19</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>5</volume>
<elocation-id>1404323</elocation-id>
<history>
<date date-type="received">
<day>20</day>
<month>03</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>30</day>
<month>05</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Guzm&#xe1;n-Morales, Armstrong, Cruz-Motta and Hern&#xe1;ndez.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Guzm&#xe1;n-Morales, Armstrong, Cruz-Motta and Hern&#xe1;ndez</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Decreased water quality in coastal environments due to land alterations by human activities has caused stressed and degraded coral reefs worldwide. The consequences of decreased water quality are not limited to coral reefs but also affect the quality of people&#x2019;s lives by increasing the incidence of diseases, so areas highly impacted have been prioritized for management. The Gu&#xe1;nica Bay Watershed Management Plan was developed to reduce the non-point sources of pollution that arrive at the bay and to protect adjacent coral reefs, however, 15&#xa0;years have passed since its creation and management actions have not been evaluated. The purpose of this study was to evaluate the effectiveness of the management actions implemented in the Gu&#xe1;nica Bay watershed. Therefore, the objective of this study was to describe temporal trends (2002&#x2013;2008 and 2016&#x2013;2022) of remotely sensed diffuse attenuation coefficient at 490&#xa0;nm (Kd<sub>490</sub>), a water quality indicator, in one managed (Gu&#xe1;nica Bay) and three non-managed (Guayanilla Bay, Descalabrado River, and Guanajibo River) estuaries in Southwestern Puerto Rico. This was achieved by integrating ocean-color satellite imagery from MERIS-Envisat and OLCI-Sentinel-3 sensors that were sampled using a beyond-Before-After-Control-Impact (beyond-BACI) approach. An additional oceanic site was selected to evaluate continuity between sensors estimates. The imagery was processed using SNAP to extract Kd<sub>490</sub> values in the estuaries. The analysis for the beyond-BACI found significant differences between periods (before and after) but the changes were unique to each location within estuary. The lowest Kd<sub>490</sub> values and variability within estuary was observed in Gu&#xe1;nica (range 0.05&#x2013;0.1 m<sup>-1</sup>) and the highest Kd<sub>490</sub> values were observed in Guayanilla (0.35 m<sup>-1</sup>). The southern estuaries showed similar temporal trends, all having a peak in 2018 and a trough in 2020. Kd<sub>490</sub> did not decrease in Gu&#xe1;nica after the implementation of management actions, which can be related to the passage of several hurricanes during the after period. Further analysis should be done as new data is available and after the implementation of the last management actions suggested in the plan.</p>
</abstract>
<kwd-group>
<kwd>Gu&#xe1;nica Bay</kwd>
<kwd>beyond-BACI</kwd>
<kwd>OLCI</kwd>
<kwd>MERIS</kwd>
<kwd>ocean Color</kwd>
<kwd>remote sensing</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Remote Sensing Time Series Analysis</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>Water quality in coastal environments has decreased in many areas around the world due to anthropogenic impacts (<xref ref-type="bibr" rid="B38">Mallin et al., 2000</xref>; <xref ref-type="bibr" rid="B8">Bordalo, 2003</xref>; <xref ref-type="bibr" rid="B1">Ahn et al., 2005</xref>; <xref ref-type="bibr" rid="B17">Dwight et al., 2011</xref>; <xref ref-type="bibr" rid="B69">Wiegner et al., 2021</xref>). Some of these human activities include watershed alteration for agriculture, irrigation practices, domestic and recreational activities, land development, and littering (<xref ref-type="bibr" rid="B34">Khatri and Tyagi, 2015</xref>; <xref ref-type="bibr" rid="B52">Sidabutar et al., 2017</xref>; <xref ref-type="bibr" rid="B57">Tavakoly Sany et al., 2019</xref>). Furthermore, according to a recent investigation conducted by Lin and colleagues (2022), over 80% of sewage produced by human actions is released into rivers and oceans untreated, leading to environmental contamination and over 50 different diseases, affecting valuable coastal ecosystems and the quality of people&#x2019;s lives (<xref ref-type="bibr" rid="B27">Halliday and Gast, 2011</xref>; <xref ref-type="bibr" rid="B21">Freeman et al., 2019</xref>; <xref ref-type="bibr" rid="B43">Nalley et al., 2021</xref>; <xref ref-type="bibr" rid="B37">Lin et al., 2022</xref>). As a result, efforts to reverse the effects of poor planning of land alterations have been made in some regions, one example being the creation of the Gu&#xe1;nica Bay Watershed Management Plan (<xref ref-type="bibr" rid="B14">CWP, 2008</xref>).</p>
<p>The Gu&#xe1;nica Bay Watershed is a 151 square miles system located in southwest Puerto Rico, encompassing Gu&#xe1;nica&#x2019;s municipality, a portion of Yauco, Sabana Grande, San Germ&#xe1;n, Maricao, Adjuntas, and Lares municipalities, and a segment of the Lajas Valley Agricultural region, all of which drain to the bay through the Loco River (<xref ref-type="bibr" rid="B14">CWP, 2008</xref>; <xref ref-type="bibr" rid="B61">Viqueira and Rios, 2018</xref>). Watersheds in southern Puerto Rico have been highly developed to sustain domestic activities, irrigation, and hydroelectric power infrastructures (<xref ref-type="bibr" rid="B14">CWP, 2008</xref>; <xref ref-type="bibr" rid="B25">G&#xf3;mez-G&#xf3;mez et al., 2014</xref>). Specifically, in Gu&#xe1;nica, the Gu&#xe1;nica Lagoon, which covered an area of approximately 1,200 acres, was drained in the 1950s as part of the Southwest Puerto Rico Project for agricultural and hydroelectric power purposes (<xref ref-type="bibr" rid="B63">Viqueira Rios et al., 2012</xref>). These developments come with a cost to marine coastal ecosystems, as impacts such as marine sedimentary deposition, eutrophication, and shifts on coastal species composition have been reported in some countries (<xref ref-type="bibr" rid="B41">Morais, 2008</xref>; <xref ref-type="bibr" rid="B50">Sanger et al., 2013</xref>).</p>
