Assessing grassland degradation based on abrupt changes in living status of vegetation in a subalpine meadow

文献类型： 外文期刊

第一作者： Zhang, Yong

作者： Zhang, Yong;Zhang, Yun;Yue, Haitao;Tian, Kun;Wang, Hang;Ganjurjav, Hasbagan;Zhang, Qiong;Zhao, Zijiao

作者机构：

关键词： geo-coding; Mann-Kendall abrupt analysis; threshold; vegetation status; grassland degradation

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Grassland degradation impacts and restoration strategies have been extensively studied in existing literature. Nevertheless, current diagnostic approaches for assessing degradation conditions predominantly rely on either empirical or mechanistic approaches, leading to inconsistent findings across studies. Here, we proposed a geo-coding and abrupt analysis based (GAAB) method to identify the degradation conditions of grasslands. The living status of vegetation (LSV), which was constructed by cover, height, aboveground biomass, species richness, and the Pielou index of the plant community, served as the indicator in the GAAB method for diagnosing the thresholds of grassland degradation. We developed a rule system to identify abrupt changes in LSV. Furthermore, we applied this method in the Dashanbao National Nature Reserve in China as a case study. We found that the subalpine meadows in the Dashanbao National Nature Reserve could be classified into four relative degradation levels, i.e. healthy, light degradation (LD), moderate degradation (MD), and severe degradation (SD), according to the thresholds that identified by abrupt alterations of the LSV. The appearance of plant communities, including cover, height, and aboveground biomass, demonstrated a linear decline across the degradation gradient (p < 0.05). In contrast, changes in species diversity aligned with the theory of moderate interference, where species richness and the Pielou index were highest in the MD level (p < 0.05). Furthermore, the composition of plant communities exhibited a gradual shift from healthy to SD (p < 0.05). Our results suggest that the GAAB method could offer a non-empirical approach for diagnosing degradation conditions, thereby enhancing the understanding of the complexities associated with grassland degradation.

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