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 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.