<p>One of the most impacted ecosystems of watershed development are adjacent coral reefs that face challenges like the introduction of pollutants causing nutrient enrichment beyond tolerance of reefs, along with marine sedimentary deposition, pathogens, and toxic contaminants (<xref ref-type="bibr" rid="B48">Richmond et al., 2007</xref>; <xref ref-type="bibr" rid="B14">CWP, 2008</xref>). In Puerto Rico, stressed and degraded coral reefs have been reported along the insular shelf, particularly in the Gu&#xe1;nica to Guayanilla region in the southwest (<xref ref-type="bibr" rid="B67">Warne et al., 2005</xref>). As a result, the Gu&#xe1;nica Bay Watershed was selected as a priority area for management by the U.S. Coral Reef Task Force (<xref ref-type="bibr" rid="B68">Whitall et al., 2013</xref>; <xref ref-type="bibr" rid="B56">Takesue et al., 2021</xref>). The Gu&#xe1;nica Bay Watershed Management Plan was developed in 2008 to reduce the non-point sources of pollution that arrived at the bay and protect adjacent coral reefs (<xref ref-type="bibr" rid="B14">CWP, 2008</xref>). Some of the recommendations in the plan included the restoration of the Gu&#xe1;nica Lagoon, agricultural outreach, the creation of treatment wetlands, among others (<xref ref-type="bibr" rid="B14">CWP, 2008</xref>). Reports created by <xref ref-type="bibr" rid="B61">Viqueira R&#xed;os, 2018</xref>, <xref ref-type="bibr" rid="B62">Viqueira R&#xed;os, 2021</xref>) highlight the management actions implemented from 2012 to 2022, which include the application of hydroseeding (2013&#x2013;2017), training farmers on Best Management Practices (BMP) and shade coffee farming, and the creation of infiltration systems in farms using native trees and vetiver grass (2010&#x2013;2016), to name a few. However, some of these management actions, i.e., the restoration of the Gu&#xe1;nica Lagoon and the creation of the treatment wetlands, which were the first two most important recommendations from the management plan, had not yet been implemented by the end of this work.</p>
<p>Assessment of watershed management requires integrating a broad range of spatial and temporal data (<xref ref-type="bibr" rid="B64">Wang et al., 2016</xref>). Although no reports assessing the effectiveness of the management actions in Gu&#xe1;nica were found, refinements and suggestions to improve the management plan considering stakeholder concerns have been made (<xref ref-type="bibr" rid="B11">Carriger et al., 2013</xref>; <xref ref-type="bibr" rid="B53">Smith et al., 2017</xref>). Therefore, the goal of this work was to evaluate the effectiveness of the management actions implemented from the Gu&#xe1;nica Bay Watershed Management Plan created to protect adjacent coral reefs using remote sensing techniques. We aimed to describe temporal trends of the diffuse attenuation coefficient at 490&#xa0;nm (Kd<sub>490</sub>) in one managed (Gu&#xe1;nica Bay) and three non-managed (reference) estuaries in Southwest Puerto Rico before and after the implementation of the Gu&#xe1;nica Bay Watershed Management Plan. It is noteworthy to mention that during the after period of this study the following major events affected our area of study: Hurricanes Irma and Mar&#xed;a in 2017, and Fiona in 2022, with significant impacts to the area. Also, the Covid-19 pandemic outbreak and associated lockdowns, and a magnitude 6.4 earthquake near Gu&#xe1;nica, all of which could result in decreased human impact in the watershed and coastal waters.</p>
<p>The diffuse attenuation coefficient (Kd) of downward irradiance is an apparent optical property of water widely used in oceanographic studies, a good indicator of turbidity, and one of the parameters for which ocean color algorithms have been developed using satellite sensors (<xref ref-type="bibr" rid="B35">Lee et al., 2005</xref>; <xref ref-type="bibr" rid="B65">Wang et al., 2009</xref>; <xref ref-type="bibr" rid="B58">Tomlinson et al., 2019</xref>; Y; <xref ref-type="bibr" rid="B66">Wang et al., 2022</xref>). For this purpose, the European Space Agency (ESA) launched in 2002 the Medium Resolution Imaging Spectrometer (MERIS) on board the Envisat satellite to provide ocean color observations. The Ocean Land Colour Instrument (OLCI) was launched in 2016 and 2018 on Sentinel-3 A and B, respectively, as successors of MERIS to ensure long term monitoring with improvements such as additional spectral bands, mitigation of sun-glint by tilting the sensors, and improved signal to noise ratio (<xref ref-type="bibr" rid="B44">Nieke et al., 2015</xref>). In Puerto Rico, satellite estimates have been used to study long term trends and disturbance impacts (e.g., hurricanes) in coastal water quality using chlorophyll-a, Kd at 490&#xa0;nm (Kd<sub>490</sub>), and absorption of colored dissolved organic matter (<xref ref-type="bibr" rid="B24">Gilbes et al., 2001</xref>; <xref