Explicitly representing soil microbial processes in Earth system models
National Center for Atmospheric Research1, University of California, Irvine2, University of Maryland Center for Environmental Science3, Lawrence Berkeley National Laboratory4, University of California, Berkeley5, Natural Resources Canada6, Purdue University7, California Institute of Technology8, University of Oklahoma9, Microsoft10, Indiana University11, Pacific Northwest National Laboratory12, Commonwealth Scientific and Industrial Research Organisation13, East China Normal University14, University of Texas at El Paso15
TL;DR: In this article, the authors present a roadmap for how to begin building, applying, and evaluating reliable microbial-explicit model formulations that can be applied in Earth system models (ESMs).
Abstract: Microbes influence soil organic matter decomposition and the long-term stabilization of carbon (C) in soils. We contend that by revising the representation of microbial processes and their interactions with the physicochemical soil environment, Earth system models (ESMs) will make more realistic global C cycle projections. Explicit representation of microbial processes presents considerable challenges due to the scale at which these processes occur. Thus, applying microbial theory in ESMs requires a framework to link micro-scale process-level understanding and measurements to macro-scale models used to make decadal- to century-long projections. Here we review the diversity, advantages, and pitfalls of simulating soil biogeochemical cycles using microbial-explicit modeling approaches. We present a roadmap for how to begin building, applying, and evaluating reliable microbial-explicit model formulations that can be applied in ESMs. Drawing from experience with traditional decomposition models, we suggest the following: (1) guidelines for common model parameters and output that can facilitate future model intercomparisons; (2) development of benchmarking and model-data integration frameworks that can be used to effectively guide, inform, and evaluate model parameterizations with data from well-curated repositories; and (3) the application of scaling methods to integrate microbial-explicit soil biogeochemistry modules within ESMs. With contributions across scientific disciplines, we feel this roadmap can advance our fundamental understanding of soil biogeochemical dynamics and more realistically project likely soil C response to environmental change at global scales.
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TL;DR: Conceptualizing SOM into POM versus MAOM is a feasible, well-supported, and useful framework that will allow scientists to move beyond studies of bulk SOM, but also use a consistent separation scheme across studies.
Abstract: Managing soil organic matter (SOM) stocks to address global change challenges requires well-substantiated knowledge of SOM behavior that can be clearly communicated between scientists, management practitioners, and policy makers. However, SOM is incredibly complex and requires separation into multiple components with contrasting behavior in order to study and predict its dynamics. Numerous diverse SOM separation schemes are currently used, making cross-study comparisons difficult and hindering broad-scale generalizations. Here, we recommend separating SOM into particulate (POM) and mineral-associated (MAOM) forms, two SOM components that are fundamentally different in terms of their formation, persistence, and functioning. We provide evidence of their highly contrasting physical and chemical properties, mean residence times in soil, and responses to land use change, plant litter inputs, warming, CO2 enrichment, and N fertilization. Conceptualizing SOM into POM versus MAOM is a feasible, well-supported, and useful framework that will allow scientists to move beyond studies of bulk SOM, but also use a consistent separation scheme across studies. Ultimately, we propose the POM versus MAOM framework as the best way forward to understand and predict broad-scale SOM dynamics in the context of global change challenges and provide necessary recommendations to managers and policy makers.
521 citations
Cites methods from "Explicitly representing soil microb..."
...In fact, several newer models use POM and MAOM (Fatichi, Manzoni, Or, & Paschalis, 2019; Robertson et al., 2018; Sulman et al., 2014), while there is debate on whether or how to include other fractions (Filser et al., 2016; Sulman et al., 2018; Wieder et al., 2015)....
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TL;DR: It is argued that the challenge of predicting the ecosystem implications of microbial processes can be met by identifying microbial life history strategies based on an organism’s phenotypic characteristics, or traits, and representing these strategies in ecosystem models.
Abstract: Microorganisms are critical in terrestrial carbon cycling because their growth, activity and interactions with the environment largely control the fate of recent plant carbon inputs as well as protected soil organic carbon [1, 2]. Soil carbon stocks reflect a balance between microbial decomposition of organic carbon and stabilisation of microbial assimilated carbon. The balance can shift under altered environmental conditions [3], and new research suggests that knowledge of microbial physiology may be critical for projecting changes in soil carbon and improving the prognosis of climate change feedbacks [4–7]. Still, predicting the ecosystem implications of microbial processes remains a challenge. Here we argue that this challenge can be met by identifying microbial life history strategies based on an organism’s phenotypic characteristics, or traits, and representing these strategies in ecosystem models.
