A trait-based approach for modelling microbial litter decomposition

Functional biogeography of marine microbial heterotrophs

Heterotrophic bacteria and archaea ("heteroprokaryotes") drive global carbon cycling, but how to quantitatively organize their functional complexity remains unclear. We generated a global-scale understanding of marine heteroprokaryotic functional biogeography by synthesizing genetic sequencing data with a mechanistic marine ecosystem model. We incorporated heteroprokaryotic diversity into the trait-based model along two axes: substrate lability and growth strategy. Using genetic sequences along three ocean transects, we compiled 21 heteroprokaryotic guilds and estimated their degree of optimization for rapid growth (copiotrophy). Data and model consistency indicated that gradients in grazing and substrate lability predominantly set biogeographical patterns, and we identified deep-ocean "slow copiotrophs" whose ecological interactions control the surface accumulation of dissolved organic carbon.

A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry

Microbes drive the biogeochemical cycles of earth systems, yet the long-standing goal of linking emerging genomic information, microbial traits, mechanistic ecosystem models, and projections under climate change has remained elusive despite a wealth of emerging genomic information. Here we developed a general genome-to-ecosystem (G2E) framework for integrating genome-inferred microbial kinetic traits into mechanistic models of terrestrial ecosystems and applied it at a well-studied Arctic wetland by benchmarking predictions against observed greenhouse gas emissions. We found variation in genome-inferred microbial kinetic traits resulted in large differences in simulated annual methane emissions, quantitatively demonstrating that the genomically observable variations in microbial capacity are consequential for ecosystem functioning. Applying microbial community-aggregated traits via genome relative-abundance-weighting gave better methane emissions predictions (i.e., up to 54% decrease in bias) compared to ignoring the observed abundances, highlighting the value of combined trait inferences and abundances. This work provides an example of integrating microbial functional trait-based genomics, mechanistic and pragmatic trait parameterizations of diverse microbial metabolisms, and mechanistic ecosystem modeling. The generalizable G2E framework will enable the use of abundant microbial metagenomics data to improve predictions of microbial interactions in many complex systems, including oceanic microbiomes.

Trait-environmental relationships reveal microbial strategies of environmental adaptation

Microbial trait variation along environmental gradients is crucial to understanding their ecological adaptation mechanisms. With the increasing availability of microbial genomes, making full use of the genome-based traits to decipher their adaptation strategies becomes promising and urgent. Here, we examined microbial communities in water and sediments of 20 East African lakes with pH values ranging from 7.2 to 10.1 through taxonomic profiling and genome-centric metagenomics. We identified functional traits important for microbial adaptation to the stresses of alkalinity and salinity based on the significant trait-environment relationships (TERs), including those involved in cytoplasmic pH homeostasis, compatible solute accumulation, cell envelope modification, and energy requisition. By integrating these significant traits, we further developed an environmental adaptation index to quantify the species-level adaptive capacity for environmental stresses, such as high pH environments. The adaptation index of pH showed consistently significant positive relationships with species pH optima across regional and global genomic datasets from freshwater, marine, and soda lake ecosystems. The generality of the index for quantifying environmental adaptation was demonstrated by showing significant relationships with the species niche optima for the gradients of soil temperature and seawater salinity. These results highlight the importance of TERs in facilitating the inference of microbial genomic-based adaptation mechanisms and expand our understanding of ecological adaptive strategies along environmental gradients.

Stochasticity Highlights the Development of Both the Gastrointestinal and Upper-Respiratory-Tract Microbiomes of Neonatal Dairy Calves in Early Life

The microbiome of dairy calves undergoes extensive change due to various forces during the first weeks of life. Importantly, diseases such as bovine respiratory disease (BRD) and calf diarrhea can have profound impacts on the early-life microbiome. Therefore, a longitudinal, repeated-measures pilot study was designed to characterize the establishment of nasal and fecal microbiomes of dairy calves, assess the governing forces of microbial assembly, and evaluate how disease states impact these microbial ecologies. Dairy calves (n = 19) were clinically evaluated for gastrointestinal and respiratory disease across three weeks beginning at age ≤ seven days old. Fecal (n = 57) and nasal (n = 57) microbial samples were taken for paired-end 16S rRNA gene amplicon sequencing. Taxonomy and diversity analyses were used to characterize early-life nasal and fecal microbiomes. Stochasticity and determinism were measured using normalized stochasticity testing (NST) and Dirichlet multinomial model (DMM). All analyses were tested for statistical significance. Clinical diarrhea was observed in 11 of the 19 calves. Clinical BRD was not independently observed among the cohort; however, two calves presented clinical signs of both BRD and diarrhea. Taxonomic analysis revealed that fecal samples were highlighted by Bacteroidaceae (40%; relative abundance), Ruminococcaceae (13%), and Lachnospiraceae (10%), with changes in diversity (Kruskal-Wallis; p < 0.05) and composition (PERMANOVA; p < 0.05). Clinical diarrhea reduced diversity in the fecal microbiome but did not impact composition. Nasal samples featured Moraxellaceae (49%), Mycoplasmataceae (16%), and Pasteurellaceae (3%). While no diversity changes were seen in nasal samples, compositional changes were observed (p < 0.05). NST metrics (Kruskal-Wallis; p > 0.01) and DMM (PERMANOVA; p < 0.01) revealed that stochastic, neutral theory-based assembly dynamics govern early-life microbial composition and that distinct microbial populations drive community composition in healthy and diarrheic calves.

