Zhang Yong, Zhang Yun, Ganjurjav Hasbagan, Yue Haitao, Tian Kun, Wang Hang, Zhang Qiong, Zhao Zijiao
Yunnan Key Laboratory of Plateau Wetland Conservation, Restoration and Ecological Services, Southwest Forestry University, Kunming, China.
Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agriculture Sciences, Beijing, China.
Front Plant Sci. 2025 Aug 12;16:1594772. doi: 10.3389/fpls.2025.1594772. eCollection 2025.
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 (), 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 . 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 . The appearance of plant communities, including cover, height, and aboveground biomass, demonstrated a linear decline across the degradation gradient ( < 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 ( < 0.05). Furthermore, the composition of plant communities exhibited a gradual shift from healthy to SD ( < 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.
现有文献中已对草原退化影响及恢复策略进行了广泛研究。然而，当前用于评估退化状况的诊断方法主要依赖经验方法或机理方法，导致各研究结果不一致。在此，我们提出一种基于地理编码和突变分析（GAAB）的方法来识别草原的退化状况。由植被盖度、高度、地上生物量、物种丰富度和植物群落的皮洛指数构建的植被生存状况，在GAAB方法中作为诊断草原退化阈值的指标。我们开发了一个规则系统来识别[此处原文缺失具体所指内容]的突变。此外，我们将该方法应用于中国大山包国家级自然保护区作为案例研究。我们发现，根据[此处原文缺失具体所指内容]突变所确定的阈值，大山包国家级自然保护区的亚高山草甸可分为四个相对退化水平，即健康、轻度退化（LD）、中度退化（MD）和重度退化（SD）。植物群落的外观，包括盖度、高度和地上生物量，在退化梯度上呈线性下降（<0.05）。相比之下，物种多样性的变化符合中度干扰理论，其中物种丰富度和皮洛指数在MD水平最高（<0.05）。此外，植物群落组成从健康到SD呈现逐渐变化（<0.05）。我们的结果表明，GAAB方法可为诊断退化状况提供一种非经验方法，从而增进对与草原退化相关复杂性的理解。
