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Modeling Spatial Patterns of Humus Forms in Montane and Subalpine Forests: Implications of Local Variability for Upscaling

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  • Niels Hellwig

    (Institute of Geography, Osnabrück University, Seminarstraße 19ab, 49074 Osnabrück, Germany
    Institute of Earth and Environmental Science, Potsdam University, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany
    Faculty of Business Management and Social Sciences, Osnabrück University of Applied Sciences, Caprivistraße 30a, 49076 Osnabrück, Germany)

  • Dylan Tatti

    (Functional ecology laboratory, University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland
    Division Agronomie, Haute école des sciences agronomiques, forestières et alimentaires HAFL, Länggasse 85, 3052 Zollikofen, Switzerland)

  • Giacomo Sartori

    (Museo Tridentino di Scienze Naturali, Corso del Lavoro e della Scienza 3, 38122 Trento, Italy)

  • Kerstin Anschlag

    (Institute of Geography, Osnabrück University, Seminarstraße 19ab, 49074 Osnabrück, Germany)

  • Ulfert Graefe

    (IFAB Institut für Angewandte Bodenbiologie GmbH, Tornberg 24a, 22337 Hamburg, Germany)

  • Markus Egli

    (Department of Geography, University of Zürich, Winterthurerstrasse 190, 8057 Zurich, Switzerland)

  • Jean-Michel Gobat

    (Functional ecology laboratory, University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland)

  • Gabriele Broll

    (Institute of Geography, Osnabrück University, Seminarstraße 19ab, 49074 Osnabrück, Germany)

Abstract

Humus forms are a distinctive morphological indicator of soil organic matter decomposition. The spatial distribution of humus forms depends on environmental factors such as topography, climate and vegetation. In montane and subalpine forests, environmental influences show a high spatial heterogeneity, which is reflected by a high spatial variability of humus forms. This study aims at examining spatial patterns of humus forms and their dependence on the spatial scale in a high mountain forest environment (Val di Sole/Val di Rabbi, Trentino, Italian Alps). On the basis of the distributions of environmental covariates across the study area, we described humus forms at the local scale (six sampling sites), slope scale (60 sampling sites) and landscape scale (30 additional sampling sites). The local variability of humus forms was analyzed with regard to the ground cover type. At the slope and landscape scale, spatial patterns of humus forms were modeled applying random forests and ordinary kriging of the model residuals. The results indicate that the occurrence of the humus form classes Mull, Mullmoder, Moder, Amphi and Eroded Moder generally depends on the topographical position. Local-scale patterns are mostly related to micro-topography (local accumulation and erosion sites) and ground cover, whereas slope-scale patterns are mainly connected with slope exposure and elevation. Patterns at the landscape scale show a rather irregular distribution, as spatial models at this scale do not account for local to slope-scale variations of humus forms. Moreover, models at the slope scale perform distinctly better than at the landscape scale. In conclusion, the results of this study highlight that landscape-scale predictions of humus forms should be accompanied by local- and slope-scale studies in order to enhance the general understanding of humus form patterns.

Suggested Citation

  • Niels Hellwig & Dylan Tatti & Giacomo Sartori & Kerstin Anschlag & Ulfert Graefe & Markus Egli & Jean-Michel Gobat & Gabriele Broll, 2018. "Modeling Spatial Patterns of Humus Forms in Montane and Subalpine Forests: Implications of Local Variability for Upscaling," Sustainability, MDPI, vol. 11(1), pages 1-15, December.
  • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:48-:d:192366
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    References listed on IDEAS

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    1. Tomislav Hengl & Gerard B M Heuvelink & Bas Kempen & Johan G B Leenaars & Markus G Walsh & Keith D Shepherd & Andrew Sila & Robert A MacMillan & Jorge Mendes de Jesus & Lulseged Tamene & Jérôme E Tond, 2015. "Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-26, June.
    2. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
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