What are the key microbial traits for soil carbon cycling under environmental change? Microbial growth and survival in soil are impacted by multiple traits that determine responses to varying resource availability and fluctuating abiotic conditions [8]. Cellular maintenance activities (those that do not produce growth) include production of extracellular enzymes to degrade and acquire resources, biomolecular repair mechanisms, maintenance of cellular integrity, osmotic balance, defence, antagonism, cell signalling and motility [9–11]. It is conceivable that microbial investment into maintenance activities would be generally high in soils, with their highly heterogeneous and temporally variable resource distribution and stressful abiotic conditions like extremes of moisture, temperature, pH and salinity [12, 13]. Selective pressures in suboptimal environmental conditions could lead to greater cellular-level physiological allocation to maintenance relative to growth traits (Fig. 1) thereby impacting soil carbon cycling processes.
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Fig. 1
Schematic showing cellular C flux that includes depolymerisation, substrate uptake, assimilation, dissimilation, biomass synthesis and non-growth production. Extracellular enzyme production represents investment in resource acquisition, stress protein production is linked to stress tolerance mechanisms, and biomass production reflects higher growth yield. Forked arrows signify metabolic points where hypothesised tradeoffs in traits might occur. The expected empirical relationships among the key traits are also shown
356 citations
University of Oklahoma1, Tsinghua University2, Stanford University3, Lund University4, University of California, Irvine5, Max Planck Society6, Universidade Nova de Lisboa7, Commonwealth Scientific and Industrial Research Organisation8, Centre national de la recherche scientifique9, University of Maryland Center for Environmental Science10, Boston University11, University of California, Berkeley12, Lawrence Berkeley National Laboratory13, Canadian Forest Service14, United States Geological Survey15, Met Office16, University of Alaska Fairbanks17, Colorado State University18, Université du Québec à Montréal19, Purdue University20, Microsoft21, Auburn University22, Pacific Northwest National Laboratory23, Northern Arizona University24, Joint Global Change Research Institute25, Oak Ridge National Laboratory26, National Center for Atmospheric Research27, East China Normal University28, Iowa State University29, University of Texas at El Paso30, Beijing Normal University31
TL;DR: In this article, the authors suggest that model structures should reflect real-world processes, parameters should be calibrated to match model outputs with observations, and external forcing variables should accurately prescribe the environmental conditions that soils experience.
Abstract: Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.
348 citations
Cites background or methods or result from "Explicitly representing soil microb..."
...…Parton et al., 2007] have been used to evaluate litter mass loss dynamics across continental-scale climate gradients in first-order and microbialexplicit models [Bonan et al., 2013; Wieder et al., 2014b, 2015; Yang et al., 2009], and should serve as a candidate benchmark data set for other models....
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...Preliminary simulations, however, suggest that explicitly considering microbial physiology and functional composition in a simplified form may offer a path forward [Wieder et al., 2015]....
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...Recent work, however, indicates that updating the assumptions underlying biogeochemical models can considerably shift the magnitude of projected soil carbon (C) in response to environmental perturbations [Allison et al., 2010; Sulman et al., 2014; Wieder et al., 2013, 2015]....
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...To date, none of the global-scale microbial-explicit decomposition models directly account for this scaling in detail [Hararuk et al., 2015; Sulman et al., 2014; Wieder et al., 2013, 2015]....
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...…of work demonstrates that microbial-explicit models match observations at least as well as first-order models, if not better, but make different projections about the fate of that C under environmental change [Hararuk et al., 2015; He et al., 2014; Sulman et al., 2014;Wieder et al., 2014b, 2015]....
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TL;DR: It is demonstrated that microbial biomass and carbon use efficiency are reduced in human-impacted near-neutral pH soils, whereas in acidic soils, microbial growth is a bigger constraint on decomposition rates.
Abstract: Soil microorganisms act as gatekeepers for soil-atmosphere carbon exchange by balancing the accumulation and release of soil organic matter However, poor understanding of the mechanisms responsible hinders the development of effective land management strategies to enhance soil carbon storage Here we empirically test the link between microbial ecophysiological traits and topsoil carbon content across geographically distributed soils and land use contrasts We discovered distinct pH controls on microbial mechanisms of carbon accumulation Land use intensification in low-pH soils that increased the pH above a threshold (~62) leads to carbon loss through increased decomposition, following alleviation of acid retardation of microbial growth However, loss of carbon with intensification in near-neutral pH soils was linked to decreased microbial biomass and reduced growth efficiency that was, in turn, related to trade-offs with stress alleviation and resource acquisition Thus, less-intensive management practices in near-neutral pH soils have more potential for carbon storage through increased microbial growth efficiency, whereas in acidic soils, microbial growth is a bigger constraint on decomposition rates
330 citations
TL;DR: The CarboSMS consortium federates French researchers working on these mechanisms and their effects on C stocks in a local and global change setting (land use, agricultural practices, climatic and soil conditions, etc.). This article is a synthesis of this consortium's first seminar.
