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Study Shows Coastal Wetlands Aid in Carbon Sequestration

data collection in marsh

PNNL scientist, Amy Borde collects data in a marsh on the Columbia River estuary.

Photo: Heida Diefenderfer

Sea-level rise impacts will likely decrease ecosystem carbon stocks

August 13, 2020
August 13, 2020
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Tidal marshes, seagrass beds, and tidal forests are exceptional at absorbing and storing carbon. They are referred to as total ecosystem carbon stocks, yet little data exists quantifying how much carbon is absorbed and stored by tidal wetlands in the Pacific Northwest (PNW). Knowing this information is valuable, particularly in the context of sea level rise and with the associated need for Earth system modeling to predict changes at the coast.

The Science

Researchers found that the average total ecosystem carbon stock in the PNW is higher than in other areas of the U.S. and other parts of the world. Marsh carbon stocks, in particular, are twice the global average. Researchers found progressive increases in total ecosystem carbon stocks along the elevation gradient of coastal wetland types common in the PNW: seagrass, low marshes, high marshes, and tidal forests. Total carbon also increased along the salinity gradient, with more carbon occurring in lower salinity areas.

Additionally, this research showed that common methods used to estimate soil carbon actually underestimate soil carbon stocks in coastal wetlands. Soil carbon storage below the depth of 100 centimeters proved to be an important carbon pool in PNW tidal wetlands.

The Impact

The results suggest that long-term sea-level rise impacts, such as tidal inundation and increased soil salinity, will likely decrease ecosystem carbon stocks. This is a concern if wetlands can’t migrate with increased sea level due to being bound by topography and human development.  

Summary

This research arose from the Pacific Northwest Blue Carbon Working Group, of which Amy Borde and Heida Diefenderfer of Pacific Northwest National Laboratory’s Coastal Sciences Division are members. The team studied 28 tidal ecosystems across the PNW coast, from Humboldt Bay, California, to Padilla Bay, Washington. They sampled common coastal wetland types that occur along broad gradients of elevation, salinity, and tidal influences, collecting the data necessary to calculate total carbon stocks in both above ground biomass and the soil profile.

In three years of study, the researchers found that most carbon is in the wetland soils not aboveground, and much of it is deeper than one meter—a typical lower limit of sampling. Total ecosystem carbon stocks progressively increased along the terrestrial-aquatic gradient of coastal wetland ecosystems common in the temperate zone including seagrass, low marshes, high marshes, and tidal forests. The findings were reported in “Total Ecosystem Carbon Stocks at the Marine-Terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, USA,” published in the August 2020 online edition of Global Change Biology (DOI: 10.1111/gcb.15248).

Research Team: PNNL’s Amy Borde and Heida Diefenderfer, along with J. Boone Kauffman, Leila Giovanonni, James Kelly, Nicholas Dunstan, and Christopher Janousek (Oregon State University); Craig Cornu and Laura Brophy (Institute for Applied Ecology/Estuary Technical Group); and Jude Apple (Padilla Bay National Estuarine Research Reserve).

Funding

The grant award was administered by the Institute of Applied Ecology, and other partners included Oregon State University and the Padilla Bay National Estuarine Research Reserve. This research was supported by the National Oceanic and Atmospheric Administration, through a cooperative agreement with the University of Michigan. 

10.1111/gcb.15248

Kauffman, J Boone, Leila Giovanonni, James Kelly, Nicholas Dunstan, Amy Borde, Heida Diefenderfer, Craig Cornu, Christopher Janousek, Jude Apple, and Laura Brophy. “Total Ecosystem Carbon Stocks at the Marine‐terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, United States.” Global change biology, no. 0 (August 11, 2020). DOI: 10.1111/GCB.15248

August 11, 2020

Integrative Network Modeling Reveals Key Drought-Associated Genes Key in the Soil Microbiome

Network Model

A new network analysis of microbial molecular responses to environmental stresses reveals connections between bacterial genes that respond to drought conditions (large red circles) and other genes and processes (smaller grey circles). Image from R.S. McClure, et al., “Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations.” Scientific Reports 10, 10882 (2020). [DOI: 10.1038/s41598-020-67878-7]

