Journal of Integrative Agriculture ›› 2025, Vol. 24 ›› Issue (3): 949-965.DOI: 10.1016/j.jia.2024.11.008

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利用BERTopic建模方法揭示和推进草地生态退化研究

  

  • 收稿日期:2024-05-20 接受日期:2024-09-13 出版日期:2025-03-20 发布日期:2025-02-28

Unveiling and advancing grassland degradation research using a BERTopic modelling approach

Tong Li1*#, Lizhen Cui2*, Yu Wu3*, Rajiv Pandey4, Hongdou Liu5, Junfu Dong6, Weijin Wang1, Zhihong Xu5, Xiufang Song7, 8#, Yanbin Hao2, 10, Xiaoyong Cui2, 10, Jianqing Du9, 10, Xuefu Zhang3#, Yanfen Wang9, 10, 11   

  1. 1 School of Agriculture and Food Sustainability, The University of Queensland, St Lucia 4072, Australia

    2 College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

    3 Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

    4 Indian Council of Forestry Research & Education, Dehradun 248006, India

    5 Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane 4111, Australia

    6 Institute of Marine Science and Technology, Shandong University, Qingdao 264209, China

    7 National Science Library, Chinese Academy of Sciences, Beijing 100190, China

    8 Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China

    9 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

    10 Beijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of Sciences, Beijing 101408, China

    11 State Key Laboratory of Tibetan Plateau Earth System Science (LATPES), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China

  • Received:2024-05-20 Accepted:2024-09-13 Online:2025-03-20 Published:2025-02-28
  • About author:#Correspondence Tong Li, E-mail: tong.li1@uq.edu.au; Xiufang Song, E-mail: songxf@mail.las.ac.cn; Xuefu Zhang, E-mail: zhangxuefu@caas.cn * These authors contributed equally to this study.
  • Supported by:

    This work was financially supported by the First-Class Curriculum Program at the School of Economics and Management, University of the Chinese Academy of Sciences, the National Natural Science Foundation of China (42041005), and the National Social Science Foundation of China (23BTQ054). 

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摘要：

草地退化对生物多样性、生态系统服务和依赖这些生态系统的社区的社会经济可持续性构成了严峻挑战。然而，由于传统科学计量方法的局限性，难以全面整合关于草地退化前沿和关键领域。为了克服这一问题，本文采用了BERTopic这一先进的自然语言处理工具，分析了关于草地退化的大量生态学文献。我们从Web of Science核心集合数据库中筛选了4504篇文献，评估了不同草地类型的地理分布及其时间演变趋势。分析结果揭示了草地退化研究的关键主题，包括草地退化对生态系统功能的影响、草地恢复和生物多样性保护、侵蚀过程以及草地水文模型等领域。与传统方法相比，BERTopic在识别目标文献中复杂且动态的主题变化方面表现出显著优势。BERTopic不仅揭示了热门研究方向，还发掘了传统方法可能忽视的新兴领域，提供了更广泛的研究视角；而科学计量学则在细节和特异性上表现突出。我们建议结合这两种方法，以更系统化和全面的方式评估研究现状。本研究展示了BERTopic算法在生态学中的新兴应用，尤其是在全球草地退化领域的应用。同时，这也凸显了在大数据时代，生态学研究亟需整合先进的计算工具。像BERTopic这样的算法对于深化我们对复杂环境问题的理解具有关键作用，标志着生态学向更为复杂的数据驱动分析迈出了重要一步。

Abstract:

Grassland degradation presents overwhelming challenges to biodiversity, ecosystem services, and the socio-economic sustainability of dependent communities.  However, a comprehensive synthesis of global knowledge on the frontiers and key areas of grassland degradation research has not been achieved due to the limitations of traditional scientometrics methods.  The present synthesis of information employed BERTopic, an advanced natural language processing tool, to analyze the extensive ecological literature on grassland degradation.  We compiled a dataset of 4,504 publications from the Web of Science core collection database and used it to evaluate the geographic distribution and temporal evolution of different grassland types and available knowledge on the subject.  Our analysis identified key topics in the global grassland degradation research domain, including the effects of grassland degradation on ecosystem functions, grassland ecological restoration and biodiversity conservation, erosion processes and hydrological models in grasslands, and others.  The BERTopic analysis significantly outperforms traditional methods in identifying complex and evolving topics in large datasets of literature.  Compared to traditional scientometrics analysis, BERTopic provides a more comprehensive perspective on the research areas, revealing not only popular topics but also emerging research areas that traditional methods may overlook, although scientometrics offers more specificity and detail.  Therefore, we argue for the simultaneous use of both approaches to achieve more systematic and comprehensive assessments of specific research areas.  This study represents an emerging application of BERTopic algorithms in ecological research, particularly in the critical research focused on global grassland degradation.  It also highlights the need for integrating advanced computational methods in ecological research in this era of data explosion.  Tools like the BERTopic algorithm are essential for enhancing our understanding of complex environmental problems, and it marks an important stride towards more sophisticated, data-driven analysis in ecology.

Key words: natural language processing , grassland degradation ,  knowledge synthesis ,  scientometrics ,  systematic review