Abstract: The international 4 per 1000 initiative aims at supporting states and non-governmental stakeholders in their efforts towards a better management of soil carbon (C) stocks. These stocks depend on soil C inputs and outputs. They are the result of fine spatial scale interconnected mechanisms, which stabilise/destabilise organic matter-borne C. Since 2016, the CarboSMS consortium federates French researchers working on these mechanisms and their effects on C stocks in a local and global change setting (land use, agricultural practices, climatic and soil conditions, etc.). This article is a synthesis of this consortium’s first seminar. In the first part, we present recent advances in the understanding of soil C stabilisation mechanisms comprising biotic and abiotic processes, which occur concomitantly and interact. Soil organic C stocks are altered by biotic activities of plants (the main source of C through litter and root systems), microorganisms (fungi and bacteria) and ‘ecosystem engineers’ (earthworms, termites, ants). In the meantime, abiotic processes related to the soil-physical structure, porosity and mineral fraction also modify these stocks. In the second part, we show how agricultural practices affect soil C stocks. By acting on both biotic and abiotic mechanisms, land use and management practices (choice of plant species and density, plant residue exports, amendments, fertilisation, tillage, etc.) drive soil spatiotemporal organic inputs and organic matter sensitivity to mineralisation. Interaction between the different mechanisms and their effects on C stocks are revealed by meta-analyses and long-term field studies. The third part addresses upscaling issues. This is a cause for major concern since soil organic C stabilisation mechanisms are most often studied at fine spatial scales (mm–μm) under controlled conditions, while agricultural practices are implemented at the plot scale. We discuss some proxies and models describing specific mechanisms and their action in different soil and climatic contexts and show how they should be taken into account in large scale models, to improve change predictions in soil C stocks. Finally, this literature review highlights some future research prospects geared towards preserving or even increasing C stocks, our focus being put on the mechanisms, the effects of agricultural practices on them and C stock prediction models.
294 citations
Cites background from "Explicitly representing soil microb..."
...In particular, biological regulation (macrofauna, microorganisms) and soil structure (Wieder et al. 2015) are barely taken into account in these models despite the fact that they are major drivers of soil C dynamics, as noted above (see Section 2)....
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References
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TL;DR: Various aspects of the biochemistry of photosynthetic carbon assimilation in C3 plants are integrated into a form compatible with studies of gas exchange in leaves.
Abstract: Various aspects of the biochemistry of photosynthetic carbon assimilation in C3 plants are integrated into a form compatible with studies of gas exchange in leaves. These aspects include the kinetic properties of ribulose bisphosphate carboxylase-oxygenase; the requirements of the photosynthetic carbon reduction and photorespiratory carbon oxidation cycles for reduced pyridine nucleotides; the dependence of electron transport on photon flux and the presence of a temperature dependent upper limit to electron transport. The measurements of gas exchange with which the model outputs may be compared include those of the temperature and partial pressure of CO2(p(CO2)) dependencies of quantum yield, the variation of compensation point with temperature and partial pressure of O2(p(O2)), the dependence of net CO2 assimilation rate on p(CO2) and irradiance, and the influence of p(CO2) and irradiance on the temperature dependence of assimilation rate.
7,312 citations
"Explicitly representing soil microb..." refers methods in this paper
...…(e.g., sun-lit vs. shaded leaves) and across plant functional types (e.g., grassland vs. forest ecosystems), while still employing micro-scale enzyme kinetics [Farquhar et al., 1980] to simulate aboveground C balance and project its response to environmental change [Bonan et al., 2011, 2012, 2014]....
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TL;DR: This work has suggested that several environmental constraints obscure the intrinsic temperature sensitivity of substrate decomposition, causing lower observed ‘apparent’ temperature sensitivity, and these constraints may, themselves, be sensitive to climate.
