Translating the ever-increasing wealth of information on microbiomes (environment, host or built environment) to advance our understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes that they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here, we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system-level processes that they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted using constituent taxa (community-aggregated traits) from those properties that cannot currently be predicted using constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these three categories of microbial characteristics and connect them with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.
Bibliographical noteFunding Information:
This work is a product of the Next Generation of Ecosystem Indicators Working Group, supported by the USGS John Wesley Powell Center for Synthesis and Analysis. Development of this manuscript was supported by NSF DEB IOS #1456959, awarded to E.K.H., and was prepared in part by E.K.H. as writer in residence at the Wolverine Farm Publick House and Press. C. Pepe-Ranney and A. Peralta provided valuable feedback on previous versions of this manuscript. This paper is dedicated to D. Nemergut, an integral part of our working group who passed away during the preparation of this manuscript.
© 2018, The Author(s).