Putting soil microbes on first: identifying the engines of home-field advantage in litter decomposition

This article is a Commentary on Shigyo et al. (2024); 243: 2146–2156.

Bacteria face trade-offs in the decomposition of complex biopolymers

Although depolymerization of complex carbohydrates is a growth-limiting bottleneck for microbial decomposers, we still lack understanding about how the production of different types of extracellular enzymes affect individual microbes and in turn the performance of whole decomposer communities. In this work we use a theoretical model to evaluate the potential trade-offs faced by microorganisms in biopolymer decomposition which arise due to the varied biochemistry of different depolymerizing enzyme classes. We specifically consider two broad classes of depolymerizing extracellular enzymes, which are widespread across microbial taxa: exo-enzymes that cleave small units from the ends of polymer chains and endo-enzymes that act at random positions generating degradation products of varied sizes. Our results demonstrate a fundamental trade-off in the production of these enzymes, which is independent of system's complexity and which appears solely from the intrinsically different temporal depolymerization dynamics. As a consequence, specialists that produce either exo- or only endo-enzymes limit their growth to high or low substrate conditions, respectively. Conversely, generalists that produce both enzymes in an optimal ratio expand their niche and benefit from the synergy between the two enzymes. Finally, our results show that, in spatially-explicit environments, consortia composed of endo- and exo-specialists can only exist under oligotrophic conditions. In summary, our analysis demonstrates that the (evolutionary or ecological) selection of a depolymerization pathway will affect microbial fitness under low or high substrate conditions, with impacts on the ecological dynamics of microbial communities. It provides a possible explanation why many polysaccharide degraders in nature show the genetic potential to produce both of these enzyme classes.

Fungal traits help to understand the decomposition of simple and complex plant litter

Litter decomposition is a key ecosystem process, relevant for the release and storage of nutrients and carbon in soil. Soil fungi are one of the dominant drivers of organic matter decomposition, but fungal taxa differ substantially in their functional ability to decompose plant litter. Knowledge is mostly based on observational data and subsequent molecular analyses and in vitro studies have been limited to forest ecosystems. In order to better understand functional traits of saprotrophic soil fungi in grassland ecosystems, we isolated 31 fungi from a natural grassland and performed several in vitro studies testing for i) leaf and wood litter decomposition, ii) the ability to use carbon sources of differing complexity, iii) the enzyme repertoire. Decomposition strongly varied among phyla and isolates, with Ascomycota decomposing the most and Mucoromycota decomposing the least. The phylogeny of the fungi and their ability to use complex carbon were the most important predictors for decomposition. Our findings show that it is crucial to understand the role of individual members and functional groups within the microbial community. This is an important way forward to understand the role of microbial community composition for the prediction of litter decomposition and subsequent potential carbon storage in grassland soils.

Microbial evolution-An under-appreciated driver of soil carbon cycling

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Investigating eco-evolutionary processes of microbial community assembly in the wild using a model leaf litter system

Microbial communities are not the easiest to manipulate experimentally in natural ecosystems. However, leaf litter-topmost layer of surface soil-is uniquely suitable to investigate the complexities of community assembly. Here, we reflect on over a decade of collaborative work to address this topic using leaf litter as a model system in Southern California ecosystems. By leveraging a number of methodological advantages of the system, we have worked to demonstrate how four processes-selection, dispersal, drift, and diversification-contribute to bacterial and fungal community assembly and ultimately impact community functioning. Although many dimensions remain to be investigated, our initial results demonstrate that both ecological and evolutionary processes occur simultaneously to influence microbial community assembly. We propose that the development of additional and experimentally tractable microbial systems will be enormously valuable to test the role of eco-evolutionary processes in natural settings and their implications in the face of rapid global change.

Microbial regulation of feedbacks to ecosystem change

Microbes are key biodiversity components of all ecosystems and control vital ecosystem functions. Although we have just begun to unravel the scales and factors that regulate microbial communities, their role in mediating ecosystem stability in response to disturbances remains underexplored. Here, we review evidence of how, when, and where microbes regulate or drive disturbance feedbacks. Negative feedbacks dampen the impacts of disturbance, which maintain ecosystem stability, whereas positive feedbacks instead erode stability by amplifying the disturbance. Here we describe the processes underlying the responses to disturbance using a hierarchy of functional traits, and we exemplify how these may drive biogeochemical feedbacks. We suggest that the feedback potential of functional traits at different hierarchical levels is contingent on the complexity and heterogeneity of the environment.

