College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Beijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of Sciences, Beijing 101408, China; State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Beijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of Sciences, Beijing 101408, China; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China; Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China; Binzhou Institute of Technology, Weiqiao-UCAS Science and Technology Park, Binzhou 256606, China.
Sci Bull (Beijing). 2023 Sep 15;68(17):1928-1937. doi: 10.1016/j.scib.2023.07.035. Epub 2023 Jul 25.
Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm.
青藏高原草地结构信息对于理解关键生态系统功能的变化及其潜在驱动因素至关重要，这些变化可能反映了环境变化。然而，由于方法学的限制，该地区的此类信息仍然缺乏。除了仅识别植被生产力的遥感指标外，我们首次利用多元数据融合和深度学习技术，首次在青藏高原地区的区域尺度上对高山草地的基于形成的植物群落结构进行了特征描述，并将其与中国早期的植被图进行了比较。在过去的 40 年中，我们揭示了（1）高山草原在高山草地中的比例从 50%增加到 69%，很好地反映了变暖变湿的趋势；（2）高山草原和草原中 Kobresia pygmaea 和 Stipa purpurea 形成的优势度分别增强到 76%和 92%；（3）气候因素主要驱动了 Stipa purpurea 形成的分布，但最近 Kobresia pygmaea 形成的分布并非如此，后者可能是人类活动塑造的。因此，过去 40 年草地变化的潜在机制被认为是基于形成的。总的来说，本研究中对植物群落变化结构信息的首次探索不仅为理解青藏高原地区草地变化及其空间异质性的驱动因素提供了新的视角，而且创新了大规模植被研究范式。
