Soils worldwide contain 2,300 Pg of carbon (C) which accounts for nearly 80% of the C present in the terrestrial biosphere \citep{Amundson_2001,BATJES_1996}. Soil microorganisms drive C flux through the terrestrial biosphere and C respiration by soil microorganisms produces annually tenfold more CO\(_{2}\) than fossil fuel emissions \citep{chapin2002principles}. Despite the contribution of microorganisms to global C flux, many global C models ignore the diversity of microbial physiology \citep{Allison2010,Six2006,Treseder2011} and we still know little about the ecophysiology of soil microorganisms. Characterizing the ecophysiology of microbes that mediate C decomposition in soil has proven difficult due to their overwhelming diversity. Such knowledge should assist the development and refinement of global C models \citep{Bradford2008,Neff_2001,McGuire2010,Wieder2013}.
Though microorganisms mediate 80-90% of the soil C-cycle \citep{ColemanCrossley_1996,Nannipieri_2003}, and microbial community composition can account for significant variation in C mineralization \citep{Strickland_2009}, terrestrial C-cycle models rarely consider the community composition of soils \citep{Zak2006,Reed2007}. Variation in microbial community composition can be linked effectively to rates of soil processes when diagnostic genes for specific functions are available (e.g. nitrogen fixation \citep{Hsu2009}). However, the lack of diagnostic genes for describing soil-C transformations has limited progress in characterizing the contributions of individual microorganisms to decomposition. Remarkably, we still lack basic information on the physiology and ecology of the majority of organisms that live in soils. For example, contributions to soil processes remain uncharacterized for cosmopolitan bacterial phyla in soil such as Acidobacteria, Chloroflexi, Planctomycetes, and Verrucomicrobia. These phyla combined can comprise 32% of soil microbial communities (based on surveys of the SSU rRNA genes in soil) \citep{Janssen2006,Buckley2002}.
To predict whether and how biogeochemical processes vary in response to microbial community structure, it is necessary to characterize functional niches within soil communities. Functional niches defined on the basis of microbial physiological characteristics have been successfully incorporated into biogeochemical process models (E.g. \citep{Wieder2013, Kaiser2014a}). In some C-cycle models physiological parameters such as growth rate and substrate specificity are used to define functional niche behavior \citep{Wieder2013}. However, it is challenging to establish the phylogenetic breadth of functional traits. Functional traits are often inferred from the distribution of diagnostic genes across genomes \citep{Berlemont2013} or from the physiology of isolates cultured on laboratory media \citep{Martiny2013}. For instance, the wide distribution of the glycolysis operon in microbial genomes is interpreted as evidence that many soil microorganisms participate in glucose turnover \citep{McGuire2010}. However, the functional niche may depend less on the distribution of diagnostic genes across genomes and more on life history traits that allow organisms to compete for a given substrate as it occurs in the environment. For instance, rapid resuscitation and fast growth are traits that may allow microorganisms to compete effectively for glucose in environments that exhibit high temporal variability. Alternatively, metabolic efficiency and slow growth rates may be traits that allow microbes to compete effectively for glucose in environments characterized by low temporal variability in glucose supply. These different competitive strategies would not be apparent from genome analysis, or when strains are grown in isolation. Hence, life history traits, rather than genomic capacity for a given pathway, are likely to constrain the diversity of microbes that metabolize a given C source in the soil under a given set of conditions. Therefore, to generate an understanding of functional niche as it relates to biogeochemical processes in soils it is important to characterize microbial functional traits as they occur in situ or in microcosm experiments.
Nucleic acid stable-isotope probing (SIP) links genetic identity and activity without the need diagnostic genetic markers or cultivation and has expanded our knowledge of microbial processes \citep{Chen_Murrell_2010}. Nucleic acid SIP has notable complications, however, including the need to add large amounts of labeled substrate \citep{radajewski2000stable}, label dilution resulting in partial labeling of nucleic acids \citep{radajewski2000stable}, the potential for cross-feeding and secondary label incorporation \citep{DeRito2005}, and variation in genome G\(+\)C content \citep{Buckley_2007}. As a result, most applications of SIP have targeted specialized microorganisms (for instance, methylotrophs \citep{lueders2004b}, syntrophs \citep{lueders2004}, or microorganisms that target pollutants \citep{derito2005}). Exploring the soil-C cycle with SIP has proven to be more challenging because SIP has lacked the resolution necessary to characterize the specific contributions of individual microbial groups to the decomposition of plant biomass. High throughput DNA sequencing technology, however, improves the resolving power of SIP \citep{Aoyagi2015}. It is now possible to use far less isotopically labeled substrate resulting in more environmentally realistic experimental conditions. It is also possible to sequence rRNA genes from numerous density gradient fractions across multiple samples thereby increasing the resolution of a typical nucleic acid SIP experiment \citep{Verastegui_2014}. With this improved resolution the activity of more soil microorganisms can be assessed. Further, since microbial activities can be more comprehensively assessed, we can begin to determine the ecological properties of functional groups defined by a specific activity in a DNA-SIP experiment. We have employed such a high resolution DNA stable isotope probing approach to explore the assimilation of both xylose and cellulose into bacterial DNA in an agricultural soil.
We added to soil a complex amendment representative of organic matter derived from fresh plant biomass. All treatments received the same amendment but the identity of isotopically labeled substrates was varied between treatments. Specifically, we set up a control treatment where all components were unlabeled, a treatment with \(^{13}\)C-xylose instead of unlabeled xylose, and a treatment with \(^{13}\)C-cellulose instead of unlabeled cellulose. Soil was sampled at days 1, 3, 7, 14, and 30 and we identified microorganisms that assimilated \(^{13}\)C into DNA at each point in time. We designed the experiment to test of the degradative succession hypothesis as it applies to soil bacteria, to identify soil bacteria that metabolize xylose and cellulose, and to characterize temporal dynamics of xylose and cellulose metabolism in soil.