The Science

Co-authors of a paper in Nature Microbiology, including Pacific Northwest National Laboratory’s Emily Graham, propose a new conceptual research framework that would harness the ever-increasing wealth of information on microbiomes. This framework, offered as a new approach to formalizing inquiries into microbiome science, proposes to empirically link three distinct categories of microbiome characteristics to each other and to the broader ecosystem processes they affect. As a result, the framework would reveal how—at the ecosystem level—microorganisms influence the ecological systems they inhabit.

The Impact

The new conceptual framework, informed by decades of research on environmental microbiomes and ecosystem processes, offers a promising pathway for discovering empirical linkages between the microorganisms in an ecosystem and the processes of that ecosystem. The framework would also help focus future research on potential microbiome-ecosystem links that are most likely to be detected empirically.

Summary

Identifying relationships between microbiomes and the ecosystem-level processes they influence is an exceptionally hard research challenge. This situation exists because of the absence of a robust conceptual research framework that would help elucidate underlying causal mechanisms and an explosion in the availability of data on microbiomes in the natural environment. Current research frameworks for understanding the microbial role in ecosystem function are often limited in their applications because they do not align with mechanistic representations of microbial processes in models of ecosystem function.

Presently, causal relationships are implied yet rarely tested, and researchers mostly rely on identifying correlations between microbes and ecosystem properties. Correlative approaches limit the potential to expand the influence of a single microbiome-ecosystem relationship to additional systems, and they do not yield any information on mechanisms that can be transferred across systems. As a result, current frameworks often yield ambiguous results that fail to provide new insights into processes and blur the mechanisms by which microbiomes relate to system-level functioning.

The authors propose a new framework that targets microbial characteristics known to contribute to system-level processes of interest. The framework, intended to link measurable microbiome characteristics with ecosystem-level processes, is constructed based on three distinct categories of microbiome characteristics: microbial processes, microbial community properties, and microbial membership.

From there, the authors show how researchers can use existing methods of investigating microbial ecology to elucidate properties within each of these categories and to connect these three categories of microbial characteristics with each other.

Central to the framework is one particularly important idea: distinguishing microbial community properties that can be predicted (called community aggregated traits) and those that researchers are currently unable to be predict (called emergent properties).

Collectively, the framework introduces a new research paradigm for closing the gaps between empirical investigations and the ecosystem process models they seek to inform.

Principal Investigator

Emily B. Graham
Pacific Northwest National Laboratory
emily.graham@pnl.gov

Program Manager

Paul Bayer
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
paul.bayer@science.doe.gov

Funding

This work is a product of the Next Generation of Ecosystem Indicators Working Group, supported by the U.S. Geological Survey John Wesley Powell Center for Synthesis and Analysis. Preparation of this manuscript was supported by National Science Foundation DEB IOS #1456959 awarded to EKH. In addition, this work was also supported by the Office of Biological and Environmental Research, within the U.S. Department of Energy, as part of the Subsurface Biogeochemical Research Scientific Focus Area at Pacific Northwest National Laboratory.

References