TY - JOUR
T1 - Combining wood traits as a promising timber origin verification and its application in the Brazilian trade chain
AU - Hornink, Bruna
AU - Ortega-Rodriguez, Daigard Ricardo
AU - Amorim, Deoclecio J.
AU - Groenendijk, Peter
AU - Paredes-Villanueva, Kathelyn
AU - Roquette, José Guilherme
AU - Barbosa, Ana Carolina M.C.
AU - Vidal, Edson
AU - Gontijo, Alexandre Bahia
AU - Costa, Monique S.
AU - Rodrigues Nunes de Senna, Norma
AU - de Lemos, Davi Neves
AU - Venegas-Gonzalez, Alejandro
AU - Callado, Catia H.
AU - Jaén-Barrios, Nelson
AU - Fontana, Cláudia
AU - Granato-Souza, Daniela
AU - Assis-Pereira, Gabriel
AU - Requena-Rojas, Edilson J.
AU - Portal-Cahuana, Leif A.
AU - Pereira, Lucas G.
AU - Chaddad, Fabio
AU - Bovi, Renata C.
AU - Carvalho, Hudson W.P.
AU - Tomazello-Filho, Mario
N1 - Publisher Copyright:
© 2025 Elsevier B.V.
PY - 2025/11/10
Y1 - 2025/11/10
N2 - Tracing the geographic origin of wood remains a major challenge in the fight against illegal timber in tropical countries. Methods using anatomical, chemical, isotopic and DNA markers have been successfully tested, but methods are either expensive, unpractical in the field or time consuming. By analyzing 17 Neotropical populations of Cedrela spp., we investigated the potential to improve wood provenance identification by combining wood traits measured with field adaptable equipment. Using machine learning models, we demonstrate that Ca, K, S, Al, wood density and tree growth rates predict wood origin with over 80 % accuracy at the regional scale and 63 % accuracy at the site level. Climate and soil conditions are the primary drivers of wood traits, particularly Ca, highlighting its value as a “fingerprint.” National and international efforts to build robust reference databases are needed. Our cost-effective and reliable method for tracing wood origins can be a powerful tool to aid law enforcement in fighting illegal timber trade.
AB - Tracing the geographic origin of wood remains a major challenge in the fight against illegal timber in tropical countries. Methods using anatomical, chemical, isotopic and DNA markers have been successfully tested, but methods are either expensive, unpractical in the field or time consuming. By analyzing 17 Neotropical populations of Cedrela spp., we investigated the potential to improve wood provenance identification by combining wood traits measured with field adaptable equipment. Using machine learning models, we demonstrate that Ca, K, S, Al, wood density and tree growth rates predict wood origin with over 80 % accuracy at the regional scale and 63 % accuracy at the site level. Climate and soil conditions are the primary drivers of wood traits, particularly Ca, highlighting its value as a “fingerprint.” National and international efforts to build robust reference databases are needed. Our cost-effective and reliable method for tracing wood origins can be a powerful tool to aid law enforcement in fighting illegal timber trade.
KW - Multi-elemental traits
KW - Timber trade
KW - Tree growth
KW - Wood physical traits
KW - Wood provenance
UR - https://www.scopus.com/pages/publications/105018851743
U2 - 10.1016/j.scitotenv.2025.180710
DO - 10.1016/j.scitotenv.2025.180710
M3 - Original Article
AN - SCOPUS:105018851743
SN - 0048-9697
VL - 1003
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 180710
ER -