Artículo

Variation in wood density across South American tropical forests

Sullivan, Martin J. P. ; Phillips, Oliver L. ; Galbraith, David ; Almeida, Everton ; de Oliveira, Edmar Almeida ; Almeida, Jarcilene ; Álvarez Dávila, Esteban ; Alves, Luciana F. ; Andrade, Ana ; Aragão, Luiz ; Araujo Murakami, Alejandro ; Arets, Eric ; Arroyo, Luzmila ; Melo Cruz, Omar Aurelio ; Baccaro, Fabrício ; Baker, Timothy R. ; Banki, Olaf ; Baraloto, Christopher ; Barlow, Jos ; Barroso, Jorcely ; Berenguer, Erika ; Blanc, Lilian ; Blundo, Cecilia Mabel Icon; Bonal, Damien ; Bongers, Frans ; Malizia, Lucio Ricardo ; Malizia, Agustina Icon; Vos, Vincent A. ; Wortel, Verginia ; Ishida, Francoise Yoko ; Zuidema, Pieter A. ; Zwerts, Joeri A.

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Fecha

03/2025

Editorial

Nature Publishing Group

Revista

Nature Communications

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ISSN

2041-1723

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Inglés

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Resumen

Wood density is a critical control on tree biomass, so poor understanding of its spatial variation can lead to large and systematic errors in forest biomass estimates and carbon maps. The need to understand how and why wood density varies is especially critical in tropical America where forests have exceptional species diversity and spatial turnover in composition. As tree identity and forest composition are challenging to estimate remotely, ground surveys are essential to know the wood density of trees, whether measured directly or inferred from their identity. Here, we assemble an extensive dataset of variation in wood density across the most forested and tree-diverse continent, examine how it relates to spatial and environmental variables, and use these relationships to predict spatial variation in wood density over tropical and sub-tropical South America.Our analysis refines previously identified eastwest Amazon gradients inwood density, improves themby revealing fine-scale variation, and extends predictions into Andean, dry, and Atlantic forests. The results halve biomass prediction errors compared to a naïve scenario with no knowledge of spatial variation in wood density. Our findings will help improve remote sensing-based estimates of aboveground biomass carbon stocks across tropical South America.

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