ref-type="bibr" rid="B3">Amirrezvani, 2016</xref>; <xref ref-type="bibr" rid="B23">Garc&#xed;a-Sais et al., 2017</xref>; <xref ref-type="bibr" rid="B28">Hern&#xe1;ndez et al., 2020</xref>; <xref ref-type="bibr" rid="B46">Ortiz-Rosa et al., 2020</xref>). Furthermore, an inverse relationship between Kd and percent coral cover was observed in Ponce, Guayanilla, La Parguera, Mayag&#xfc;ez Bay, and other areas, which suggests that Kd can be used for indirect assessment of the percent living coral cover in coral reefs (<xref ref-type="bibr" rid="B10">Cardona-Maldonado, 2008</xref>; <xref ref-type="bibr" rid="B23">Garc&#xed;a-Sais et al., 2017</xref>; <xref ref-type="bibr" rid="B22">Freitas et al., 2019</xref>). While Puerto Rico has no coastal water quality standards of light attenuation, <xref ref-type="bibr" rid="B28">Hern&#xe1;ndez et al. (2020)</xref> suggest using 0.1 m<sup>-1</sup> to reference degraded water quality adopted from Hawai&#x2019;i standards.</p>
<p>If management actions implemented in the Gu&#xe1;nica Bay watershed have been effective, it was expected that: i) values of Kd<sub>490</sub> decreased in Gu&#xe1;nica Bay after the implementation of management actions, and ii) this temporal change would be observed only in Gu&#xe1;nica Bay as compared to reference estuaries. It is important to note that the matter in question was not to compare estuaries as they are part of different watersheds and might change through time but to detect whether trends in Gu&#xe1;nica Bay were unique when compared to reference estuaries which could be attributable to the implementation of management actions to this watershed.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>2 Materials and methods</title>
<sec id="s2-1">
<title>2.1 Study area</title>
<p>The areas of interest for this study were one managed and three not managed estuaries located in south and southwest Puerto Rico (<xref ref-type="fig" rid="F1">Figure 1</xref>). Puerto Rico has two defined seasons, the dry (December-April) and wet (May-November) seasons (<xref ref-type="bibr" rid="B29">Hern&#xe1;ndez Ayala and M&#xe9;ndez Tejeda, 2023</xref>). Guanajibo River, located in southwestern Puerto Rico, has a relatively humid climate and prevailing winds are from the west, however, the southern estuaries, Gu&#xe1;nica Bay, Guayanilla Bay and Descalabrado River, have a semi-arid climate and wind direction is from the northeast from midnight to early morning, and from the southeast during the day (<xref ref-type="bibr" rid="B25">G&#xf3;mez-G&#xf3;mez et al., 2014</xref>). Gu&#xe1;nica and Guayanilla Bays are estuaries classified as semi-closed bays where exchange between fresh and seawater is direct. On the other hand, Guanajibo and Descalabrado estuaries are river mouths, which tend to be stratified as seawater is denser than fresh water (<xref ref-type="bibr" rid="B15">DNER, 2016</xref>). Seawater temperatures in Puerto Rico do not change much since solar radiation is nearly constant throughout the year, with October having a mean maximum water temperature of 28&#xb0;C, and January having the mean minimum of 24&#xb0;C (<xref ref-type="bibr" rid="B25">G&#xf3;mez-G&#xf3;mez et al., 2014</xref>). Three of the study sites are in regions where stressed coral reefs have been reported, these sites are Guanajibo River, Gu&#xe1;nica, and Guayanilla Bays, in contrast, coral reefs from the south where Descalabrado River is have been classified as &#x201c;healthy&#x201d; (<xref ref-type="bibr" rid="B67">Warne et al., 2005</xref>). The water depth of the estuaries was assessed using bathymetry data for Puerto Rico derived from Sentinel-2 imagery and the apparent optical depth (AOD &#x3d; 1.3/Kd), which is the depth at which light penetration is optically significant, was calculated (<xref ref-type="fig" rid="F2">Figure 2</xref>; <xref ref-type="sec" rid="s10">Supplementary Table S1</xref>) (<xref ref-type="bibr" rid="B6">Bailey and Werdel, 2006</xref>; <xref ref-type="bibr" rid="B36">Li et al., 2021</xref>). Lastly, the shelf sediment composition in the southern estuaries is carbonate clastic, which has the effect of increasing Kd, and in the western estuary is mixed terrigenous and carbonate clastic sediments (<xref ref-type="bibr" rid="B67">Warne et al., 2005</xref>; <xref ref-type="bibr" rid="B30">Hochberg et al., 2020</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Map of the study area in Southwest Puerto Rico consisting of one managed estuary (Gu&#xe1;nica Bay) and three not managed estuaries (1: Guanajibo River, 2: Guayanilla Bay, 3: Descalabrado River).</p>
</caption>
<graphic xlink:href="frsen-05-1404323-g001.tif"/>
</fig>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Map showing the four estuaries selected for the study with the locations and their bathymetries. The red arrows indicate the river mouths and where the bathymetry layer ends it is the shelf break.</p>
</caption>
<graphic xlink:href="frsen-05-1404323-g002.tif"/>
</fig>
</sec>
<sec id="s2-2">
<title>2.2 Study design</title>
