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Modelling Beach Litter Accumulation on Mediterranean Coastal Landscapes: An Integrative Framework Using Species Distribution Models

Author

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  • Mirko Di Febbraro

    (EnviX–Lab, Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, IS, Italy
    EnviX–Lab, Department of Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, 86039 Termoli, CB, Italy
    These authors contributed equally to this work.)

  • Ludovico Frate

    (EnviX–Lab, Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, IS, Italy
    EnviX–Lab, Department of Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, 86039 Termoli, CB, Italy
    These authors contributed equally to this work.)

  • Maria Carla de Francesco

    (EnviX–Lab, Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, IS, Italy
    EnviX–Lab, Department of Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, 86039 Termoli, CB, Italy)

  • Angela Stanisci

    (EnviX–Lab, Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, IS, Italy
    EnviX–Lab, Department of Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, 86039 Termoli, CB, Italy)

  • Francesco Pio Tozzi

    (EnviX–Lab, Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, IS, Italy
    EnviX–Lab, Department of Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, 86039 Termoli, CB, Italy)

  • Marco Varricchione

    (EnviX–Lab, Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, IS, Italy
    EnviX–Lab, Department of Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, 86039 Termoli, CB, Italy)

  • Maria Laura Carranza

    (EnviX–Lab, Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone, 86090 Pesche, IS, Italy
    EnviX–Lab, Department of Biosciences and Territory, University of Molise, Via Duca degli Abruzzi, 86039 Termoli, CB, Italy)

Abstract

Beach litter accumulation patterns are influenced by biotic and abiotic factors, as well as by the distribution of anthropogenic sources. Although the importance of comprehensive approaches to deal with anthropogenic litter pollution is acknowledged, integrated studies including geomorphologic, biotic, and anthropic factors in relation to beach debris accumulation are still needed. In this perspective, Species Distribution Models (SDMs) might represent an appropriate tool to predict litter accumulation probability in relation to environmental conditions. In this context, we explored the applicability of a SDM–type modelling approach (a Litter Distribution Model; LDM) to map litter accumulation in coastal sand dunes. Starting from 180 litter sampling plots combined with fine–resolution variables, we calibrated LDMs from litter items classified either by their material type or origin. We also mapped litter accumulation hotspots. LDMs achieved fair-to-good predictive performance, with LDMs for litter classified by material type performing significantly better than models for litter classified by origin. Accumulation hotspots were mostly localized along the beach, by beach accesses, and at river mouths. In light of the promising results achieved by LDMs in this study, we conclude that this tool can be successfully applied within a coastal litter management context.

Suggested Citation

  • Mirko Di Febbraro & Ludovico Frate & Maria Carla de Francesco & Angela Stanisci & Francesco Pio Tozzi & Marco Varricchione & Maria Laura Carranza, 2021. "Modelling Beach Litter Accumulation on Mediterranean Coastal Landscapes: An Integrative Framework Using Species Distribution Models," Land, MDPI, vol. 10(1), pages 1-17, January.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:1:p:54-:d:477510
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    References listed on IDEAS

    as
    1. Di Febbraro, Mirko & D’Amen, Manuela & Raia, Pasquale & De Rosa, Davide & Loy, Anna & Guisan, Antoine, 2018. "Using macroecological constraints on spatial biodiversity predictions under climate change: the modelling method matters," Ecological Modelling, Elsevier, vol. 390(C), pages 79-87.
    2. Manhong Shen & Di Mao & Huiming Xie & Chuanzhong Li, 2019. "The Social Costs of Marine Litter along the East China Sea: Evidence from Ten Coastal Scenic Spots of Zhejiang Province, China," Sustainability, MDPI, vol. 11(6), pages 1-15, March.
    3. Maria Carla de Francesco & Maria Laura Carranza & Marco Varricchione & Francesco Pio Tozzi & Angela Stanisci, 2019. "Natural Protected Areas as Special Sentinels of Littering on Coastal Dune Vegetation," Sustainability, MDPI, vol. 11(19), pages 1-16, October.
    4. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    5. Bernardo Tabuenca & Marco Kalz & Ansje Löhr, 2019. "Massive Open Online Education for Environmental Activism: The Worldwide Problem of Marine Litter," Sustainability, MDPI, vol. 11(10), pages 1-16, May.
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