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

Viewed

Total article views: 7,204 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,990 4,529 685 7,204 656 112 140
  • HTML: 1,990
  • PDF: 4,529
  • XML: 685
  • Total: 7,204
  • Supplement: 656
  • BibTeX: 112
  • EndNote: 140
Views and downloads (calculated since 18 Sep 2015)
Cumulative views and downloads (calculated since 18 Sep 2015)

Cited

Saved (preprint)

Latest update: 21 Nov 2024
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.
Altmetrics
Final-revised paper
Preprint