<p>To assess changes of Kd<sub>490</sub> related to the implementation of the Gu&#xe1;nica Bay management plan, a beyond-Before-After-Control-Impact (beyond-BACI) design was used (<xref ref-type="fig" rid="F3">Figure 3</xref>). This method, proposed to evaluate environmental impacts, consists in measuring the variable of interest before and after the occurrence of an anthropogenic disturbance at the location of the disturbance and reference location/s (<xref ref-type="bibr" rid="B26">Green, 1979</xref>; <xref ref-type="bibr" rid="B31">Hurlbert, 1984</xref>; <xref ref-type="bibr" rid="B55">Stewart-Oaten et al., 1986</xref>; <xref ref-type="bibr" rid="B59">Underwood, 1994</xref>). It has been described as one of the most powerful models for assessing environmental impacts when well-designed (<xref ref-type="bibr" rid="B54">Smokorowski and Randall, 2017</xref>; <xref ref-type="bibr" rid="B6">Bertocci et al., 2021</xref>). Additionally, the BACI design has been combined with novel remote sensing techniques to develop a web-based application to monitor changes in vegetation using the MODIS sensor (<xref ref-type="bibr" rid="B47">Poortinga et al., 2018</xref>). BACI studies in the ocean have been in acoustical studies, but it is not a widely used method in oceanography (<xref ref-type="bibr" rid="B51">Seger et al., 2021</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>The beyond-BACI approach used to evaluate management efforts for the Gu&#xe1;nica Bay Watershed. The locations are classified as C: Close, E: East, W: West, and F: Far from the river mouths. The plot boxes indicate the number of pixels selected for analysis at each location (3).</p>
</caption>
<graphic xlink:href="frsen-05-1404323-g003.tif"/>
</fig>
<p>The before period for this study consists of 6&#xa0;years before the development of the Gu&#xe1;nica Bay Watershed Management Plan (2002&#x2013;2008) and the after period consists of 6&#xa0;years after (2016&#x2013;2022) in which management actions were implemented in the Gu&#xe1;nica Bay Watershed (<xref ref-type="bibr" rid="B61">Viqueira Rios, 2018</xref>). There is an 8-year gap between both periods because of a gap between sensors (2012&#x2013;2016) and to ensure implementation of the management recommendations. In each estuary, the managed and three not managed, four locations were selected in terms of proximity to the river mouths. These locations were classified as Close, East, West, and Far from the river mouths (<xref ref-type="fig" rid="F2">Figure 2</xref>). The Far locations at all estuaries were selected outside of the insular shelf (offshore). <xref ref-type="bibr" rid="B5">Bailey and Werdell (2006)</xref> recommend the use of a pixel box for more accurate satellite estimations, nonetheless, at each location the plot consisted of three pixels randomly selected instead of a pixel box, avoiding land and bottom reflectance interference, due to limitations in the spatial resolution of the satellite sensors (300&#xa0;m) used for the proposed study sites.</p>
</sec>
<sec id="s2-3">
<title>2.3 Remotely sensed data collection and analysis</title>
<p>Level 2 Water Full Resolution Imagery for Puerto Rico was obtained from two sensors, MERIS on board Envisat and OLCI on board Sentinel-3 A and B. MERIS imagery was downloaded through the MERIS Online Dissemination Service and OLCI imagery was downloaded from the EUMETSAT Data Services portal (<xref ref-type="bibr" rid="B19">EUMETSAT, 2017</xref>; <xref ref-type="bibr" rid="B18">ESA, 2020</xref>; <xref ref-type="bibr" rid="B20">EUMETSAT, 2022</xref>). The MERIS dataset for this study covers the years 2002&#x2013;2008 and the OLCI dataset covers 2016&#x2013;2022. Using different sensors would not affect data analysis as the fourth MERIS reprocessing was focused to align with Sentinel-3/OLCI data processing chains to allow for continuity of the products derived from the instruments (<xref ref-type="bibr" rid="B2">Alhammoud and Meris Validation Team, 2019</xref>). Nonetheless, an oceanic site was selected in the northwest of Puerto Rico, where terrestrial input is unlikely to affect the continuity evaluation of the sensors (<xref ref-type="sec" rid="s10">Supplementary Figure S1</xref>). The satellite imagery was processed using the SeNtinel Application Platform (SNAP), where a series of steps were built using the graph builder tool to allow for batch processing of the imagery (<xref ref-type="fig" rid="F4">Figure 4</xref>). The pixel extraction of Kd<sub>490</sub> values for each location within estuary and the additional oceanic site was done using the Kd fail mask as a filter to remove contaminated pixels, but any value higher than 1&#xa0;m<sup>-1</sup> was removed from the dataset before analysis because they corresponded to contaminated pixels, e.g., land contamination (<xref ref-type="fig" rid="F4">Figure 4</xref>). Lastly, the Level-3 Binning tool was used to generate Kd<sub>490</sub> averages for the before and after periods and the Band Maths tool was used to generate a difference map for our area of interest. The algorithm used to derive the Kd<sub>490</sub> product was developed by <xref ref-type="bibr" rid="B42">Morel et al. (2007)</xref> and is suitable for non-turbid ocean waters (Kd<sub>490</sub> &#x3c; 0.3 m<sup>-1</sup>) as is the case in our area of study (<xref ref-type="bibr" rid="B65">Wang et al., 2009</xref>; <xref ref-type="bibr" rid="B23">Garc&#xed;a-Sais et al., 2017</xref>; <xref ref-type="bibr" rid="B28">Hern&#xe1;ndez et al., 2020</xref>; <xref ref-type="bibr" rid="B46">Ortiz-Rosa et al., 2020</xref>). It uses a ratio of the irradiance reflectance at 490&#xa0;nm and 560&#xa0;nm:<disp-formula id="equ1">
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</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Workflow used to download and process satellite imagery using the SeNtinel Application Platform and to extract Kd<sub>490</sub> values in southwest Puerto Rico.</p>
</caption>
<graphic xlink:href="frsen-05-1404323-g004.tif"/>
