State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
Sci Total Environ. 2024 Oct 10;946:174295. doi: 10.1016/j.scitotenv.2024.174295. Epub 2024 Jun 25.
As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional climate change, directly impact primary productivity. This review comprehensively explored the effects of climate change on soil nitrogen (N), phosphorus (P), and their balance in the alpine meadows, highlighting the significant roles these nutrients played in plant growth and species diversity. We also shed light on machine learning utilization in soil nutrient evaluation. As global warming continues, alongside shifting precipitation patterns, soil characteristics of grasslands, such as moisture and pH values vary significantly, further altering the availability and composition of soil nutrients. The rising air temperature in alpine regions substantially enhances the activity of soil organisms, accelerating nutrient mineralization and the decomposition of organic materials. Combined with varied nutrient input, such as increased N deposition, plant growth and species composition are changing. With the robust capacity to use and integrate diverse data sources, including satellite imagery, sensor-collected spectral data, camera-captured videos, and common knowledge-based text and audio, machine learning offers rapid and accurate assessments of the changes in soil nutrients and associated determinants, such as soil moisture. When combined with powerful large language models like ChatGPT, these tools provide invaluable insights and strategies for effective grassland management, aiming to foster a sustainable ecosystem that balances high productivity and advanced services with reduced environmental impacts.
作为陆地生态系统，高山草原具有多样的植被类型，在调节水资源和碳储存方面发挥着关键作用，从而塑造全球气候。该生态系统中的土壤养分动态响应区域气候变化，直接影响初级生产力。本综述全面探讨了气候变化对高山草甸土壤氮（N）、磷（P）及其平衡的影响，强调了这些养分在植物生长和物种多样性中的重要作用。我们还介绍了机器学习在土壤养分评估中的应用。随着全球变暖的持续，以及降水模式的变化，草原土壤的特性，如水分和 pH 值，会发生显著变化，进一步改变土壤养分的有效性和组成。高山地区的空气温度升高，极大地提高了土壤生物的活性，加速了养分的矿化和有机物质的分解。再加上不同的养分输入，如增加的氮沉积，植物的生长和物种组成正在发生变化。机器学习具有强大的能力，可以利用包括卫星图像、传感器收集的光谱数据、摄像机拍摄的视频以及常见的基于知识的文本和音频在内的多种数据源，快速准确地评估土壤养分及其相关决定因素（如土壤水分）的变化。当与强大的大型语言模型（如 ChatGPT）结合使用时，这些工具为有效的草原管理提供了宝贵的见解和策略，旨在促进一个平衡高生产力和先进服务与减少环境影响的可持续生态系统。