Increasing aggregate size reduces single-cell organic carbon incorporation by hydrogel-embedded wetland microbes

Microbial degradation of organic carbon in sediments is impacted by the availability of oxygen and substrates for growth. To better understand how particle size and redox zonation impact microbial organic carbon incorporation, techniques that maintain spatial information are necessary to quantify elemental cycling at the microscale. In this study, we produced hydrogel microspheres of various diameters (100, 250, and 500 μm) and inoculated them with an aerobic heterotrophic bacterium isolated from a freshwater wetland (Flavobacterium sp.), and in a second experiment with a microbial community from an urban lacustrine wetland. The hydrogel-embedded microbial populations were incubated with 13C-labeled substrates to quantify organic carbon incorporation into biomass via nanoSIMS. Additionally, luminescent nanosensors enabled spatially explicit measurements of oxygen concentrations inside the microspheres. The experimental data were then incorporated into a reactive-transport model to project long-term steady-state conditions. Smaller (100 μm) particles exhibited the highest microbial cell-specific growth per volume, but also showed higher absolute activity near the surface compared to the larger particles (250 and 500 μm). The experimental results and computational models demonstrate that organic carbon availability was not high enough to allow steep oxygen gradients and as a result, all particle sizes remained well-oxygenated. Our study provides a foundational framework for future studies investigating spatially dependent microbial activity in aggregates using isotopically labeled substrates to quantify growth.

Rethinking Aerobic Respiration in the Hyporheic Zone under Variation in Carbon and Nitrogen Stoichiometry

Hyporheic zones (HZs)─zones of groundwater-surface water mixing─are hotspots for dissolved organic matter (DOM) and nutrient cycling that can disproportionately impact aquatic ecosystem functions. However, the mechanisms affecting DOM metabolism through space and time in HZs remain poorly understood. To resolve this gap, we investigate a recently proposed theory describing trade-offs between carbon (C) and nitrogen (N) limitations as a key regulator of HZ metabolism. We propose that throughout the extent of the HZ, a single process like aerobic respiration (AR) can be limited by both DOM thermodynamics and N content due to highly variable C/N ratios over short distances (centimeter scale). To investigate this theory, we used a large flume, continuous optode measurements of dissolved oxygen (DO), and spatially and temporally resolved molecular analysis of DOM. Carbon and N limitations were inferred from changes in the elemental stoichiometric ratio. We show sequential, depth-stratified relationships of DO with DOM thermodynamics and organic N that change across centimeter scales. In the shallow HZ with low C/N, DO was associated with the thermodynamics of DOM, while deeper in the HZ with higher C/N, DO was associated with inferred biochemical reactions involving organic N. Collectively, our results suggest that there are multiple competing processes that limit AR in the HZ. Resolving this spatiotemporal variation could improve predictions from mechanistic models, either via more highly resolved grid cells or by representing AR colimitation by DOM thermodynamics and organic N.

Denitrification and the challenge of scaling microsite knowledge to the globe

Our knowledge of microbial processes-who is responsible for what, the rates at which they occur, and the substrates consumed and products produced-is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are-by themselves-insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

Soil enzyme kinetics and thermodynamics in response to long-term vegetation succession

Our current knowledge regarding soil organic matter (SOM) turnover during vegetation succession is often limited to conventional C decomposition models. However, microbial enzyme-mediated SOM degradation and nutrient cycling are mainly reflected in the kinetic parameters of these enzymes. Changes in the composition and structure of plant communities are typically accompanied by alterations in soil ecological functions. Therefore, it is important to clarify the kinetic parameters of soil enzymes and their temperature sensitivity in response to vegetation succession, especially under the current trend of climate change-related global warming; however, these are still understudied. Here, we examined the kinetic parameters of soil enzymes, their temperature sensitivity, and their associations with environmental variables over long-term (approximately 160 years) vegetation succession on the Loess Plateau using a space-for-time substitution method. We found that the kinetic parameters of soil enzymes changed significantly during vegetation succession. Specific response characteristics varied depending on the enzyme. Overall, the temperature sensitivity (Q₁₀, 0.79-1.87) and activation energy (E_a, 8.69-41.49 kJ·mol^-1) remained stable during long-term succession. Compared with N-acetyl-glucosaminidase and alkaline phosphatase, β-glucosidase was more sensitive to extreme temperatures. In particular, two kinetic parameters (i.e., maximum reaction rate, V_max; half-saturation constant, K_m) of β-glucosidase were decoupled at low (5 °C) and high (35 °C) temperatures. Overall, V_max was the primary determinant of variations of enzyme catalytic efficiency (K_cat) during succession, and soil total nutrients had a greater impact on K_cat than available nutrients. Our results suggested that soil ecosystems played an increasingly important role as a C source during long-term vegetation succession, as indicated by the positive responses of the C cycling enzyme K_cat, while the factors related to soil N and P cycling remained relatively stable.

Implications of Soil Microbial Community Assembly for Ecosystem Restoration: Patterns, Process, and Potential

While it is now widely accepted that microorganisms provide essential functions in restoration ecology, the nature of relationships between microbial community assembly and ecosystem recovery remains unclear. There has been a longstanding challenge to decipher whether microorganisms facilitate or simply follow ecosystem recovery, and evidence for each is mixed at best. We propose that understanding microbial community assembly processes is critical to understanding the role of microorganisms during ecosystem restoration and thus optimizing management strategies. We examine how the connection between environment, community structure, and function is fundamentally underpinned by the processes governing community assembly of these microbial communities. We review important factors to consider in evaluating microbial community structure in the context of ecosystem recovery as revealed in studies of microbial succession: (1) variation in community assembly processes, (2) linkages to ecosystem function, and (3) measurable microbial community attributes. We seek to empower restoration ecology with microbial assembly and successional understandings that can generate actionable insights and vital contexts for ecosystem restoration efforts.