</fig>
<p>A permutational multivariate analysis of variance (PERMANOVA) was used to analyze the reduced data for which a centroid was calculated for each season (wet and dry) per year using PRIMER v7 (<xref ref-type="bibr" rid="B13">Clarke and Gorley, 2015</xref>). This multivariate model considers that Kd<sub>490</sub> can respond differently to multiple factors, specifically the condition of managed and not managed, localization of the estuary, and location/proximity to the mouth of the river within the estuary (<xref ref-type="fig" rid="F2">Figure 2</xref>). The analyses used 9,999 permutations of the residuals under a reduced model to produce a null distribution (<xref ref-type="bibr" rid="B4">Anderson et al., 2008</xref>).</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec id="s3-1">
<title>3.1 Oceanic site</title>
<p>A total of 269 valid pixels of Kd<sub>490</sub> estimates were extracted from the MERIS dataset (2002&#x2013;2008) and a total of 1,276 estimates were extracted from the OLCI dataset (2016&#x2013;2022) at the oceanic site to allow comparison between sensors. The analysis to evaluate the continuity of the sensors estimates in the oceanic site shows that there is not a significant change (<italic>p</italic> &#x3d; 0.06) between sensor estimates (<xref ref-type="table" rid="T1">Table 1</xref>).</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Analysis of variance for the oceanic site of the diffuse attenuation coefficient at 490&#xa0;nm (Kd<sub>490</sub>) values in the Before (2002&#x2013;2008) and After period (2016&#x2013;2022).</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Source of variation</th>
<th align="left">df</th>
<th align="left">SS</th>
<th align="left">MS</th>
<th align="left">Pseudo-F</th>
<th align="left">P (perm)</th>
<th align="left">Unique perms</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Period</td>
<td align="right">1</td>
<td align="right">0.006</td>
<td align="right">0.006</td>
<td align="right">5.613</td>
<td align="right">0.062</td>
<td align="right">994</td>
</tr>
<tr>
<td align="left">Year (Period)</td>
<td align="right">12</td>
<td align="right">0.009</td>
<td align="right">0.001</td>
<td align="right">0.597</td>
<td align="right">0.831</td>
<td align="right">999</td>
</tr>
<tr>
<td align="left">Residual</td>
<td align="right">1,531</td>
<td align="right">1.977</td>
<td align="right">0.001</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Total</td>
<td align="right">1,544</td>
<td align="right">1.995</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-2">
<title>3.2 Patterns of Kd<sub>490</sub> in the estuaries</title>
<p>As for the results of the main goal of this study, the PERMANOVA results indicated that interaction at the lowest level, year by estuary, is statistically significant (<italic>p</italic> &#x3d; 0.001) (<xref ref-type="table" rid="T2">Table 2</xref>); indicating that the patterns of temporal variation were not the same for each estuary (<xref ref-type="fig" rid="F5">Figure 5</xref>). Interaction at the second lowest level, period by location, was also significant (<italic>p</italic> &#x3d; 0.0001); also indicating that temporal changes detected between periods were unique for each location within estuary. The interactions between year and condition, period by estuary, and period by condition were not statistically significant (<italic>p</italic> &#x3e; 0.05).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Permutation multivariate analysis of variance on a mixed multifactorial model for the managed and not managed estuaries before and after the implementation of management actions.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Source of variation</th>
<th align="left">df</th>
<th align="left">SS</th>
<th align="left">MS</th>
<th align="left">Pseudo-F</th>
<th align="left">P (perm)</th>
<th align="left">Unique perms</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Period &#x3d; P</td>
<td align="right">1</td>
<td align="right">0.0157</td>
<td align="right">0.0157</td>
<td align="right">3.6043</td>
<td align="right">0.0413</td>
<td align="right">9,958</td>
</tr>
<tr>
<td align="left">Condition &#x3d; C</td>
<td align="right">1</td>
<td align="right">0.1763</td>
<td align="right">0.1763</td>
<td align="right">4.0594</td>
<td align="right">0.0338</td>
<td align="right">9,947</td>
</tr>
<tr>
<td align="left">Year(P) &#x3d; Y(P)</td>
<td align="right">26</td>
<td align="right">0.0852</td>
<td align="right">0.0033</td>
<td align="right">8.7921</td>
<td align="right">0.0001</td>
<td align="right">9,916</td>
</tr>
<tr>
<td align="left">Estuary(C) &#x3d; E(C)</td>
<td align="right">2</td>
<td align="right">0.0866</td>
<td align="right">0.0433</td>
<td align="right">0.2726</td>
<td align="right">0.7676</td>
<td align="right">9,948</td>
</tr>
<tr>
<td align="left">PxC</td>
<td align="right">1</td>
<td align="right">0.0004</td>
<td align="right">0.0004</td>
<td align="right">0.5226</td>
<td align="right">0.8036</td>
<td align="right">9,954</td>
</tr>
<tr>
<td align="left">Location (E(C)) &#x3d; L (E(C))</td>
<td align="right">12</td>
<td align="right">1.9095</td>
<td align="right">0.1591</td>
<td align="right">789.87</td>
<td align="right">0.0001</td>
<td align="right">9,920</td>
</tr>
<tr>
<td align="left">PxE(C)</td>
<td align="right">2</td>
<td align="right">0.0024</td>
<td align="right">0.0012</td>
<td align="right">1.08</td>
<td align="right">0.3538</td>
<td align="right">9,931</td>
</tr>
<tr>