Molecular and gene networks were combined to better understand how soil microbial communities respond to changes in water levels and nutrient sources

July 24, 2020
July 24, 2020
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The Science

Harnessing the soil microbiome to enhance ecosystem services, like plant productivity or bioenergy production, requires understanding how soil microbiomes respond to environmental stresses, such as flood, drought, or changing nutrient levels. In this study, researchers examined how the soil microbiome responds at genetic and metabolic levels to changes in water content and nutrient sources. This integrated network analysis identified unique sets of genes and metabolic reactions that are expressed only under wet, dry, or high nutrient conditions. When focusing on these unique genes and pathways, the analysis showed that genes associated with dry soil conditions are central to the soil microbiome's response to environmental shifts.

The Impact

The soil microbiome promotes plant health and affects the cycling of carbon through the ecosystem. Here, researchers examined how many different genes and molecules expressed by soil microbes are related to each other and to certain environmental conditions. They were also able to identify how individual genes occupied key positions in a functional network. The researchers found that the soil microbiome particularly responded to dry conditions. This knowledge will help in future efforts to harness the soil microbiome for optimizing plant productivity under drought conditions. 

Summary

It is challenging to untangle the complex response of the soil microbial community to environmental change, partly due to the absence of modeling frameworks that can predict how environmental changes in soil can lead to changes in the microbial community’s function and role in promoting soil health. To fill this gap, researchers performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responds to changes in moisture and nutrient conditions. The integrated modeling approach revealed previously unknown, but critically important, aspects of the soil microbiomes’ response to environmental perturbations, including soil desiccation. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended treatments. A subsequent gene co-expression network analysis revealed that dry-associated genes, in particular, are central to the network; this means they are especially critical to the soil microbial community’s response to changing conditions. These results indicate the occurrence of a system-wide microbiome metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, this approach to analyzing large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which genomic and metabolite data are available.

PNNL Contacts

Janet K. Jansson, Pacific Northwest National Laboratory, janet.jansson@pnnl.gov

Kirsten Hofmockel, Pacific Northwest National Laboratory, kirsten.hofmockel@pnnl.gov

Funding

This research was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, as part of the Genomic Science Program, and is a contribution of the Pacific Northwest National Laboratory Soil Microbiome Scientific Focus Area "Phenotypic Response of the Soil Microbiome to Environmental Perturbations."

R.S. McClure, et al., “Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations.” Scientific Reports 10, 10882 (2020). [DOI: 10.1038/s41598-020-67878-7]

Revealing an Unexplored Mechanism for Microbial Metabolism in River Sediment

river bank on a sunny day

River corridors have major influences on the Earth system by transforming organic matter into substances that impact water quality, contaminants, and climate.

Image: Jackie Wells, PNNL

Laboratory experiment is the first to show organic matter thermodynamics govern aerobic respiration rates in ecosystems with low carbon to nutrient ratios

July 24, 2020
July 24, 2020
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The Science

River corridors have major influences on the Earth system by transforming organic matter into substances that impact water quality, contaminants, and climate. It has long been thought that the microbial metabolism underlying these transformations are controlled by temperature and the concentration of carbon-containing molecules. However, recent field experiments suggest thermodynamics, or the amount of chemical energy in the system available for organic matter decomposition, plays a key role in controlling microbial metabolism within river corridors, particularly in areas where groundwater and surface water mix. Now researchers have performed controlled laboratory experiments using river sediment to test organic matter thermodynamics as a mechanism of metabolic control in these environments. They find that organic matter thermodynamics control metabolism in oxygen rich environments in ways that depend on the concentration of nutrients and organic matter.

The Impact

This work challenges a long-held belief about processes that govern organic matter metabolism in freshwater ecosystems. It is the first study to provide direct evidence for thermodynamic regulation of organic matter metabolism under oxygen-rich conditions in a controlled laboratory setting. Improving representations of river corridors with refined mechanisms of nutrient processing could improve predictive models of local to regional to global biogeochemical cycling used to help manage ecosystems and predict changes to the integrated Earth system.

