Jin Zhao;Liyu Li;William P. Meehan;Yan Yan;Q. WANG;Chris Newman;Boyu Le

articles;forestry;forest ecology;applied;ecology;202

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 some countries in North America, Europe, and China 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 North America, Central Asia, South America, and Australia. R statistical software predominated articles, increasing from 29.5% of articles in 2010 to 71.4% in 2022, principally lme4, vegan, nlme, MuMIn, ggplot2, car, MASS, mgcv, multcomp and raster packages. R was more often used in forest ecology than applied forestry articles. R software has script and workflow-sharing advantages over competing statistical packages. Our findings demonstrate that the disciplines of forest ecology/forestry are expanding 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.