<td align="left">Y(P)xC</td>
<td align="right">26</td>
<td align="right">0.0064</td>
<td align="right">0.0002</td>
<td align="right">0.6581</td>
<td align="right">0.8442</td>
<td align="right">9,921</td>
</tr>
<tr>
<td align="left">PxL (E(C))</td>
<td align="right">12</td>
<td align="right">0.011</td>
<td align="right">0.0009</td>
<td align="right">4.5356</td>
<td align="right">0.0001</td>
<td align="right">9,912</td>
</tr>
<tr>
<td align="left">Y(P)xE ((C)</td>
<td align="right">52</td>
<td align="right">0.0194</td>
<td align="right">0.0004</td>
<td align="right">1.8511</td>
<td align="right">0.0016</td>
<td align="right">9,865</td>
</tr>
<tr>
<td align="left">Residual</td>
<td align="right">312</td>
<td align="right">0.0629</td>
<td align="right">0.0002</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Total</td>
<td align="right">447</td>
<td align="right">2.4012</td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Kd<sub>490</sub> distributions in <bold>(A)</bold> Gu&#xe1;nica Bay, <bold>(B)</bold> Guayanilla Bay, <bold>(C)</bold> Descalabrado River, and <bold>(D)</bold> Guanajibo River. The legend to identify each location within estuary is in panel <bold>(A)</bold>. The red line separates the before and the after periods.</p>
</caption>
<graphic xlink:href="frsen-05-1404323-g005.tif"/>
</fig>
<p>Overall, the lowest Kd<sub>490</sub> values were observed in Gu&#xe1;nica, while the highest values were observed in Guayanilla Bay, particularly in the Close location (<xref ref-type="fig" rid="F5">Figures 5A, B</xref>). In Gu&#xe1;nica Bay, Kd<sub>490</sub> values ranged from &#x223c;0.05&#x2013;0.15 m<sup>-1</sup> and in Guayanilla ranged from &#x223c;0.05&#x2013;0.3 m<sup>-1</sup> except for two anomalies in 2016 and 2018 (<xref ref-type="fig" rid="F5">Figures 5A, B</xref>). In Descalabrado they ranged from &#x223c;0.05&#x2013;0.25 m<sup>-1</sup> and in Guanajibo from &#x223c;0.05&#x2013;0.3 m<sup>-1</sup> (<xref ref-type="fig" rid="F5">Figures 5C,D</xref>). From <xref ref-type="fig" rid="F5">Figures 5A&#x2013;D</xref> a general pattern of Kd<sub>490</sub> in the estuaries can be seen, which was the Far location having lower Kd<sub>490</sub> values than other locations, as expected. Another generalized pattern was the Close location having the highest Kd<sub>490</sub> values at all estuaries in the southern region, but this was not observed in the Guanajibo River, probably due to differences between wind patterns and that the West location was closest to land in this site. In the southern estuaries, a peak can be observed in 2018, the year after the passage of Hurricane Maria on the island, and a decrease in 2020, the year of the Covid-19 pandemic outbreak and a 6.4 magnitude earthquake offshore of southwest Puerto Rico, indicating less human impact during this year (<xref ref-type="fig" rid="F5">Figures 5A, B, D</xref>). The averages from the before and after period showed Kd<sub>490</sub> values ranging from 0.02 m<sup>-1</sup>&#x2013;1&#xa0;m <sup>-1</sup> and the difference plot (After-Before) showed increasing values in the insular shelf and decreasing values in the open ocean (<xref ref-type="fig" rid="F6">Figure 6</xref>).</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Maps of Southwest Puerto Rico showing the average Kd<sub>490</sub> values for the before and after periods with the difference between periods (after-before).</p>
</caption>
<graphic xlink:href="frsen-05-1404323-g006.tif"/>
</fig>
<sec id="s3-2-1">
<title>3.2.1 Gu&#xe1;nica Bay</title>
<p>The Gu&#xe1;nica Bay estuary showed the least variability within locations of all estuaries, having Kd<sub>490</sub> values close to 0.1 m<sup>-1</sup> in all locations, except in the Far (offshore) location where values were closer to 0.05 m<sup>-1</sup> (<xref ref-type="fig" rid="F5">Figure 5A</xref>). Observations from the Before period showed steady trends, i.e., no increase or decrease of Kd<sub>490</sub>&#xa0;at all locations, the West location being the most variable. In the After period, more variability and a subtle upward trend can be observed in all locations except for the Far location. The Close, East, and West locations followed similar patterns of temporal variability in the After period, more noticeably in the last 5&#xa0;years. In these locations, an increase of Kd<sub>490</sub> can be observed in the 2016&#x2013;2018 period, and a decrease in the 2019&#x2013;2020 period, after which it started to increase again.</p>
</sec>
<sec id="s3-2-2">
<title>3.2.2 Guayanilla Bay</title>
<p>Guayanilla Bay showed significantly higher Kd<sub>490</sub> values, &#x223c;0.3 m<sup>-1</sup>, in the Close location as compared to the rest of the locations where not much variability was observed (<xref ref-type="fig" rid="F5">Figure 5B</xref>). In the Before period, all locations remained steady throughout time. The Close location had noticeable peaks in the 2016&#x2013;2018 period, which showed a &#x223c;0.05 m<sup>-1</sup> increase when compared to the rest of the period. Similar patterns as those of Gu&#xe1;nica Bay can be observed at all locations in the after period, where the 2016&#x2013;2018 period followed an upward trend and the 2019&#x2013;2020 period followed a downward trend after which it started to increase again. In the After period, the East and West location followed a more similar pattern and closer values than the Before period.</p>
</sec>
<sec id="s3-2-3">
<title>3.2.3 Descalabrado River</title>