Current controversies on mechanisms controlling soil carbon storage: implications for interactions with practitioners and policy-makers. A review

There is currently an intense debate about the potential for additional organic carbon storage in soil, the strategies by which it may be accomplished and what the actual benefits might be for agriculture and the climate. Controversy forms an essential part of the scientific process, but on the topic of soil carbon storage, it may confuse the agricultural community and the general public and may delay actions to fight climate change. In an attempt to shed light on this topic, the originality of this article lies in its intention to provide a balanced description of contradictory scientific opinions on soil carbon storage and to examine how the scientific community can support decision-making despite the controversy. In the first part, we review and attempt to reconcile conflicting views on the mechanisms controlling organic carbon dynamics in soil. We discuss the divergent opinions about chemical recalcitrance, the microbial or plant origin of persistent soil organic matter, the contribution of particulate organic matter to additional organic carbon storage in soil, and the spatial and energetic inaccessibility of soil organic matter to decomposers. In the second part, we examine the advantages and limitations of big data management and modeling, which are essential tools to link the latest scientific theories with the actions taken by stakeholders. Finally, we show how the analysis and discussion of controversies can guide scientists in supporting stakeholders for the design of (i) appropriate trade-offs for biomass use in agriculture and forestry and (ii) climate-smart management practices, keeping in mind their still unresolved effects on soil carbon storage.

Decomposition rate as an emergent property of optimal microbial foraging

Decomposition kinetics are fundamental for quantifying carbon and nutrient cycling in terrestrial and aquatic ecosystems. Several theories have been proposed to construct process-based kinetics laws, but most of these theories do not consider that microbial decomposers can adapt to environmental conditions, thereby modulating decomposition. Starting from the assumption that a homogeneous microbial community maximizes its growth rate over the period of decomposition, we formalize decomposition as an optimal control problem where the decomposition rate is a control variable. When maintenance respiration is negligible, we find that the optimal decomposition kinetics scale as the square root of the substrate concentration, resulting in growth kinetics following a Hill function with exponent 1/2 (rather than the Monod growth function). When maintenance respiration is important, optimal decomposition is a more complex function of substrate concentration, which does not decrease to zero as the substrate is depleted. With this optimality-based formulation, a trade-off emerges between microbial carbon-use efficiency (ratio of growth rate over substrate uptake rate) and decomposition rate at the beginning of decomposition. In environments where carbon substrates are easily lost due to abiotic or biotic factors, microbes with higher uptake capacity and lower efficiency are selected, compared to environments where substrates remain available. The proposed optimization framework provides an alternative to purely empirical or process-based formulations for decomposition, allowing exploration of the effects of microbial adaptation on element cycling.

Priming of soil organic carbon mineralization and its temperature sensitivity in response to vegetation restoration in a karst area of Southwest China

Plant residue input alters native soil organic carbon (SOC) mineralization through the priming effect, which strongly controls C sequestration during vegetation restoration. However, the effects of different vegetation types on SOC priming and the underlying microbial mechanisms due to global warming are poorly understood. To elucidate these unknowns, the current study quantified soil priming effects using ¹³C-labeled maize residue amendments and analyzed the community structure and abundances in the soils of a vegetation succession gradient (maize field (MF), grassland (GL), and secondary forest (SF)) from a karst region under two incubation temperatures (15 °C and 25 °C). Results revealed that after 120 d of incubation, vegetation restoration increased the soil priming effects. Compared to MF, the priming effects of SF at 15 °C and 25 °C increased by 142.36 % and 161.09 %, respectively. This may be attributed to a high C/N ratio and low-N availability (NO₃^-), which supports the "microbial nitrogen mining" theory. Variations in soil priming were linked to changes in microbial properties. Moreover, with vegetation restoration, the relative abundance of Actinobacteria (copiotrophs) increased, while Ascomycota (oligotrophs) decreased, which accelerated native SOC decomposition. Co-occurrence network analysis indicated that the cooperative interactions of co-existing keystone taxa may facilitate SOC priming. Furthermore, structural equation modeling (SEM) indicated that changes in the priming effects were directly related to the fungal Shannon index and microbial biomass C (MBC), which were affected by soil C/N and NO₃^-. Warming significantly decreased soil priming, which may be attributed to the increase in microbial respiration (qCO₂) and decreased MBC. The temperature sensitivity (Q₁₀) of SOC mineralization was higher after residue amendment, but significant differences were not detected among the vegetation types. Collectively, our results indicated that the intensity of priming effects was dependent on vegetation type and temperature. Microbial community alterations and physicochemical interactions played important roles in SOC decomposition and sequestration.

