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<p>The Qinghai-Tibe Plateaut (QTP) plays a vital role in ecological stability in China and Asia. Investigating the spatial variations and nonlinear connections of landscape ecological risk (LER) drivers on the QTP is crucial for fostering sustainable development and ecological security in the region. This study utilized land use data from 1980 to 2020 to establish a LER assessment model for the QTP. Employing exploratory spatial data analysis methods, it described the spatiotemporal evolution characteristics of LER. Using the Multiscale Geographically Weighted Regression (MGWR) model and machine learning methods to explored spatial heterogeneity and nonlinear connection of the influencing factors of LER in the QTP. The results suggest that: (1) The LER in the QTP is predominantly of moderate risk, with high-risk regions concentrated in the north and northwest. In contrast, the southern and southeastern regions are mainly of low risk, relative landscape stability. (2) The global Moran’s index of LER on the QTP demonstrates an increasing trend, indicating positive correlation and enhanced spatial autocorrelation. (3) The MGWR model demonstrates varying influences of human activities and natural factors on the QTP’s LER at different scales: per capita GDP fluctuates, population consistently impacts positively, precipitation and slope negatively affect. Nonlinear analysis identifies precipitation as the most influential factor on LER (94.57%), followed by DEM (63.58%), while per capita GDP and night light have lower, insignificant impacts.</p>

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Spatial heterogeneity and nonlinear relationship between landscape ecological risk and drivers: a case of the Qinghai-Tibet Plateau, China

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Fazi Zhang,
  • Jingyao Lin,
  • Qiang Wang,
  • Yuying Lin,
  • Niu Dang,
  • Yinan Li

摘要

The Qinghai-Tibe Plateaut (QTP) plays a vital role in ecological stability in China and Asia. Investigating the spatial variations and nonlinear connections of landscape ecological risk (LER) drivers on the QTP is crucial for fostering sustainable development and ecological security in the region. This study utilized land use data from 1980 to 2020 to establish a LER assessment model for the QTP. Employing exploratory spatial data analysis methods, it described the spatiotemporal evolution characteristics of LER. Using the Multiscale Geographically Weighted Regression (MGWR) model and machine learning methods to explored spatial heterogeneity and nonlinear connection of the influencing factors of LER in the QTP. The results suggest that: (1) The LER in the QTP is predominantly of moderate risk, with high-risk regions concentrated in the north and northwest. In contrast, the southern and southeastern regions are mainly of low risk, relative landscape stability. (2) The global Moran’s index of LER on the QTP demonstrates an increasing trend, indicating positive correlation and enhanced spatial autocorrelation. (3) The MGWR model demonstrates varying influences of human activities and natural factors on the QTP’s LER at different scales: per capita GDP fluctuates, population consistently impacts positively, precipitation and slope negatively affect. Nonlinear analysis identifies precipitation as the most influential factor on LER (94.57%), followed by DEM (63.58%), while per capita GDP and night light have lower, insignificant impacts.