Spatially non-stationary relationships between landscape fragmentation and soil conservation services in China, 2000–2018

Ecological Indicators(2024)

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摘要
Soil conservation (SC), which supports ecosystems by retaining vegetation, controlling erosion, retaining sediment, cycling nutrients, retaining water, maintaining diversity, and forming soil, is threatened by human-induced soil erosion and landscape fragmentation. Therefore, revealing the spatial relationship between landscape fragmentation (LF) and SC can provide a scientific basis for ecosystem conservation policy-making and landscape planning in China. However, previous studies failed to reveal the spatially non-stationary response of SC to LF, especially at the national scale. To compensate for the research gaps, this study first measured the SC at the county scale with the benefit transfer method, then the LF was characterized with landscape pattern indices, and finally the spatial relationships between the SC and LF from global and local perspectives through regression models were analyzed. Results showed that national values of SC services per unit area in 2000, 2010, and 2018 were $134.73/ha, $132.30/ha, and $130.71/ha, respectively, showing a continuous decreasing trend. The high-value areas were distributed in the south of the Qinling-Huaihe River, Greater Khingan Mountains, and Changbai Mountain areas. In contrast, the low-value areas were mainly distributed in northwest China. LF was low in the northwestern region, while Shanxi, Shaanxi, Hebei, and southern Gansu presented highly fragmented landscapes. The spatial regression results showed that the higher the regional LF, the higher the value of SC. Meanwhile, there was significant spatial heterogeneity in the response of SC to LF, which was closely related to population distribution and resource endowment. In the future, landscape planning, land use structure and layout can take the impact mechanism of LF on SC as the essential basis.
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关键词
Soil conservation,Landscape fragmentation,Landscape pattern index,Geographically weighted regression model,China
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