TbasCO: trait-based comparative 'omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

Contrasting effects of maize litter and litter-derived biochar on the temperature sensitivity of paddy soil organic matter decomposition

Organic matter input regulates the rate and temperature sensitivity (expressed as Q10) of soil organic matter (SOM) decomposition by changing microbial composition and activities. It remains unclear how the incorporation of litter-made biochar instead of litter affects the Q10 of SOM decomposition. Using a unique combination of two-and three-source partitioning methods (isotopic discrimination between C3/C4 pathways and 14C labeling), we investigated: (1) how maize litter versus litter-made biochar (of C4 origin) addition influenced the Q10 of SOM (C3 origin) under 10°C warming, and (2) how the litter or biochar amendments affected the Q10 of 14C-labeled fresh organic matter (FOM) after long-term incubation. Compared with biochar addition, litter increased the rates and Q10 of mass-specific respiration, SOM and FOM decomposition, as well as the contents of SOM-derived dissolved organic C (DOC) and total phospholipid fatty acids (PLFA). Litter-amended soils have much higher activities (Vmax) of β-glucosidase, N-acetyl-β-glucosaminidase, and leucine aminopeptidase, suggesting larger enzyme pools than in soils with biochar. The Q10 of enzyme Vmax (1.6–2.0) and Km (1.2–1.4) were similar between litter-and biochar-amended soils, and remained stable with warming. However, warming reduced microbial biomass (PLFA) and enzyme activity (Vmax), suggesting decreased enzyme production associated with smaller microbial biomass or faster enzyme turnover at higher temperatures. Reductions in PLFA content and enzyme Vmax due to warming were larger in litter-amended soils (by 31%) than in the control and biochar-amended soils (by 4–11%), implying the active litter-feeding microorganisms have a smaller degree of heat tolerance than the inactive microorganisms under biochar amendments. The reduction in enzyme activity (Vmax) by warming was lower in soils with biochar than in the control soil. Our modeling suggested that the higher Q10 in litter-amended soils was mainly caused by faster C loss under warming, linked to reductions in microbial biomass and growth efficiency, rather than the slightly increased SOM-originated substrate availability (DOC). Overall, using straw-made biochar instead of straw per se as a soil amendment lowers the Q10 of SOM and FOM by making microbial communities and enzyme pools more temperature-tolerant, and consequently reduces SOM losses under warming.

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait, a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Ecological modelling approaches for predicting emergent properties in microbial communities

Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.

Individual-based model highlights the importance of trade-offs for virus-host population dynamics and long-term co-existence

Viruses play diverse and important roles in ecosystems. In recent years, trade-offs between host and virus traits have gained increasing attention in viral ecology and evolution. However, microbial organism traits, and viral population parameters in particular, are challenging to monitor. Mathematical and individual-based models are useful tools for predicting virus-host dynamics. We have developed an individual-based evolutionary model to study ecological interactions and evolution between bacteria and viruses, with emphasis on the impacts of trade-offs between competitive and defensive host traits on bacteria-phage population dynamics and trait diversification. Host dynamics are validated with lab results for different initial virus to host ratios (VHR). We show that trade-off based, as opposed to random bacteria-virus interactions, result in biologically plausible evolutionary outcomes, thus highlighting the importance of trade-offs in shaping biodiversity. The effects of nutrient concentration and other environmental and organismal parameters on the virus-host dynamics are also investigated. Despite its simplicity, our model serves as a powerful tool to study bacteria-phage interactions and mechanisms for evolutionary diversification under various environmental conditions.

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

Construction of Environmental Synthetic Microbial Consortia: Based on Engineering and Ecological Principles

In synthetic biology, engineering principles are applied to system design. The development of synthetic microbial consortia represents the intersection of synthetic biology and microbiology. Synthetic community systems are constructed by co-cultivating two or more microorganisms under certain environmental conditions, with broad applications in many fields including ecological restoration and ecological theory. Synthetic microbial consortia tend to have high biological processing efficiencies, because the division of labor reduces the metabolic burden of individual members. In this review, we focus on the environmental applications of synthetic microbial consortia. Although there are many strategies for the construction of synthetic microbial consortia, we mainly introduce the most widely used construction principles based on cross-feeding. Additionally, we propose methods for constructing synthetic microbial consortia based on traits and spatial structure from the perspective of ecology to provide a basis for future work.

Soil microbial trait-based strategies drive metabolic efficiency along an altitude gradient

Trait-based approaches provide a candidate framework for linking soil microbial community to ecosystem processes, yet how the trade-offs in different microbial traits regulate the community-level metabolic efficiency remains unknown. Herein we assessed the roles of the microbial taxa with particular trait strategies in mediating soil microbial metabolic efficiency along an altitude gradient on the Tibetan Plateau. Results showed that soil microbial metabolic efficiency declined with increasing altitude, as indicated by the increasing metabolic quotient (microbial respiration per unit biomass, qCO2) and decreasing carbon use efficiency (CUE). Both qCO2 and CUE were predominantly predicted by microbial physiological and taxonomic attributes after considering key environmental factors including soil pH, substrate quantity and quality. Specifically, the reduced metabolic efficiency was associated with higher investment into nutrient (particularly for phosphorus) acquisitions via enzymes. Furthermore, we identified key microbial assemblies selected by harsh environments (low substrate quality and temperature) as important predictors of metabolic efficiency. These results suggest that particular microbial assemblies adapted to nutrient limited and cold habitats, but at the expense of lower metabolic efficient at higher altitude. Our findings provide a candidate mechanism underlying community-level metabolic efficiency, which has important implications for microbial-mediated processes such as carbon dynamics under global climate changes.