Summary

Researchers gathered sediment from the Columbia River in areas where groundwater and surface water mix. In the laboratory, they added four different organic compounds to the sediment at one of three different concentrations. Then the researchers measured the rate of metabolism and used mass spectrometry to characterize the organic molecules that remained after incubation using an ultrahigh resolution technique. Using the molecular formulas of the observed molecules, the researchers calculated the amount of energy required to oxidize these molecules as a way of capturing thermodynamic favorability for decomposition. They found that organic matter thermodynamics govern aerobic microbial metabolism when organic carbon is at low concentration. As the concentration of organic carbon increased, thermodynamic controls became less influential and nutrient availability became the key factor governing metabolic rates. Although this study is of a single ecosystem, it provides a proof-of-concept that can be applied to experiments in more diverse ecosystems. It also demonstrates that thermodynamic constraints, in addition to the kinetic constraints of temperature and substrate concentration, can govern aerobic metabolism. Finally, the work proposes a new conceptual model in which organic matter thermodynamic and nutrient limitations dually control aerobic metabolism. Understanding microbial metabolism at a finer resolution, as well as from a variety of mechanistic perspectives, can help improve models of local to regional to global biogeochemical cycling used to help manage ecosystems and predict changes to the integrated Earth system.

Contact

Emily Graham, Pacific Northwest National Laboratory, emily.graham@pnnl.gov

Funding

This research was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, as part of Subsurface Biogeochemical Research Program’s Scientific Focus Area at the Pacific Northwest National Laboratory. A portion of the research was performed at Environmental Molecular Science Laboratory User Facility.

V. A. Garayburu-Caruso, et al., “Carbon Limitation Leads to Thermodynamic Regulation of Aerobic Metabolism.” Environmental Science & Technology Letters 7, 517-524 (2020). [DOI: 10.1021/acs.estlett.0c00258]

Deep Learning to Predict Interspecies Spatial Interactions from Microbial Assembly Patterns

microbial deep learning

Principles of microbial interactions (left) are integrated with modeling to create simulated fluorescent microscopy images (right). These simulated images are used to train a neural network to predict microbial spatial interactions in novel microscopy images. Image from J.-Y. Lee, et al., "Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns." Computational and Structural Biotechnology Journal 18, 1259-1269 (2020). [DOI: 10.1016/j.csbj.2020.05.023]

Deep learning enables incorporating microscopic images as a new data source to predict microbial spatial interactions

July 23, 2020
July 23, 2020
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The Science

Interactions between different species in a microbial community govern how members self-assemble in specific spatial patterns. However, methods that use physical features of ecological assembly to predict microbial interactions do not exist. Now researchers trained deep neural networks to accurately predict microbial interactions captured by fluorescence microscopy. They trained the networks using data collected experimentally and simulated from modeling.

The Impact

This work shows how data-driven modeling can leverage visualization techniques to tackle key science questions in microbial ecology. The developed deep learning workflow can significantly improve understanding of how microorganisms colonize habitats and interact with each other in spatially varied environments such as soils.

Summary

Rapid advancement in experimental and instrumental technologies is enabling the generation of high-resolution and high-throughput microscopic images that reveal the spatial distribution of microorganisms. These spatial interactions are key to carrying out coordinated metabolic reactions within microbial communities, but the use of spatial patterns for predicting microbial interactions is currently lacking. Conventional population-based computational methods that use species abundance data as a primary input to predict interspecies interactions have yet to be extended to incorporate spatial organizations of microorganisms. To fill this gap, the research team proposed supervised deep learning as a new network inference tool.

Currently, developing deep neural networks directly from experimental microscopy image data is infeasible due to unknown input-output relationships and insufficient amounts of training data. The team overcame these limitations by using high-fidelity agent-based models to perform 5000 simulations of the growth of two interacting microorganisms. This generated usable image data to effectively train deep learning networks. The resulting neural networks accurately predicted microbial interactions and their spatial variations not only from in silico images, but also from actual microscopic images obtained through carefully co-designed experiments. Therefore, the combined use of the agent-based model, machine learning algorithms, and experiments successfully demonstrated how to infer microbial interactions from spatially distributed data. This combination of techniques is a useful tool to reveal key—but previously unknown—interaction mechanisms in complex microbial communities that have been underexplored to date.

