Reliable biomass estimates require the inclusion of predictors that express inherent variations in species architecture.
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.
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, email@example.com, 301-903-9237; Daniel Stover, SC-23.1, firstname.lastname@example.org, 301-903-0289
Robinson Negron-Juarez, email@example.com
Jeffrey Q. Chambers, firstname.lastname@example.org
This study was financed by the Brazilian Council for Scientific and Technological Development (CNPq) within the projects Piculus, INCT Madeiras da AmazÃ´nia, 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)
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
Aug 24, 2019
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]
Aug 08, 2019
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]
Aug 05, 2019
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]
Jul 29, 2019
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]
Jul 15, 2019
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]
List all highlights (possible long download time)