Articles | Volume 13, issue 5
https://doi.org/10.5194/bg-13-1553-2016
https://doi.org/10.5194/bg-13-1553-2016
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, Niro Higuchi, Susan E. Trumbore, Gabriel H. P. M. Ribeiro, Joaquim dos Santos, Vilany M. C. Carneiro, Adriano J. N. Lima, Jeffrey Q. Chambers, Robinson I. Negrón-Juárez, Frederic Holzwarth, Björn Reu, and Christian Wirth

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Cited articles

Alvarez, E., Duque, A., Saldarriaga, J., Cabrera, K., de las Salas, G., del Valle, I., Lema, A., Moreno, F., Orrego, S., and Rodríguez, L.: Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia, Forest Ecol. Manag., 267, 297–308, 2012.
Amaral, D. D., Vieira, I. C. G., Almeida, S. S., Salomão, R. P., Silva, A. S. L., and Jardim, M. A. G.: Checklist of remnant forest fragments of the metropolitan area of Belém and historical value of the fragments, State of Pará, Brazil, Bol. Mus. Para. Emílio Goeldi, 4, 231–289, 2009.
Asner, G. P.: Geography of forest disturbance, P. Natl. Acad. Sci. USA, 110, 3711–3712, 2013.
Bolker, B. M.: Ecological Models and Data in R, Princeton University Press, New Jersey, 2008.
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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.
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