Mapping soil microbial residence time at the global scale

Soil microbes are the fundamental engine for carbon (C) cycling. Microbial residence time (MRT) therefore determines the mineralization of soil organic C, releasing C as heterotrophic respiration and contributing substantially to the C efflux in terrestrial ecosystems. We took use of a comprehensive dataset (2627 data points) and calculated the MRT based on the basal respiration and microbial biomass C. Large variations in MRT were found among biomes, with the largest MRT in boreal forests and grasslands and smallest in natural wetlands. Biogeographic patterns of MRT were found along climate variables (temperature and precipitation), vegetation variables (root C density and net primary productivity), and edaphic factors (soil texture, pH, topsoil porosity, soil C, and total nitrogen). Among environmental factors, edaphic properties dominate the MRT variations. We further mapped the MRT at the global scale with an empirical model. The simulated and observed MRT were highly consistent at plot- (R² = .86), site- (R²  = .88), and biome- (R²  = .99) levels. The global average of MRT was estimated to be 38 (±5) days. A clear latitudinal biogeographic pattern was found for MRT with lower values in tropical regions and higher values in the Arctic. The biome- and global-level estimates of MRT serve as valuable data for parameterizing and benchmarking microbial models.

Toward a function-first framework to make soil microbial ecology predictive

Soil microbial communities perform vital ecosystem functions, such as the decomposition of organic matter to provide plant nutrition. However, despite the functional importance of soil microorganisms, attribution of ecosystem function to particular constituents of the microbial community has been impeded by a lack of information linking microbial function to community composition and structure. Here, we propose a function-first framework to predict how microbial communities influence ecosystem functions. We first view the microbial community associated with a specific function as a whole and describe the dependence of microbial functions on environmental factors (e.g., the intrinsic temperature dependence of bacterial growth rates). This step defines the aggregate functional response curve of the community. Second, the contribution of the whole community to ecosystem function can be predicted, by combining the functional response curve with current environmental conditions. Functional response curves can then be linked with taxonomic data in order to identify sets of "biomarker" taxa that signal how microbial communities regulate ecosystem functions. Ultimately, such indicator taxa may be used as a diagnostic tool, enabling predictions of ecosystem function from community composition. In this paper, we provide three examples to illustrate the proposed framework, whereby the dependence of bacterial growth on environmental factors, including temperature, pH, and salinity, is defined as the functional response curve used to interlink soil bacterial community structure and function. Applying this framework will make it possible to predict ecosystem functions directly from microbial community composition.

Polysaccharide-Bacteria Interactions From the Lens of Evolutionary Ecology

Microbes have the unique ability to break down the complex polysaccharides that make up the bulk of organic matter, initiating a cascade of events that leads to their recycling. Traditionally, the rate of organic matter degradation is perceived to be limited by the chemical and physical structure of polymers. Recent advances in microbial ecology, however, suggest that polysaccharide persistence can result from non-linear growth dynamics created by the coexistence of alternate degradation strategies, metabolic roles as well as by ecological interactions between microbes. This complex "landscape" of degradation strategies and interspecific interactions present in natural microbial communities appears to be far from evolutionarily stable, as frequent gene gain and loss reshape enzymatic repertoires and metabolic roles. In this perspective, we discuss six challenges at the heart of this problem, ranging from the evolution of genetic repertoires, phenotypic heterogeneity in clonal populations, the development of a trait-based ecology, and the impact of metabolic interactions and microbial cooperation on degradation rates. We aim to reframe some of the key questions in the study of polysaccharide-bacteria interactions in the context of eco-evolutionary dynamics, highlighting possible research directions that, if pursued, would advance our understanding of polysaccharide degraders at the interface between biochemistry, ecology and evolution.

Intracellular Storage Reduces Stoichiometric Imbalances in Soil Microbial Biomass – A Theoretical Exploration

Microbial intracellular storage is key to defining microbial resource use strategies and could contribute to carbon (C) and nutrient cycling. However, little attention has been devoted to the role of intracellular storage in soil processes, in particular from a theoretical perspective. Here we fill this gap by integrating intracellular storage dynamics into a microbially explicit soil C and nutrient cycling model. Two ecologically relevant modes of storage are considered: reserve storage, in which elements are routed to a storage compartment in proportion to their uptake rate, and surplus storage, in which elements in excess of microbial stoichiometric requirements are stored and limiting elements are remobilized from storage to fuel growth and microbial maintenance. Our aim is to explore with this model how these different storage modes affect the retention of C and nutrients in active microbial biomass under idealized conditions mimicking a substrate pulse experiment. As a case study, we describe C and phosphorus (P) dynamics using literature data to estimate model parameters. Both storage modes enhance the retention of elements in microbial biomass, but the surplus storage mode is more effective to selectively store or remobilize C and nutrients according to microbial needs. Enhancement of microbial growth by both storage modes is largest when the substrate C:nutrient ratio is high (causing nutrient limitation after substrate addition) and the amount of added substrate is large. Moreover, storage increases biomass nutrient retention and growth more effectively when resources are supplied in a few large pulses compared to several smaller pulses (mimicking a nearly constant supply), which suggests storage to be particularly relevant in highly dynamic soil microhabitats. Overall, our results indicate that storage dynamics are most important under conditions of strong stoichiometric imbalance and may be of high ecological relevance in soil environments experiencing large variations in C and nutrient supply.