Abstract: Significantly more carbon is stored in the world's soils--including peatlands, wetlands and permafrost--than is present in the atmosphere. Disagreement exists, however, regarding the effects of climate change on global soil carbon stocks. If carbon stored belowground is transferred to the atmosphere by a warming-induced acceleration of its decomposition, a positive feedback to climate change would occur. Conversely, if increases of plant-derived carbon inputs to soils exceed increases in decomposition, the feedback would be negative. Despite much research, a consensus has not yet emerged on the temperature sensitivity of soil carbon decomposition. Unravelling the feedback effect is particularly difficult, because the diverse soil organic compounds exhibit a wide range of kinetic properties, which determine the intrinsic temperature sensitivity of their decomposition. Moreover, several environmental constraints obscure the intrinsic temperature sensitivity of substrate decomposition, causing lower observed 'apparent' temperature sensitivity, and these constraints may, themselves, be sensitive to climate.
5,367 citations
"Explicitly representing soil microb..." refers background or methods in this paper
..., Q10) describe the temperature sensitivity of SOM decomposition [Davidson and Janssens, 2006]....
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...In first-order models, fixed parameters (e.g., Q10) describe the temperature sensitivity of SOM decomposition [Davidson and Janssens, 2006]....
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University of Zurich1, University of California, Berkeley2, Lawrence Berkeley National Laboratory3, University of Oldenburg4, Max Planck Society5, Leibniz University of Hanover6, University of Antwerp7, Oregon State University8, Technische Universität München9, Cornell University10, Newcastle University11, University of Florence12, Weizmann Institute of Science13
TL;DR: In this article, a new generation of experiments and soil carbon models were proposed to predict the SOM response to global warming, and they showed that molecular structure alone alone does not control SOM stability.
Abstract: Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily—and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.
4,219 citations
TL;DR: Bacterial growth is considered as a method for the study of bacterial physiology and biochemistry, with the interpretation of quantitative data referring to bacterial growth limited to populations considered genetically homogeneous.
Abstract: The study of the growth of bacterial cultures does not constitute a specialized subject or branch of research: it is the basic method of Microbiology. It would be a foolish enterprise, and doomed to failure, to attempt reviewing briefly a \"subject\" which covers actually our whole discipline. Unless, of course, we considered the formal laws of growth for their own sake, an approach which has repeatedly proved sterile. In the present review we shall consider bacterial growth as a method for the study of bacterial physiology and biochemistry. More precisely, we shall concern ourselves with the quantitative aspects of the method, with the interpretation of quantitative data referring to bacterial growth. Furthermore, we shall considerz exclusively the positive phases of growth, since the study of bacterial \"death,\" i.e., of the negative phases of growth, involves distinct problems and methods. The discussion will be limited to populations considered genetically homogeneous. The problems of mutation and selection in growing cultures have been excellently dealt with in recent review articles by Delbriick (1) and Luria (2). No attempt is made at reviewing the literature on a subject which, as we have just seen, is not really a subject at all. The papers and results quoted have been selected as illustrations of the points discussed.
4,104 citations
"Explicitly representing soil microb..." refers background in this paper
...Both of these models are rooted in kinetic rates observed in laboratory isolates [Monod, 1949]....
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...The basic micro-scalemechanisms represented inmicrobial models are well supported by decades of biochemical and physiological studies [Monod, 1949; Sinsabaugh et al., 2014, 2015], but it remains challenging to predict how diverse enzymes and microbes interact with heterogeneous soil environments to…...
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TL;DR: In this article, a model of soil organic matter (SOM) quantity and composition was used to simulate steady-state organic matter levels for 24 grassland locations in the U.S. Great Plains.
Abstract: We analyzed climatic and textural controls of soil organic C and N for soils of the U.S. Great Plains. We used a model of soil organic matter (SOM) quantity and composition to simulate steady-state organic matter levels for 24 grassland locations in the Great Plains. The model was able to simulate the effects of climatic gradients on SOM and productivity. Soil texture was also a major control over organic matter dynamics. The model adequately predicted above-ground plant production and soil C and N levels across soil textures (sandy, medium, and fine); however, the model tended to overestimate soil C and N levels for fine textured soil by 10 to 15%. The impact of grazing on the system was simulated and showed that steady-state soil C and N levels were sensitive to the grazing intensity, with soil C and N levels decreasing with increased grazing rates. Regional trends in SOM can be predicted using four site-specific variables, temperature, moisture, soil texture, and plant lignin content. Nitrogen inputs must also be known. Grazing intensity during soil development is also a significant control over steady-state levels of SOM, and since few data are available on presettlement grazing, some uncertainty is inherent in the model predictions
3,594 citations