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Towards Quantitative Spatial Models of Seabed Sediment Composition

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  • David Stephens
  • Markus Diesing

Abstract

There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom’s parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel) using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.

Suggested Citation

  • David Stephens & Markus Diesing, 2015. "Towards Quantitative Spatial Models of Seabed Sediment Composition," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-23, November.
  • Handle: RePEc:plo:pone00:0142502
    DOI: 10.1371/journal.pone.0142502
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    Cited by:

    1. Edward J Gregr & Dana R Haggarty & Sarah C Davies & Cole Fields & Joanne Lessard, 2021. "Comprehensive marine substrate classification applied to Canada’s Pacific shelf," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-28, October.
    2. Luisetti, Tiziana & Turner, R. Kerry & Andrews, Julian E. & Jickells, Timothy D. & Kröger, Silke & Diesing, Markus & Paltriguera, Lucille & Johnson, Martin T. & Parker, Eleanor R. & Bakker, Dorothee C, 2019. "Quantifying and valuing carbon flows and stores in coastal and shelf ecosystems in the UK," Ecosystem Services, Elsevier, vol. 35(C), pages 67-76.

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