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Research Article | Open Access

A bibliometric analysis using machine learning to track paradigm shifts and analytical advances in forest ecology and forestry journal publications from 2010 to 2022

Jin Zhaoa,b, Liyu Lia, Jian Liua, Yimei Yana, Qian Wanga, Chris Newmanc, Youbing Zhoua,b( )
College of Biological & Pharmaceutical Sciences, China Three Gorges University, Daxue Road 8, Yichang 443002, China
Hubei International Scientific & Technological Cooperation Center of Ecological Conservation & Management in Three Gorges Area, China Three Gorges University, Daxue Road 8, Yichang 443002, China
Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Oxford, United Kingdom
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Abstract

Forest habitats are critical for biodiversity, ecosystem services, human livelihoods, and well-being. Capacity to conduct theoretical and applied forest ecology research addressing direct (e.g., deforestation) and indirect (e.g., climate change) anthropogenic pressures has benefited considerably from new field- and statistical-techniques. We used machine learning and bibliometric structural topic modelling to identify 20 latent topics comprising four principal fields from a corpus of 16,952 forest ecology/forestry articles published in eight ecology and five forestry journals between 2010 and 2022. Articles published per year increased from 820 in 2010 to 2,354 in 2021, shifting toward more applied topics. Publications from China and some countries in North America and Europe dominated, with relatively fewer articles from some countries in West and Central Africa and West Asia, despite globally important forest resources. Most study sites were in some countries in North America, Central Asia, and South America, and Australia. Articles utilizing R statistical software predominated, increasing from 29.5% in 2010 to 71.4% in 2022. The most frequently used packages included lme4, vegan, nlme, MuMIn, ggplot2, car, MASS, mgcv, multcomp and raster. R was more often used in forest ecology than applied forestry articles. R software offers advantages in script and workflow-sharing compared to other statistical packages. Our findings demonstrate that the disciplines of forest ecology/forestry are expanding both in number and scope, aided by more sophisticated statistical tools, to tackle the challenges of redressing forest habitat loss and the socio-economic impacts of deforestation.

Keywords

Forest ecologyForestryR softwareStructural topic modellingMachine learningPublication

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Forest Ecosystems
Volume 11 Issue 5,
October 2024
Article number: 100233
DOI: 10.1016/j.fecs.2024.100233
Cite this article:
Zhao J, Li L, Liu J, et al. A bibliometric analysis using machine learning to track paradigm shifts and analytical advances in forest ecology and forestry journal publications from 2010 to 2022. Forest Ecosystems, 2024, 11(5): 100233. https://doi.org/10.1016/j.fecs.2024.100233

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Received: 09 February 2024
Revised: 28 July 2024
Accepted: 28 July 2024
Published: 02 August 2024
© 2024 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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