Articles | Volume 13, issue 5
https://doi.org/10.5194/bg-13-1553-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-13-1553-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Mar 2016
Research article |
| 11 Mar 2016
Predicting biomass of hyperdiverse and structurally complex central Amazonian forests – a virtual approach using extensive field data
Daniel Magnabosco Marra
CORRESPONDING AUTHOR
AG Spezielle Botanik und Funktionelle Biodiversität,
Universität Leipzig, Germany
Biogeochemical Processes Department, Max Planck Institute
for Biogeochemistry, Jena, Germany
Laboratório de Manejo Florestal, Instituto Nacional
de Pesquisas da Amazônia, Manaus, Brazil
Niro Higuchi
Laboratório de Manejo Florestal, Instituto Nacional
de Pesquisas da Amazônia, Manaus, Brazil
Susan E. Trumbore
Biogeochemical Processes Department, Max Planck Institute
for Biogeochemistry, Jena, Germany
Gabriel H. P. M. Ribeiro
Laboratório de Manejo Florestal, Instituto Nacional
de Pesquisas da Amazônia, Manaus, Brazil
Joaquim dos Santos
Laboratório de Manejo Florestal, Instituto Nacional
de Pesquisas da Amazônia, Manaus, Brazil
Vilany M. C. Carneiro
Laboratório de Manejo Florestal, Instituto Nacional
de Pesquisas da Amazônia, Manaus, Brazil
Adriano J. N. Lima
Laboratório de Manejo Florestal, Instituto Nacional
de Pesquisas da Amazônia, Manaus, Brazil
Jeffrey Q. Chambers
Geography Department, University of California, Berkeley,
USA
Robinson I. Negrón-Juárez
Climate Sciences Department, Lawrence Berkeley National
Laboratory, Berkeley, USA
Frederic Holzwarth
AG Spezielle Botanik und Funktionelle Biodiversität,
Universität Leipzig, Germany
Björn Reu
AG Spezielle Botanik und Funktionelle Biodiversität,
Universität Leipzig, Germany
Escuela de Biología, Universidad Industrial de
Santander, Bucaramanga, Colombia
Christian Wirth
AG Spezielle Botanik und Funktionelle Biodiversität,
Universität Leipzig, Germany
German Centre for Integrative Biodiversity Research
(iDiv) Halle-Jena-Leipzig, Leipzig, Germany
Functional Biogeography Fellow Group,
Max Planck Institute for Biogeochemistry, Jena, Germany
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Short summary
Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests requires the inclusion of predictors that express inherent variations in species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases.
Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex...
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