Articles | Volume 23, issue 3
https://doi.org/10.5194/bg-23-881-2026
https://doi.org/10.5194/bg-23-881-2026
Research article
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02 Feb 2026
Research article | Highlight paper |  | 02 Feb 2026

Machine-learning models of δ13C and δ15N isoscapes in Amazonian wood

Isabela M. Souza-Silva, Luiz A. Martinelli, Brent Holmes, Ana C. G. Batista, Maria G. S. Araújo, Anna L. Garção, Stéphane Ponton, Peter Groenendijk, Giuliano M. Locosselli, Daigard R. Ortega-Rodriguez, Deoclecio J. Amorim, Fábio J. V. Costa, Gabriela B. Nardoto, Alexandre T. Brunello, Vladimir Eliodoro Costa, Gabriel Assis-Pereira, Mario Tomazello-Filho, Niro Higuchi, Ana C. Barbosa, João Paulo Sena-Souza, and Clément P. Bataille

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Co-editor-in-chief
The study by Souza-Silva and co-workers presents a comprehensive dataset and a foundational approach for assessing the legitimacy of logged wood in the Amazon basin—a topic of global relevance and high societal impact. Building on this dataset and further developing these methods will be critical for safeguarding the Amazon’s ecologically vital yet fragile forests into the future.
Short summary
Illegal logging is a major environmental concern in the Amazon. We tested whether the isotopic composition of carbon (δ13C) and nitrogen (δ15N) in wood can support timber traceability. Using machine-learning models, we generated basin-wide isoscapes showing that both isotopes capture consistent environmental gradients, providing a scientific basis to improve provenance verification and guide enforcement efforts.
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