Terrestrial Microbial Carbon Cycling

Science Focus Area: Los Alamos National Laboratory

  • Principal Investigators: John Dunbar1(Lead PI) and Michaeline Albright1 (Co-Lead)
  • Lead Institution: 1Los Alamos National Laboratory
  • LANL Research Team: Sanna Sevanto (Technical Co-PI), La Verne Gallegos-Graves (chief technician, environmental microbiology), Marie Kroeger (postdoc, bacterial ecology), Joany Babilonia (postdoc, bacterial ecology), Rae Devan (postdoc, fungal ecology), Miriam Hutchinson (postdoc, fungal ecology), Jack Heneghan (post-bac technician), Dennis Suazo (post-bac technician), Kyana Montoya (post-bac technician)
  • External Collaborators: Johannes Lehmann2, Dominic Woolf2 (staff scientist), Dan Buckley2, Cassie Wattenberger2 (postdoc), Francesca Cotrufo3, Brian Munsky3, Jaron Thompson3 (MS student), Huy Vo3 (postdoc), Vanessa Bailey4, Tayte Campbell4 (postdoc)
  • External Institutions: 2Cornell University, 3Colorado State University, 4Pacific Northwest National Laboratory
  • Project Website: https://www.lanl.gov/science-innovation/science-programs/office-of-science-programs/biological-environmental-research/sfa-microbial-carbon.php


microbial variation diagram

Microbial Variation. Understanding variation in soil microbes can improve carbon management and modeling. [Courtesy LANL]

This Science Focus Area (SFA) aims to inform climate modeling and enable carbon management in terrestrial ecosystems. To achieve these aims, our program develops and uses community genomics approaches to discover widespread biological processes that control carbon storage and release in temperate biome soils. We are building on our model-community approach to discover traits that drive carbon cycling variation. Our proposed work follows a progression in system complexity that will lead in a later phase to application of validated, trait-based models in field studies. We use metagenomic, metatranscriptomic, stable-isotope probing, chemical profiling, and machine learning approaches to understand how model communities with substantial differences in carbon flow interact with environmental factors to control ecosystem carbon cycling under N deposition. This SFA merges DOE strengths in microbial genomics, computation, user-facility capabilities, and ecosystem science.

Research Objectives

Our research objectives address BER grand challenges to discover widespread microbiological processes that influence ecosystem C cycling under altered environmental regimes (Grand Challenge 4.1) and to define the associated traits that ‘predict larger-scale ecosystem phenomena for an Earth system understanding’ (Grand Challenges 4.2 and 4.3). The central concept of our proposed work is that microbial community variation creates a distribution of possible outcomes for every component of the C cycle in soil. Identifying the community features that create substantial variation in C cycling provides the foundational knowledge to define essential mechanisms (ecological traits) to improve modeling and management of soil C in natural ecosystems. Essential components of C-cycling subject to microbial modification include:

  • Transformation of plant particulate organic matter (POM) at the soil surface & in the subsurface into dissolved organic carbon (DOC) stabilized by the soil matrix
  • Transformation of plant DOC into other products stabilized by the soil matrix
  • Transformation of microbial particulate organic matter (POM) or DOC into products stabilized by the soil matrix
  • Destabilization and transformation of mineral-bound SOM or SOM within aggregates
  • Modulation of plant primary production and spatial allocation of C to soil

The objectives below aim to characterize the breadth of community-driven variation that is possible for C-cycle components, exploit the observed distributions to discover the mechanistic processes (ecological traits) that drive the functional extremes, understand their relevance over climate gradients, and determine the consequences of interacting functional extremes over space and time.

Objective 1: Predictive links between microbial traits and C flow. In the prior phase of the SFA, we performed microcosm studies to discover microbial functional guilds driving DOC abundance in the early phase of decomposition of surface plant litter (pine, oak, and grass) in microcosms. Further work is needed to elevate the prior findings to the level of robust predictive links between traits and ecosystem phenomena. The next logical steps are to extend our findings to natural ecosystems and to determine the general mechanism that underpins control of DOC abundance by the functional guilds we identified. We will also expand the temporal and spatial scope by characterizing functional guilds controlling C fate during late stages of litter decomposition and C fate in subsurface soil. The tasks involve manipulative experiments with soil-core microcosms, 13C-labeled grass litter, and examination of traits among different types of plant litter.

Objective 2: Interaction of microbial effect traits and environmental fluctuation. In the prior phase, we found large (up to 7-fold) variation in community-driven C flow under constant environmental conditions in microcosms, but in nature, conditions fluctuate. This objective will assess whether environmental fluctuation is likely to amplify or dampen the variation in C flow caused by microbial functional guilds. These studies will provide foundational insights into potential impacts of changing climate on microbial-driven feedbacks. This objective will involve manipulative experiments with soil-core microcosms and 13C-labeled grass litter.

Objective 3: Ecosystem-level consequences of microbial-driven variation in C flow. This objective will quantify the ecosystem-level consequences of community-driven extremes in C flow. Do functional extremes in one component of C-cycling (e.g. surface litter decomposition) create cascading effects on other components (e.g. subsurface SOM formation) that modify the net behavior of the ecosystem? Or are functional extremes severely dampened by compensatory behavior in other C-cycle components? Answering such questions will provide foundational insights that inform management strategies and C-cycle process modeling. This objective will exploit two model microbial communities from the prior phase that cause divergent C-flow patterns with surface litter, mini-ecosystems (soil-cores with blue grama), stable isotope tracing, an accelerated 5-yr seasonal cycle, and data synthesis with an existing C-cycle process model (SOMIC 1.0).