PNNL Contacts

Janet K. Jansson, Pacific Northwest National Laboratory, janet.jansson@pnnl.gov

Kirsten Hofmockel, Pacific Northwest National Laboratory, kirsten.hofmockel@pnnl.gov

Funding

This research was supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research Program, and is a contribution of the Scientific Focus Area "Phenotypic response of the soil microbiome to environmental perturbations" at the Pacific Northwest National Laboratory. A portion of the research described in this paper was also performed at EMSL- the Environmental Molecular Sciences Laboratory.

J.-Y. Lee, et al., "Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns." Computational and Structural Biotechnology Journal 18, 1259-1269 (2020). [DOI: 10.1016/j.csbj.2020.05.023]

This work has been published in Computational and Structural Biotechnology Journal as an invited paper for a special issue organized by the Editor-in-Chief.

Deconstructing the Soil Microbiome

shovel in soil with tiny green plants around

Microbes in the soil play a major role in nutrient cycling and plant health, but the inherent complexity of the soil microbiome makes it challenging to effectively analyze microbial functions and relationships. 

Image courtesy of Lukas from Pexels

Deconstruction of soil microbial communities into discrete functional groups enables piecing together the functional potential of the complex soil microbiome

July 23, 2020
July 23, 2020
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The Science                                

The soil microbiome plays a major role in nutrient cycling and plant health. However, its inherent complexity, with a vast array of microbes that metabolize many different molecules, makes it challenging to effectively analyze ecosystem functions performed by interacting members of soil microbial communities. Researchers dissected the complex microbial community of a native Washington soil into reproducible, low-complexity communities called 'functional modules.' Because these subcommunities are easier to study than a bulk community, researchers could analyze microbial species and functions present in the soil in more depth than before.

The Impact

By studying discrete functional components of the soil microbiome at high resolution, the researchers obtained a more complete picture of soil diversity compared to analysis of the entire soil community. They identified specific evolutionary relationships and biochemical characteristics of the soil microbiome that otherwise would have been hidden in previous community-scale genomic analyses. Improved understanding of the functions of the soil microbiome could help scientists harness beneficial aspects of the soil microbiome to increase soil health or crop productivity.  

Summary

One gram of soil contains microbes from thousands of different evolutionary groups. These microbes also have a wide variety of metabolic functions that help them survive in different soil microenvironments. Analyzing the complete functional and taxonomic diversity of a soil microbiome requires a large amount of computing power, and it may fail to capture large populations of quiet or rare microbes.

To simplify the analysis of a soil microbial community, researchers incubated a parent soil microbiome under several different conditions to create different subcommunities of microbes with specific functions, or functional modules. The functional modules included: usage of simple and complex carbon substrates, antibiotic resistance, anaerobic growth with different redox acceptors, and stress resistance. For each functional module, the researchers performed 16S rRNA gene amplicon sequencing to determine the community composition and RNA sequencing to identify expressed functions. Approximately 27% of unique taxa present in the parent soil were found in the functional modules, in addition to 341 taxa not detected in the parent community. The functional modules had unique gene expression patterns that were also enriched for transcripts associated with functional characteristic of each module. By dissecting the soil microbiome into discrete components, the researchers obtained a more comprehensive and highly detailed view of a soil microbiome and its biochemical potential than through analysis of a soil microbiome as a whole.

Contact

Ryan McClure, Pacific Northwest National Laboratory, ryan.mcclure@pnnl.gov

Funding

This research was supported by the U.S. Department of Energy’s Office of Science, Biological and Environmental Research Program and is a contribution of the Scientific Focus Area “Phenotypic response of the soil microbiome to environmental perturbations.”

D. Naylor, et al., “Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules.” mBio 11, e01349-20 (2020). [DOI: 10.1128/mBio.01349-20]

Digging into the Details of Phosphorus Availability

Photo of plant with roots under ground

Courtesy of Shutterstock

New root blotting technique visualizes relationship between root growth, microbial activity, and soil nutrients.