<p>The second most variable estuary within locations was the Descalabrado River estuary ranging from &#x223c;0.5&#x2013;0.25 m<sup>-1</sup> (<xref ref-type="fig" rid="F5">Figure 5C</xref>). The Before period showed steady trends of Kd<sub>490</sub>&#xa0;at the East, West, and Far locations while the Close location showed an upward trend up to 2005 after which it started to decrease. In the After period, similar patterns as the ones described for Gu&#xe1;nica and Guayanilla Bays are observed in the Close, East, and West locations. The 2016&#x2013;2018 period showed an upward trend and the 2019&#x2013;2020 period showed a downward trend after which it started to increase. The Far location appeared to show a slight decrease of Kd<sub>490</sub> for this period.</p>
</sec>
<sec id="s3-2-4">
<title>3.2.4 Guanajibo River</title>
<p>The Guanajibo River estuary showed the most variability of Kd<sub>490</sub> within locations with a range of &#x223c;0.5&#x2013;0.3 m<sup>-1</sup> (<xref ref-type="fig" rid="F5">Figure 5D</xref>). All of them followed very similar patterns at different amplitudes. This estuary was the only where the West location reported the highest Kd<sub>490</sub> values. The West and Close locations showed a downward trend in the Before period. In the After period, a peak can be observed in 2016, except for the East location where it is not noticeable, and no upward or downward trend can be observed.</p>
</sec>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>To evaluate the effectiveness of the Gu&#xe1;nica management actions, it was hypothesized that Kd<sub>490</sub> values would decrease in Gu&#xe1;nica after the implementation of the management actions. The temporal distribution patterns observed were not consistent with an improvement, i.e., a decrease of Kd<sub>490</sub>, following the management actions, thus, the hypothesis is rejected. The results of the analysis indicated that there were differences between the before and after periods, but they were unique to each location within the estuary. This means that variability within the estuaries was high, and each location followed different patterns of temporal variability of Kd<sub>490</sub> driven by conditions unique to the location. Even in the managed estuary, Gu&#xe1;nica Bay, variability of turbidity between the locations (Close, East, West, and Far) was high. The oceanic continuity analysis indicated that there was not a significant difference between sensor estimates, which allows for the integration of the sensors estimates for long-term trend monitoring and time series analysis. Any differences between sensor estimates can be explained by the improvements applied in the OLCI sensor (mitigation of sun-glint, improved signal-to-noise ratio) and improved revisit times, thus being able to detect more variability and accurate estimates than the MERIS sensor (See <xref ref-type="sec" rid="s10">Supplementary Figure S2</xref> showing the increase in frequency sampling) (<xref ref-type="bibr" rid="B44">Nieke et al., 2015</xref>).</p>
<p>Waters in the estuaries can be considered non-turbid ocean waters (Kd<sub>490</sub> &#x3c; 0.3 m<sup>-1</sup>) (<xref ref-type="bibr" rid="B65">Wang et al., 2009</xref>). Generalized patterns were observed in the southern estuaries which showed the highest Kd<sub>490</sub> values in the Close location and the lowest values in the Far location as expected. The Guanajibo River estuary, located in southwest Puerto Rico, followed different patterns which can be attributed by the difference in prevailing trade winds in this area causing higher resuspension rates near the shore (<xref ref-type="bibr" rid="B40">Miller and Cruise, 1995</xref>; <xref ref-type="bibr" rid="B25">G&#xf3;mez-G&#xf3;mez et al., 2014</xref>). The 2018 peak observed in the southern estuaries coincides with the fact that the impact of Hurricane Maria in September 2017 has been reported to have lasted up to 4&#xa0;months after its passage through the island driven by mass wasting, sediment runoff, coastal turbidity, and bottom sediment resuspension (<xref ref-type="bibr" rid="B57">Takesue Sany et al., 2019</xref>). <xref ref-type="bibr" rid="B33">Keellings and Hern&#xe1;ndez Ayala, (2019)</xref> reported the extreme rainfall event associated with the hurricane while <xref ref-type="bibr" rid="B7">Bessette-Kirton et al. (2019)</xref> reported a low landside density (1&#x2013;25 landslides/Km<sup>2</sup>) in the upland region of the Gu&#xe1;nica Bay watershed. In terms of water quality, Ortiz-Rosa and colleagues (2020) reported that absorption of colored dissolved organic matter or detrital gelbstoff (aCDOM/ADG) was high (&#x3e;0.13 m<sup>-1</sup>) in Gu&#xe1;nica 4&#xa0;months after the event as well as the coastal total suspended sediment (TSM) (&#x223c;5&#x2013;6&#xa0;mg/L) values as reported by <xref ref-type="bibr" rid="B39">Miller et al. (2019)</xref>. Additionally, <xref ref-type="bibr" rid="B28">Hern&#xe1;ndez et al. (2020)</xref> reported high Kd<sub>490</sub> (&#x3e;0.1 m<sup>-1</sup>) and Chlorophyll-a concentration (&#x3e;0.45&#xa0;&#x3bc;g/L) values and <xref ref-type="bibr" rid="B12">Cheriton et al. (2019)</xref> reported increased concentrations of suspended particulate material (SPM) and polychlorinated biphenyls (PCBs), a highly toxic industrial contaminant, after the hurricane. This peak was not observed in the Guanajibo River which could be explained due to the low impact of the hurricane on the west side of the island and possibly to its difference in sediment type, i.e., mixed terrigenous and carbonate clastic. The COVID-19 lockdowns as well as a major earthquake offshore of southwest Puerto Rico in 2020 caused less anthropogenic disturbance to the coastal environment in this area, and therefore improved water quality, which is why a trough can be observed in 2020&#x2013;2021 in some of the estuaries, a phenomenon also observed in other areas of the world (<xref ref-type="sec" rid="s10">Supplementary Figure S2</xref>) (<xref ref-type="bibr" rid="B9">Callejas et al., 2021</xref>; <xref ref-type="bibr" rid="B45">Ormaza-Gonz&#xe1;lez et al., 2021</xref>; <xref ref-type="bibr" rid="B60">Vijay Prakash et al., 2021</xref>).</p>
