Dr. Melanie Mayes was one of four Oak Ridge National Laboratory (ORNL) researchers to receive a 2016 US Department of Energy (DOE) Office of Science (SC) Early Career Research Program research grant. The program, now in its seventh year, is designed to bolster the nation’s scientific workforce by providing support to exceptional researchers during the crucial early career years, when many scientists do their most formative work.

Dr. Mayes’ proposal, “A Comprehensive Framework for Modeling Emissions from Tropical Soils and Wetlands,” selected for funding by the DOE Office of Biological and Environmental Research (BER), has as its goal development of a framework for modeling greenhouse gas (GHG) emissions in tropical regions that takes into account factors such as microbial traits and functions, soil characteristics, different microbial energy sources, and soil moisture. Tropical wetlands are important contributors to GHG emissions worldwide; however, tropical biomes are extremely complex, and many of the factors contributing to GHG emissions are poorly understood and thus poorly represented in models.

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Study shows that reliable biomass estimates must include predictors that express inherent variations in tree species architecture.

The Science  
The hyper-diversity 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.

Summary
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.

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

Publication
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)

 

Reported by: http://www.saleskalab.org/saleska-lab-gets-the-cover-of-science-with-paper-led-by-jin-wu/

Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests.  The analyses, conducted by Scott Saleska’s Lab at the University of Arizona, are reported in the February 26, 2016 issue of Science in a paper led by recent Saleska Lab graduate, Dr. Jin Wu, current graduate student Loren Albert, and INPA (Manaus) student Aline Lopes (advised by our INPA collaborator Bruce Nelson).

They used special tower-mounted cameras to discover that synchronization of the birth and death of leaves in evergreen forest trees across broad areas of the Brazilian Amazon is the cause of strong seasonal increases and decreases in overall tropical forest photosynthesis. These findings about how forests regulate their seasonal “breathing in” of atmospheric carbon dioxide will help scientists better understand how climate influences these forests and more accurately predict how these forests will respond to future climate change.

Their team included 5 other current and former Saleska Lab members — Natalia Restrepo-Coupe, Kenia Wiedemann (now a post-doc at Harvard), Scott Stark (faculty at MSU), Brad Christoffersen (post-doc at Los Alamos lab), and Neill Prohaska (Ph.D. student) — and an international collaborators from Brazil led by Rodrigo da Silva (UFOPa, Santarem) and Paulo Brando (IPAM).

Find the publication here: http://science.sciencemag.org/content/351/6276/972

Dr. L. Ruby Leung, an internationally renowned atmospheric scientist specializing in climate modeling and the water cycle at Pacific Northwest National Laboratory, has been selected by the U.S. Department of Energy to lead the Accelerated Climate Modeling for Energy (ACME) project as Chief Climate Scientist. In this role, Leung will help guide the science behind one of DOE’s most important areas of research: transforming the Nation’s ability to predict climate change and its impacts.  Dr. Leung also leads NGEE-Tropics’ hydrological research to advance understanding and model representation of tropical forest surface and subsurface water and its availability to plants.

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Robinson Negrón-Juárez, Charles Koven, William Riley, Ryan Knox, and Jeff Chambers, researchers in EESA and CESD, published a letter in Environmental Research Letters showing that most earth system models (ESM) overpredict tropical forest biomass in response to increased forest productivity. In contrast, observations show that as tropical forest productivity increases, trees do not continue to store CO₂ at the same rate, and biomass saturates. This bias may lead to an overprediction of carbon uptake in response to climate change. Negrón-Juárez et al. explain that observations of how plants allocate the carbon derived from photosynthesis into leaves, wood and roots are useful to assess model performance. Including these allocation patterns and turnover times into ESMs will improve understanding of how quickly the climate system will warm over the coming decades.

Their paper was highlighted by Environmental Research Web this September.

Citation: Observed allocations of productivity and biomass, and turnover times in tropical forests are not accurately represented in CMIP5 Earth system models. Robinson I Negrón-Juárez, Charles D Koven, William J Riley, Ryan G Knox and Jeffrey Q Chambers. Open Access: Environmental Research Letters 10 (2015) 064017; doi:10.1088/1748-9326/10/6/064017. http://dx.doi.org/10.1088/1748-9326/10/6/064017