Diversity begets diversity during community assembly until ecological limits impose a diversity ceiling

Microbial communities are hugely diverse, but we do not yet understand how species invasions and extinctions drive and limit their diversity. On the one hand, the ecological limits hypothesis posits that diversity is primarily limited by environmental resources. On the other hand, the diversity‐begets‐diversity hypothesis posits that such limits can be easily lifted when new ecological niches are created by biotic interactions. To find out which hypothesis better explains the assembly of microbial communities, we used metabolic modelling. We represent each microbial species by a metabolic network that harbours thousands of biochemical reactions. Together, these reactions determine which carbon and energy sources a species can use, and which metabolic by‐products—potential nutrients for other species—it can excrete in a given environment. We assemble communities by modelling thousands of species invasions in a chemostat‐like environment. We find that early during the assembly process, diversity begets diversity. By‐product excretion transforms a simple environment into one that can sustain dozens of species. During later assembly stages, the creation of new niches slows down, existing niches become filled, successful invasions become rare, and species diversity plateaus. Thus, ecological limitations dominate the late assembly process. We conclude that each hypothesis captures a different stage of the assembly process. Species interactions can raise a community's diversity ceiling dramatically, but only within limits imposed by the environment.

Exploring Microbiome Functional Dynamics through Space and Time with Trait-Based Theory

Microbiomes play essential roles in the health and function of animal and plant hosts and drive nutrient cycling across ecosystems. Integrating novel trait-based approaches with ecological theory can facilitate the prediction of microbial functional traits important for ecosystem functioning and health. In particular, the yield-acquisition-stress (Y-A-S) framework considers dominant microbial life history strategies across gradients of resource availability and stress. However, microbiomes are dynamic, and spatial and temporal shifts in taxonomic and trait composition can affect ecosystem functions. We posit that extending the Y-A-S framework to microbiomes during succession and across biogeographic gradients can lead to generalizable rules for how microbiomes and their functions respond to resources and stress across space, time, and diverse ecosystems. We demonstrate the potential of this framework by applying it to the microbiomes hosted by the carnivorous pitcher plant Sarracenia purpurea, which have clear successional trajectories and are distributed across a broad climatic gradient.

Transcriptomic markers of fungal growth, respiration and carbon-use efficiency

Fungal metabolic carbon acquisition and its subsequent partitioning between biomass production and respiration, i.e. the carbon-use efficiency (CUE), are central parameters in biogeochemical modeling. However, current available techniques for estimating these parameters are all associated with practical and theoretical shortcomings, making assessments unreliable. Gene expression analyses hold the prospect of phenotype prediction by indirect means, providing new opportunities to obtain information about metabolic priorities. We cultured four different fungal isolates (Chalara longipes, Laccaria bicolor, Serpula lacrymans and Trichoderma harzianum) in liquid media with contrasting nitrogen availability and measured growth rates and respiration to calculate CUE. By relating gene expression markers to measured carbon fluxes, we identified genes coding for 1,3-β-glucan synthase and 2-oxoglutarate dehydrogenase as suitable markers for growth and respiration, respectively, capturing both intraspecific variation as well as within-strain variation dependent on growth medium. A transcript index based on these markers correlated significantly with differences in CUE between the fungal isolates. Our study paves the way for the use of these markers to assess differences in growth, respiration and CUE in natural fungal communities, using metatranscriptomic or the RT-qPCR approach.

Micro on a macroscale: relating microbial-scale soil processes to global ecosystem function

Soil microorganisms play a key role in driving major biogeochemical cycles and in global responses to climate change. However, understanding and predicting the behavior and function of these microorganisms remains a grand challenge for soil ecology due in part to the microscale complexity of soils. It is becoming increasingly clear that understanding the microbial perspective is vital to accurately predicting global processes. Here, we discuss the microbial perspective including the microbial habitat as it relates to measurement and modeling of ecosystem processes. We argue that clearly defining and quantifying the size, distribution and sphere of influence of microhabitats is crucial to managing microbial activity at the ecosystem scale. This can be achieved using controlled and hierarchical sampling designs. Model microbial systems can provide key data needed to integrate microhabitats into ecosystem models, while adapting soil sampling schemes and statistical methods can allow us to collect microbially-focused data. Quantifying soil processes, like biogeochemical cycles, from a microbial perspective will allow us to more accurately predict soil functions and address long-standing unknowns in soil ecology.

