U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

Predicting Biomass Of Hyperdiverse And Structurally Complex Central Amazonian Forests
Published: March 11, 2016
Posted: April 27, 2016

Reliable biomass estimates require the inclusion of predictors that express inherent variations in species architecture.

The Science  
The hyperdiversity of tropical forests makes it difficult to predict their aboveground biomass levels based on biomass models that generalize across species. In a recent study, researchers employed a virtual forest approach using extensive field data to estimate biomass levels in the central Amazon.

The Impact
Due to the highly heterogenous nature of old-growth forests in structure and species composition, this study found that generic global or pantropical biomass estimation models can lead to strong biases.

Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change, and extreme weather events. Trees store circa 90% of the total aboveground biomass (AGB) in tropical forests, and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity, and inherent variations in tree allometry and architecture. The researchers parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. They used 727 trees from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this dataset, the researchers assembled six scenarios designed to span existing gradients in floristic composition and size distribution to select models that best predict AGB at the landscape level across successional gradients. They found that good individual tree model fits do not necessarily translate into reliable AGB predictions at the landscape level. 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. Reliable biomass assessments for the Amazon basin still depend on the collection of allometric data at the local and regional scales and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.

Contacts (BER PM)
Renu Joseph, SC-23.1, renu.joseph@science.doe.gov, 301-903-9237; Daniel Stover, SC-23.1, daniel.stover@science.doe.gov, 301-903-0289

PI Contacts
Robinson Negron-Juarez, robinson.inj@lbl.gov
Jeffrey Q. Chambers, jchambers@lbl.gov

This study was financed by the Brazilian Council for Scientific and Technological Development (CNPq) within the projects Piculus, INCT Madeiras da Amazônia, and Succession after Windthrows (SAWI) (Chamada Universal MCTI/No 14/2012, Proc. 473357/2012-7), and supported by the Max Planck Institute for Biogeochemistry within the Tree Assimilation and Carbon Allocation Physiology Experiment (TACAPE). Further support was provided by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231 as part of the Next-Generation Ecosystem Experiments–Tropics project and Regional and Global Climate Modeling program.

Magnabosco Marra, D., et al. “Predicting biomass of hyperdiverse and structurally complex central Amazonian forests: A virtual approach using extensive field data.” Biogeosciences 13, 1553–70 (2016). [DOI:10.5194/bg-13-1553-2016]. (Reference link)

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Terrestrial Ecosystem Science

Division: SC-23.1 Climate and Environmental Sciences Division, BER


BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)