July 7, 2020
July 7, 2020
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The Science

Phosphorous is an important nutrient for plants. However, the mechanisms used by plants to extract phosphorus from soil and incorporate it into their biomass are not well understood. Now, researchers developed a new technique to visualize the activity and distribution of enzymes that mobilize phosphate around plant roots. Tracking the location of these enzymes can help researchers better understand the chemical dynamics between roots, microbes, and soil that influence how plants get nutrients. The approach could also be applied to other nutrient-cycling enzymes.

Diagram showing rhizosphere blotting nondestructive process
A new root blotting technique produces an imprint of plant roots growing in flat slabs. The paper imprints can then be probed with different fluorescent indicators to visualize both the distribution and activity of phosphate-mobilizing enzymes surrounding the roots.

The Impact

Phosphorus is an essential nutrient for plants and therefore, global demand for phosphorus fertilizers is expected to grow to accommodate the world’s growing population. However, most of these fertilizers are made from rock phosphorus, a non-renewable resource. This research provides new insights into the complex dynamics of phosphorous exchange between soil, microbes, and plant roots. Knowledge from this newly developed approach will help scientists identify strategies to improve phosphorus use efficiency for bioenergy crop production in marginal environments, as well as for agriculture in general.

Summary

Soil bacteria, fungi, and plants produce enzymes called phosphatases, which convert organic sources of phosphorus into a form that plants can absorb. Researchers have studied the microbial activity in bulk soil samples, providing information about the overall functional potential of the environment. But to better understand the dynamics between soil, plants, and microbes, more detail is needed. To accomplish that, a team of researchers developed a new technique based on root blotting to reveal phosphatase activity and distribution around plant roots. They grew switchgrass in flat pots or “rhizoboxes” containing soil with pellets of root matter as sources of organic phosphorus. Then, they applied a nitrocellulose membrane to capture proteins around the roots. Finally, the researchers stained the membrane with fluorescent indicators for phosphatase activity and protein concentration. This revealed the spatial distribution of phosphatase around the roots of plants, and highlighted regions of increased phosphatase activity.

This approach could be used to study phosphatase activity over time, as well as other nutrient-cycling enzymes. The combination of membrane extraction, with rapid analysis via fluorescent probes to reveal localization of phosphatase activity in the rhizosphere, offers a new technique for environmental applications. Expanding this approach could enable simultaneous visualization of multiple enzyme types in soil systems.

Funding

Development of this method was funded by DOE’s Office of Science, Biological and Environmental Research Program by the Early Career Research Award program (PI: Jim Moran).

10.1016/j.soilbio.2020.107820

V.S. Lin, et al. “Non-destructive spatial analysis of phosphatase activity and total protein distribution in the rhizosphere using a root blotting method.” Soil Biology and Biochemistry, 146 (2020). DOI: 10.1016/j.soilbio.2020.107820

Predicting Soil CO2 Emissions from Air Temperature

graph with multicolored dots

The mean annual air temperature and precipitation coverage of soil respiration samples used in this study, by ecosystem type. The gray dotes represent worldwide data points.

A cheaper, more efficient way to estimate soil respiration and carbon flux

June 2, 2020
June 2, 2020
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The Science

Soil respiration—the flow of CO2 from the soil surface to the atmosphere—is one of the largest carbon fluxes in the terrestrial biosphere. In recent DOE-funded study, researchers created a model that predicted annual soil respiration in different parts of the world based on average air temperature for each region.

The Impact

Monitoring greenhouse gas exchange between the soil and the atmosphere is important in tracking worldwide CO2 emissions. Despite this, many regions are either inaccessible or do not have the resources to undertake rigorous research to monitor soil respiration. In this study, researchers found that soil respiration measured at annual mean temperature can be used to predict annual soil respiration. The findings could be used to reduce soil respiration measurement frequency and greatly decrease cost-- enabling easier measurements in low income and inaccessible regions worldwide.