<p>This study is limited to changes observed in light attenuation and does not account for other coral reef threats such as increased seawater temperature and disease outbreaks (<xref ref-type="bibr" rid="B49">Rogers and Ramos-Scharr&#xf3;n, 2022</xref>). Events of resuspension, changes in precipitation, and tropical cyclone frequency and intensity cannot be eliminated as sources of change in Kd<sub>490</sub> values. Furthermore, Gu&#xe1;nica&#x2019;s water discharge is managed and not direct and the primary discharge occurs in Gu&#xe1;nica Bay and then flushes out to the south where our stations were located. Additionally, a recent study found that watersheds located east of Gu&#xe1;nica are a predominant source of sediments to Gu&#xe1;nica&#x2019;s reefs, suggesting that management efforts should also be implemented in these watersheds (<xref ref-type="bibr" rid="B56">Takesue et al., 2021</xref>). In addition, the impact of the hurricanes on water clarity probably outweighed the efforts made in the watershed to improve water quality. It has also been of discussion that ecosystems follow complicated responses after restoration efforts because there can be a lag time between efforts and evidence of recovery as well as other environmental stressors, e.g., climate change, affecting water quality, resulting in shifting baselines (<xref ref-type="bibr" rid="B32">Jeppesen et al., 2005</xref>; <xref ref-type="bibr" rid="B16">Duarte et al., 2009</xref>).</p>
<p>This work is a preliminary assessment of the management actions implemented in the Gu&#xe1;nica Bay Watershed by 2022, in which the two most important recommendations by the management plan, i.e., restoration of the Gu&#xe1;nica Lagoon and construction of treatment wetlands, had not been completed (<xref ref-type="bibr" rid="B62">Viqueira Rios, 2021</xref>). Future assessments should be done after the implementation of these two management actions and a more comprehensive analysis that includes other remotely sensed water quality parameters and <italic>in situ</italic> measurements is recommended.</p>
<p>This study is the first attempt, to our knowledge, to evaluate the Gu&#xe1;nica Bay Watershed Management Plan in terms of water quality. Results from this work could potentially contribute to determining whether current strategies in the Gu&#xe1;nica Bay watershed should be continued, stopped, or modified. Other contributions could include the implementation of management actions in other watersheds with anthropogenic impacts like those of the Gu&#xe1;nica Bay Watershed or the refinement of future watershed management plans developed to improve water quality in Gu&#xe1;nica Bay and other areas around the world.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s5">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s10">Supplementary Material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s6">
<title>Author contributions</title>
<p>AG-M: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing&#x2013;original draft, Writing&#x2013;review and editing. RA: Conceptualization, Funding acquisition, Resources, Supervision, Writing&#x2013;review and editing. JC-M: Conceptualization, Formal Analysis, Methodology, Supervision, Visualization, Writing&#x2013;review and editing. WH: Conceptualization, Funding acquisition, Resources, Supervision, Writing&#x2013;review and editing.</p>
</sec>
<sec sec-type="funding-information" id="s7">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This publication was made possible by the National Oceanic and Atmospheric Administration, Office of Education Educational Partnership Program under Agreement No. NA16SEC4810006. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration.</p>
</sec>
<ack>
<p>Thanks to colleagues &#xc1;ngel A. Acosta-Col&#xf3;n for his valuable feedback during this research and Jenniffer P&#xe9;rez P&#xe9;rez for her guidance in image processing. Thanks to Isabel R&#xed;os from the Information Technology Center at the University of Puerto Rico, Mayag&#xfc;ez Campus for her support with ArcGIS Pro software and to Clark E Sherman for his input to coastal sediment processes. Lastly, thanks to Michelle Tomlinson, Sachi Mishra, Andrew Meredith, Richard Stumpf, and the NCCOS team for their training and help during Kd<sub>490</sub> evaluation efforts in Puerto Rico.</p>
</ack>
<sec sec-type="COI-statement" id="s8">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s9">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s10">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/frsen.2024.1404323/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/frsen.2024.1404323/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet1.docx" id="SM1" mimetype="application/docx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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