Journal of Plant Ecology ›› 2025, Vol. 18 ›› Issue (1): 1-14.DOI: 10.1093/jpe/rtaf010

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2008–2023年R在生态学研究中的应用

  

  • 收稿日期:2024-11-18 接受日期:2025-01-08 出版日期:2025-02-01 发布日期:2025-04-06

A comprehensive analysis of R’s application in ecological research from 2008 to 2023

Meixiang Gao1,2, Yanyan Ye1, Ye Zheng3 and Jiangshan Lai2,*   

  1. 1Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China
    2College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China
    3Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

    *Corresponding author. E-mail: lai@njfu.edu.cn
  • Received:2024-11-18 Accepted:2025-01-08 Online:2025-02-01 Published:2025-04-06
  • Supported by:
    This study was supported by the National Nature Science Foundation of China (grant nos 32271551, 42271051, 42471054), Jiangsu Social Development Project (grant no. BE2022792) and the Metasequoia fund of Nanjing Forestry University.

摘要: 生态学领域因计算工具与统计方法的深度整合取得了显著进展,其中R语言以其强大的灵活性和核心作用,已成为生态研究不可或缺的分析工具。随着生态学研究的快速发展,深入理解近年来R语言的使用趋势及其具体应用模式对于推动学科前沿具有重要意义。本研究系统分析了2008至2023年间,发表在40种生态学期刊上的125 494篇学术文章中R语言及其软件包的使用情况。结果显示,共有52 658篇文章(42%)将R语言作为主要分析工具,其使用率呈现持续且显著的线性增长,从2008年的10.3%提升至2023年的66.9%。在R语言的众多软件包中,"lme4"、“vegan”、“nlme”、“MuMIn”、“ape”、“ggplot2”、“car”、“mgcv”、“MASS”、“raster”、“multcomp”和“lmerTest”这12个软件包的引用量均超过1000篇,成为生态研究的重要工具。其中,“lme4”的高频使用反映了混合效应模型在生态学研究中的核心地位,这些模型在解决复杂生态学问题方面表现出极高的应用价值。此外,不同期刊对R软件包的偏好与其所属的学科领域密切相关,而单篇文章中R软件包的平均使用数量逐年增加,表明生态学分析方法正日益复杂化和多样化。本研究揭示了R语言的发展与生态学研究之间的相互促进关系,强调了数量生态学者、R语言程序包开发者与生态学家之间加强协作的必要性。这种协同合作不仅有助于推动R语言功能的优化与扩展,同时也为生态学领域的持续进步提供了坚实的技术支持。

关键词: 生态学研究, 生态学期刊, R软件, R软件包, R使用率

Abstract: The field of ecology has been greatly enhanced by the integration of computational tools and statistical methods, with the programming language R emerging as a pivotal and flexible tool for ecological research. As ecological studies accelerate, understanding the prevalent trends and specific usage patterns of R in recent research is crucial. This study investigated the use of R and its packages in 125 494 scholarly articles published in 40 ecology journals from 2008 to 2023. A total of 52 658 articles (42%) designated R as their principal analytical tool, demonstrating a steady linear growth in its utilization from 10.3% in 2008 to 66.9% in 2023. Twelve R packages, including ‘lme4’, ‘vegan’, ‘nlme’, ‘MuMIn’, ‘ape’, ‘ggplot2’, ‘car’, ‘mgcv’, ‘MASS’, ‘raster’, ‘multcomp’ and ‘lmerTest’, each played a pivotal role in contributing to more than 1000 scholarly articles. The highest usage rate of the 'lme4' package indicates that mixed-effect models have a particularly important role in ecological research, and the application of these models has helped ecologists solve many important scientific problems. Journal-specific package preferences aligned with their scientific domains, while the rise in the average number of R packages per article indicates a trend towards more complex and diverse analytical methods in ecology. Our findings reveal a reciprocal relationship between the development of R and ecological research, underscoring the need for collaboration among quantitative ecologists, R developers and ecologists to further advance both the language and the field. Such collaboration will not only enhance the functionality and versatility of R but also provide robust technical support for the continued progress of ecological research.

Key words: ecology research, ecology journal, R software, R packages, use rate of R