Summary

Led by Jinshi Jian of Pacific Northwest National Laboratory, this internationally diverse research collaboration used data from more than 800 site-year observations worldwide. The team developed a predictive model to test the relationship between annual soil respiration and instant soil respiration rate at mean annual temperature among diverse ecosystems and climates throughout the world. Air temperature data is more common than soil temperature data, making it a more achievable measurement to gauge carbon emissions in lower income countries. Their results were recently published in Agricultural and Forest Meteorology.

PNNL Contact

Jinshi Jian, Pacific Northwest National Laboratory, jinshi.jian@pnnl.gov

Funding

This research was supported by the DOE Office of Biological and Environmental Research (BER), as part of BER’s Terrestrial Ecosystem Science Program [number: DE-AC05-76RL01830].

 

Jian, J., Bahn, M., Wang, C., Bailey, V. L., Bond-Lamberty, B. Prediction of annual soil respiration from its flux at mean annual temperature. Agricultural and Forest Meteorology. Volume 287. DOI: 10.1016/j.agrformet.2020.107961

MAY 20, 2020
Web Feature

PNNL, OSU Superfund Collaboration Continues

A long-standing collaboration between PNNL and Oregon State University to study harmful chemicals at federally designated hazardous waste sites primarily across the Pacific Northwest has been awarded a five-year, $12.7 million grant.
MAY 15, 2020
Web Feature

Staying Ahead of Antibiotic Resistance

The recent coronavirus pandemic shows just how quickly a deadly pathogen can sweep across the globe, killing tens of thousands in the U.S. and disrupting daily life for millions more in the span of a few months.

Machine Learning Produces Unprecedented High-Resolution Map of Global Soil Respiration

forest floor and tree trunks

Soil respiration is one of the largest fluxes in the global carbon cycle, providing critical insights into biological activity in the underlying soil. Photo by Carl Newton on Unsplash

Research provides a new understanding of the magnitude and uncertainties surrounding this major global flow of carbon to the atmosphere.

April 27, 2020
April 27, 2020
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The Science

Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory have developed and continue to maintain a global database of measurements made of soil-to-atmosphere CO2 flows, termed soil respiration. A research team at the University of Delaware has leveraged these observations in a machine-learning approach to create a new high-resolution global map of soil respiration and its uncertainties.

The Impact

Soil respiration is one of the largest fluxes in the global carbon cycle, providing critical insights into biological activity in the underlying soil. This new global map of soil respiration and its uncertainties provides modelers and experimentalists with a “gold standard” benchmark dataset identifying areas with the highest uncertainties to target in the future.

Summary

Soils emit large amounts of carbon dioxide to the atmosphere every year via the process of soil respiration. Rates of soil respiration are highly variable in space, however, limiting scientists’ ability to balance global carbon budgets and forecast climate change. This study used a novel machine learning approach to predict soil respiration rates at high resolution (1 km2) globally, based on how observations of soil respiration were related to climate (annual temperature, annual and seasonal precipitation) and vegetation. It also examined the spatial patterns of the associated uncertainty of these predictions. Predicted annual soil respiration and prediction uncertainty varied across ecosystem types and regions, with evergreen tropical forests dominating global annual soil respiration emissions. Dryland, wetland, and cold ecosystems had the highest associated prediction uncertainties, suggesting that future soil respiration measurements would be especially useful in these areas. The high spatial resolution of these predictions will help researchers studying the carbon cycle at local to global scales and provide a high-quality benchmark dataset for Earth System Models.

Contact

Ben Bond-Lamberty, Pacific Northwest National Lab, bondlamberty@pnnl.gov  

Funding

Rodrigo Vargas acknowledges support from NASA’s Carbon Monitoring Systems (80NSSC18K0173). Ben Bond-Lamberty was supported by the US Department of Energy, Office of Science, Biological and Environmental Research as part of the Terrestrial Ecosystem Sciences Program.

Research topics

D. Warner, B. Bond-Lamberty, J. Jian, E. Stell, and R. Vargas “Spatial patterns of global soil respiration at 1 km resolution.” Global Biogeochemical Cycles 33, 1733-1745 (2019). [DOI: 10.1029/2019GB006264]

January 10, 2020