Clustering analysis of the <i>Sargassum</i> transport process: application to beaching prediction in the Lesser Antilles

Bernard, Didier; Biabiany, Emmanuel; Cécé, Raphaël; Chery, Romual; Sekkat, Naoufal

doi:https://doi.org/10.5194/os-18-915-2022

Articles | Volume 18, issue 4

https://doi.org/10.5194/os-18-915-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/os-18-915-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 4

Research article

|

04 Jul 2022

Research article |

| 04 Jul 2022

Clustering analysis of the Sargassum transport process: application to beaching prediction in the Lesser Antilles

Didier Bernard, Emmanuel Biabiany, Raphaël Cécé, Romual Chery, and Naoufal Sekkat

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Cited articles

Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-minute global relief model: procedures, data sources and analysis, NOAA Technical Memorandum NESDIS NGDC-24, Marine Geology and Geophysics Division, Boulder, Colorado, 25 pp., https://doi.org/10.1594/PANGAEA.769615, 2009.

Anses: Expositions aux émanations d'algues sargasses en décomposition aux Antilles et en Guyane, Technical report, ANSES, Maisons-Alfort, France, 162 pp., available at: https://www.anses.fr/en/system/files/AIR2015SA0225Ra.pdf (last access: 20 October 2021), 2017.

Arnault, S., Thiria, S., Crépon, M., and Kaly, F.: A tropical Atlantic dynamics analysis by combining machine learning and satellite data, Adv. Space Res., 68, 467–486, https://doi.org/10.1016/j.asr.2020.09.044 2021.

Baklouti, M., Devenon, J.-L., Bourret, A., Froidefond, J.-M., Ternon, J.-F., and Fuda, J.-L.: New insights in the French Guiana continental shelf circulation and its relation to the North Brazil Current retroflection, J. Geophys. Res., 112, C02023, https://doi.org/10.1029/2006JC003520, 2007.

Becker, J. J., Sandwell, D. T., Smith, W. H. F., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S. H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A., Trimmer, R., Von Rosenberg, J., Wallace, G., and Weatherall, P.: Global Bathymetry and Elevation Data at 30 Arc Seconds Resolution: SRTM30_PLUS, Mar. Geod., 32, 355–371, 2009.

Putman, N. F., Goni, G. J., Gramer, L. J., Hu, C., Johns, E. M., Trinanes, J., and Wang, M.: Simulating transport pathways of pelagic Sargassum from the equatorial Atlantic into the Caribbean Sea, Prog. Oceanogr., 165, 205–214, https://doi.org/10.1016/j.pocean.2018.06.009, 2018.

Putman, N. F., Lumpkin, R., Olascoaga, M. J., Trinanes, J., and Goni, G. J.: Improving transport predictions of pelagic Sargassum, J. Exp. Mar. Biol. Ecol., 529, 151398, https://doi.org/10.1016/j.jembe.2020.151398, 2020.

Resiere, D., Valentino, R., Nevière R., Banydeen, R., Gueye, P., Florentin, J., Cabié, A., Lebrun, T., Mégarbane B., Guerrier G., and Mehdaoui, H.: Sargassum seaweed on Caribbean islands: an international public health concern, Lancet, 392, 2691, https://doi.org/10.1016/S0140-6736(18)32777-6, 2018.

Rousseeuw, P. J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Mathemat., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987.

SargassumWatch System (SAWS): Sargassum Outlook Bulletin, University of South Florida, https://optics.marine.usf.edu/projects/SaWS.html, last access: 15 June 2021

Studer, M. and Ritschard, G.: What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures, J. Roy. Stat. Soc. Ser. A, 179, 481–511, https://doi.org/10.1111/rssa.12125, 2016.

Swain, P. H. and Hauska, H.: The decision tree classifier: Design and potential, IEEE Trans. Geosci. Elect., 15, 142–147, https://doi.org/10.1109/TGE.1977.6498972, 1977.

Trinanes, J., Putman, N. F., Goni, G., Hu, C., and Wang, M.: Monitoring pelagic Sargassum inundation potential for coastal communities, J. Operat. Oceanogr., 14, 1–12, https://doi.org/10.1080/1755876X.2021.1902682, 2021.

Van Tussenbroek, B. I., Hernández Arana, H. A., Rodríguez-Martínez, R. E., Espinoza-Avalos, J., Canizales-Flores, H. M., González-Godoy, C. E., Barba-Santos, M. G., Vega-Zepeda, A., and Collado-Vides, L.: Severe impacts of brown tides caused by Sargassum spp. on near-shore Caribbean seagrass communities, Mar. Pollut. Bull., 122, 272–281. https://https://doi.org/10.1016/j.marpolbul.2017.06.057, 2017.

Wang, M. and Hu, C.: Mapping and quantifying Sargassum distribution and coverage in the central West Atlantic using MODIS observations, Remote Sens. Environ., 183, 350–367, https://doi.org/10.1016/j.rse.2016.04.019, 2016.

Wang, M. and Hu, C.: Predicting Sargassum blooms in the Caribbean Sea from MODIS observations, Geophys. Res. Lett., 44, 3265–3273, https://doi.org/10.1002/2017GL072932, 2017.

Wang, M., Hu, C., Cannizzaro, J., English, D., Han, X., Naar, D., Lapointe, B., Brewton, R., and Hernandez, F.: Remote sensing of Sargassum biomass, nutrients, and pigments, Geophys. Res. Lett., 45, 12359–12367, https://doi.org/10.1029/2018GL078858, 2018.

Wang, M., Hu, C., Barnes, B. B., Mitchum, G., Lapointe, B., and Montoya, J. P.: The great Atlantic Sargassum belt, Science, 365, 83–87, https://doi.org/10.1126/science.aaw7912, 2019.

Ward Jr., J. H.: Hierarchical Grouping to Optimize an Objective Function, J. Am. Stat. Assoc., 58, 236–244, https://doi.org/10.1080/01621459.1963.10500845, 1963.

Webster, R. K. and Linton, T.: Development and implementation of Sargassum Early Advisory System (SEAS), Shore & Beach, 81, 1–6, http://www.sargassoseacommission.org/storage/Webster_et_linon_2013_1.pdf (last access: 15 February 2022), 2013.

Witherington, B., Hirama S., and Hardy, R.: Youngsea turtles of the pelagic Sargassum-dominated drift community: habitat use, population density, and threats, Mar. Ecol. Prog. Ser., 463, 1–22, https://doi.org/10.3354/meps09970, 2012.

Zhao, Q., Basher, Z., and Costello, M. J.: Mapping near surface global marine ecosystems through cluster analysis of environmental data, Ecol. Res., 35, 327–342, https://doi.org/10.1111/1440-1703.12060, 2020.

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The massive Sargassum algae beachings observed over the past decade are a new natural hazard currently impacting the island states of the Caribbean region. This study aims to improve the prediction of the surface current dynamic leading to beachings in the Lesser Antilles using clustering analysis methods. The present clustering analysis predictive system would help improve this risk management in the islands of the region.

The massive Sargassum algae beachings observed over the past decade are a new natural hazard...

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