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Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation

Author

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  • Mi-Young Kim

    (Graduate Program, Department of Environmental Science, Konkuk University, Gwangjin-Gu, Seoul 05029, Korea)

  • Sang-Woo Lee

    (Department of Forestry and Landscape Architecture, Konkuk University, Gwangjin-Gu, Seoul 05029, Korea)

Abstract

Multiple studies have been conducted to identify the complex and diverse relationships between stream ecosystems and land cover. However, these studies did not consider spatial dependency inherent from the systemic structure of streams. Therefore, the present study aimed to analyze the relationship between green/urban areas and topographical variables with biological indicators using regression tree analysis, which considered spatial autocorrelation at two different scales. The results of the principal components analysis suggested that the topographical variables exhibited the highest weights among all components, including biological indicators. Moran′s I values verified spatial autocorrelation of biological indicators; additionally, trophic diatom index, benthic macroinvertebrate index, and fish assessment index values were greater than 0.7. The results of spatial autocorrelation analysis suggested that a significant spatial dependency existed between environmental and biological indicators. Regression tree analysis was conducted for each indicator to compensate for the occurrence of autocorrelation; subsequently, the slope in riparian areas was the first criterion of differentiation for biological condition datasets in all regression trees. These findings suggest that considering spatial autocorrelation for statistical analyses of stream ecosystems, riparian proximity, and topographical characteristics for land use planning around the streams is essential to maintain the healthy biological conditions of streams.

Suggested Citation

  • Mi-Young Kim & Sang-Woo Lee, 2021. "Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation," IJERPH, MDPI, vol. 18(10), pages 1-19, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5150-:d:553486
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    References listed on IDEAS

    as
    1. Jong-Won Lee & Sang-Woo Lee & Kyung-Jin An & Soon-Jin Hwang & Nan-Young Kim, 2020. "An Estimated Structural Equation Model to Assess the Effects of Land Use on Water Quality and Benthic Macroinvertebrates in Streams of the Nam-Han River System, South Korea," IJERPH, MDPI, vol. 17(6), pages 1-16, March.
    2. Yirigui Yirigui & Sang-Woo Lee & A. Pouyan Nejadhashemi & Matthew R. Herman & Jong-Won Lee, 2019. "Relationships between Riparian Forest Fragmentation and Biological Indicators of Streams," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    3. Everaert, Gert & Boets, Pieter & Lock, Koen & Džeroski, Sašo & Goethals, Peter L.M., 2011. "Using classification trees to analyze the impact of exotic species on the ecological assessment of polder lakes in Flanders, Belgium," Ecological Modelling, Elsevier, vol. 222(14), pages 2202-2212.
    4. Daniel A. Griffith, 2009. "Spatial Autocorrelation in Spatial Interaction," Advances in Spatial Science, in: Aura Reggiani & Peter Nijkamp (ed.), Complexity and Spatial Networks, chapter 0, pages 221-237, Springer.
    5. Yirigui Yirigui & Sang-Woo Lee & A. Pouyan Nejadhashemi, 2019. "Multi-Scale Assessment of Relationships between Fragmentation of Riparian Forests and Biological Conditions in Streams," Sustainability, MDPI, vol. 11(18), pages 1-24, September.
    6. Yung-Chul Jun & Doo-Hee Won & Soo-Hyung Lee & Dong-Soo Kong & Soon-Jin Hwang, 2012. "A Multimetric Benthic Macroinvertebrate Index for the Assessment of Stream Biotic Integrity in Korea," IJERPH, MDPI, vol. 9(10), pages 1-30, October.
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