Microorganisms have a variety of evolutionary adaptations and physiological acclimation mechanisms that allow them to survive and remain active in the face of environmental stress. Physiological resp...

A major thrust of terrestrial microbial ecology is focused on understanding when and how the composition of the microbial community affects the functioning of biogeochemical processes at the ecosystem scale (meters-to-kilometers and days-to-years). While research has demonstrated these linkages for physiologically and phylogenetically “narrow” processes such as trace gas emissions and nitrification, there is less conclusive evidence that microbial community composition influences the “broad” processes of decomposition and organic matter (OM) turnover in soil. In this paper, we consider how soil microbial community structure influences C cycling. We consider the phylogenetic level at which microbes form meaningful guilds, based on overall life history strategies, and suggest that these are associated with deep evolutionary divergences, while much of the species-level diversity probably reflects functional redundancy. We then consider under what conditions it is possible for differences among microbes to affect process dynamics, and argue that while microbial community structure may be important in the rate of OM breakdown in the rhizosphere and in detritus, it is likely not important in the mineral soil. In mineral soil, physical access to occluded or sorbed substrates is the rate-limiting process. Microbial community influences on OM turnover in mineral soils are based on how organisms allocate the C they take up – not only do the fates of the molecules differ, but they can affect the soil system differently as well. For example, extracellular enzymes and extracellular polysaccharides can be key controls on soil structure and function. How microbes allocate C may also be particularly important for understanding the long-term fate of C in soil – is it sequestered or not?

Despite decades of research progress, ecologists are still debating which pools and fluxes provide nitrogen (N) to plants and soil microbes across different ecosystems. Depolymerization of soil organic N is recognized as the rate-limiting step in the production of bioavailable N, and it is generally assumed that detrital N is the main source. However, in many mineral soils, detrital polymers constitute a minor fraction of total soil organic N. The majority of organic N is associated with clay-sized particles where physicochemical interactions may limit the accessibility of N-containing compounds. Although mineral-associated organic matter (MAOM) has historically been considered a critical, but relatively passive, reservoir of soil N, a growing body of research now points to the dynamic nature of mineral-organic associations and their potential for destabilization. Here we synthesize evidence from biogeoscience and soil ecology to demonstrate how MAOM is an important, yet overlooked, mediator of bioavailable N, especially in the rhizosphere. We highlight several biochemical strategies that enable plants and microbes to disrupt mineral-organic interactions and access MAOM. In particular, root-deposited low-molecular-weight exudates may enhance the mobilization and solubilization of MAOM, increasing its bioavailability. However, the competitive balance between the possible fates of N monomers—bound to mineral surfaces versus dissolved and available for assimilation—will depend on the specific interaction between mineral properties, soil solution, mineral-bound organic matter, and microbes. Building off our emerging understanding of MAOM as a source of bioavailable N, we propose a revision of the Schimel and Bennett (Ecology 85:591–602, 2004 ) model (which emphasizes N depolymerization), by incorporating MAOM as a potential proximal mediator of bioavailable N.

Soil organic nitrogen (N) is a critical resource for plants and microbes, but the processes that govern its cycle are not well-described. To promote a holistic understanding of soil N dynamics, we need an integrated model that links soil organic matter (SOM) cycling to bioavailable N in both unmanaged and managed landscapes, including agroecosystems. We present a framework that unifies recent conceptual advances in our understanding of three critical steps in bioavailable N cycling: organic N (ON) depolymerization and solubilization; bioavailable N sorption and desorption on mineral surfaces; and microbial ON turnover including assimilation, mineralization, and the recycling of microbial products. Consideration of the balance between these processes provides insight into the sources, sinks, and flux rates of bioavailable N. By accounting for interactions among the biological, physical, and chemical controls over ON and its availability to plants and microbes, our conceptual model unifies complex mechanisms of ON transformation in a concrete conceptual framework that is amenable to experimental testing and translates into ideas for new management practices. This framework will allow researchers and practitioners to use common measurements of particulate organic matter (POM) and mineral-associated organic matter (MAOM) to design strategic organic N-cycle interventions that optimize ecosystem productivity and minimize environmental N loss.

Numerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions.