AUTHOR=Zhang Yong , Zhang Yun , Ganjurjav Hasbagan , Yue Haitao , Tian Kun , Wang Hang , Zhang Qiong , Zhao Zijiao 

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1594772

DOI=10.3389/fpls.2025.1594772

ISSN=1664-462X

ABSTRACT=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.