Integrating Theories, Measurements, and Models to Advance Biogeochemical Insights
Image courtesy of Nature Communications
Figure. Genome-to-ecosystem (G2E) conceptual framework integrating genomic information, site characterization, and a mechanistic ecosystem model (ecosys). Microbial metagenomes reconstructed from ecosystem samples are annotated and genomes are assigned to functional guilds using microTrait. Kinetic traits are then predicted using DEBmicroTrait and integrated with ecosys to simulate depth-resolved hydrological, thermal, and plant processes that are compared against field observations.
The Science
Scientists have developed a new way to predict how microbes affect the environment by analyzing their genetic information. This framework, called G2E, links microbial genomes to their ecosystem functions. By applying G2E at a thawing permafrost site, they found that variations in microbial traits as predicted from their genomes significantly affect methane emissions. They also discovered that using the relative abundance of microbes improves the ecosystem model predictions. The authors demonstrate how the G2E approach can improve predictions of ecosystem dynamics.
The Impact
This research provides a new tool for understanding and predicting how ecosystem processes vary over time. The G2E framework allows scientists to use the wealth of available microbial genetic data to make better predictions of ecosystem processes. This is important because microbes play a vital role in ecosystem processes, but their activity is rarely fully considered in models at ecosystem scale. By linking microbial traits and their relative abundance to ecosystem dynamics, this work has the potential to improve ecosystem models and is an important component of the new work in the Biology and Environmental Program Integration Center (BioEPIC) at Berkeley Lab.
Summary
Microbes drive the biogeochemical cycles of earth systems, yet the long-standing goal of linking genomes, microbial traits, mechanistic ecosystem models, and predictions has remained elusive despite a wealth of knowledge from emerging genomic information. Here we developed a general genome-to-ecosystem (G2E) framework for integrating genome-inferred microbial kinetic traits into mechanistic models of terrestrial ecosystems and applied it at a well-studied wetland by benchmarking predictions against observed ecosystem processes. We found variation in genome-inferred microbial kinetic traits resulted in large differences in simulated annual methane emissions, quantitatively demonstrating that the genomically observable variations in microbial capacity are consequential for ecosystem functioning. Applying microbial community-aggregated traits via genome relative-abundance weighting gave better emission predictions (i.e., up to 54% decrease in bias) compared to ignoring the observed abundances, highlighting the value of combined trait inferences and abundances. This work provides an example of integrating microbial functional trait-based genomics, mechanistic and pragmatic trait parameterizations of diverse microbial metabolisms, and mechanistic ecosystem modeling. The generalizable G2E framework will enable use of abundant microbial metagenomics data to improve predictions of microbial interactions in many complex systems and will be a key integration capability in BioEPIC collaborations.
Contact
William J. Riley
Lawrence Berkeley National Laboratory
Eoin L. Brodie
Lawrence Berkeley National Laboratory
Funding
This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070 (V.I.R., R.K.V., S.R.S., M.B.S., E.L.B., and the EMERGE Coordinators). Additional support for individual contributors included the following. Z.L. was additionally supported by Lawrence Livermore National Laboratory under the auspices of the U.S. Department of Energy under contract DE-AC52-07NA27344. W.J.R. was supported by the Belowground Biogeochemistry Scientific Focus Area and U.K. was supported by the Watershed Function Science Area, both funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. G.L.M. was supported by the LLNL “Microbes Persist” Soil Microbiome Scientific Focus Area SCW1632 and an associated KBase award SCW1746. N.J.B. was supported by the US Department of Energy, Office of Science (BER), Early Career Research Program (#FP00005182). B.J.W. was supported by an Australian Research Council Future Fellowship (#FT210100521). J.T. was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a US Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the US Department of Energy under Contract no. DE-AC02-05CH11231).
Publications
Li, Z., Riley, W. J., Marschmann, G. L., Karaoz, U., et al., A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry, Nature Communications, 16 (2025). https://doi.org/10.1038/s41467-025-57386-5.
https://eesa.lbl.gov/2025/03/07/microbes-matter-a-new-framework-to-represent-microbial-function-in-ecosystem-models/