Linking Genes to Traits in Fungi

Fungi are mediators of the nitrogen and carbon cycles in terrestrial ecosystems. Examining how nitrogen uptake and organic matter decomposition potential differs in fungi can provide insight into the underlying mechanisms driving fungal ecological processes and ecosystem functioning. In this study, we assessed the frequency of genes encoding for specific enzymes that facilitate nitrogen uptake and organic matter decomposition in 879 fungal genomes with fungal taxa grouped into trait-based categories. Our linked gene-trait data approach revealed that gene frequencies vary across and within trait-based groups and that trait-based categories differ in trait space. We present two examples of how this linked gene-trait approach can be used to address ecological questions. First, we show that this type of approach can help us better understand, and potentially predict, how fungi will respond to environmental stress. Specifically, we found that trait-based categories with high nitrogen uptake gene frequency increased in relative abundance when exposed to high soil nitrogen enrichment. Second, by comparing frequencies of nitrogen uptake and organic matter decomposition genes, we found that most ectomycorrhizal fungi in our dataset have similar gene frequencies to brown rot fungi. This demonstrates that gene-trait data approaches can shed light on potential evolutionary trajectories of life history traits in fungi. We present a framework for exploring nitrogen uptake and organic matter decomposition gene frequencies in fungal trait-based groups and provide two concise examples on how to use our framework to address ecological questions from a mechanistic perspective.

Microbial species performance responses to environmental changes: genomic traits and nutrient availability

How microbial species performance indicators, such as growth rate and carbon assimilation rate, respond to environmental changes is a challenging question, especially for complex communities. This limits our ability to understand how species performance responses to environmental changes (that is, species environmental responses) of microbes could be linked to genomic traits and nutrient availability. Based on stable isotope labeling of DNA, we propose a new approach with effect-size metrics to quantify the species environmental responses of microbes by comparing the species performance between defined control and treatment groups. The species performance within microbial communities of the natural or altered environments could be quantitatively determined with quantitative stable isotope probing (qSIP). We further apply this approach, namely effect-size qSIP, to measure species environmental responses upon carbon and nitrogen additions for soil bacteria on mountainsides and to understand their responses from the perspective of genomic traits. Towards high elevations, there is a stronger nitrogen limitation that is indicated by the higher aggregated responses, measured as community-weighted means, of bacterial growth rate upon nitrogen additions. The aggregated responses are further explained by genomic traits, which show higher percentages of significant Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues (KOs) and more diverse KEGG pathways under nutrient additions including nitrogen, and further improve the explanatory power of microbial environmental responses. Nitrogen-induced responses at the species level show the strongest associations with essential KOs for rare species, whereas carbon-induced responses show the strongest associations for dominant species. We conclude that, in addition to environmental determinants such as nitrogen limitation, genomic traits are extremely important for predicting microbial environmental responses at both the community and species levels. Taking advantage of this new approach at the species level, we reveal that rare and dominant species differentially respond to nutrient enrichment via their metabolic traits. The approach and findings can lead to a more holistic understanding of microbial environmental responses in natural habitats, which will be essential for predicting microbial community responses to global environmental changes.

Trait dimensions in bacteria and archaea compared to vascular plants

Bacteria and archaea have very different ecology compared to plants. One similarity, though, is that much discussion of their ecological strategies has invoked concepts such as oligotrophy or stress tolerance. For plants, so-called 'trait ecology'-strategy description reframed along measurable trait dimensions-has made global syntheses possible. Among widely measured trait dimensions for bacteria and archaea three main axes are evident. Maximum growth rate in association with rRNA operon copy number expresses a rate-yield trade-off that is analogous to the acquisitive-conservative spectrum in plants, though underpinned by different trade-offs. Genome size in association with signal transduction expresses versatility. Cell size has influence on diffusive uptake and on relative wall costs. These trait dimensions, and potentially others, offer promise for interpreting ecology. At the same time, there are very substantial differences from plant trait ecology. Traits and their underpinning trade-offs are different. Also, bacteria and archaea use a variety of different substrates. Bacterial strategies can be viewed both through the facet of substrate-use pathways, and also through the facet of quantitative traits such as maximum growth rate. Preliminary evidence shows the quantitative traits vary widely within substrate-use pathways. This indicates they convey information complementary to substrate use.

Niche Selection by Soil Bacterial Community of Disturbed Subalpine Forests in Western Sichuan

Soil bacterial microbial communities are important in the ecosystem function and succession of forests. Using high-throughput 16S rRNA gene sequencing and relative importance for linear regression, we explored how the structures of soil bacterial community were influenced by the environmental factors and restoration succession of secondary forests in the Miyaluo Mountains of western Sichuan, China. Using a space-for-time approach, field measurements and sampling were conducted in four stands at different stages of natural restoration. Results of distance-based multivariate analysis showed that soil pH, organic carbon, available phosphorus, and C/N ratio were the predominant environmental factors that collectively explained a 46.9% variation in the bacterial community structures. The community compositions were jointly controlled by the direct and indirect effects of the rehabilitation stages. The changes in soil environmental factors coincided with restoration succession could lead to the shifts in the relative abundance of different soil bacterial taxa. We screened 13 successional discriminant taxa that could quantitatively indicate the secondary succession subalpine stage. Collectively, our findings show that soil bacteria in different taxa are governed by different local soil variables and rehabilitation ages, which can lead to shifts in the relative abundance of different taxa in successional stages, ultimately changing the entire soil bacterial community with the succession of secondary forest.

Diversity and function of soil microbes on montane gradients: the state of knowledge in a changing world

Mountains have a long history in the study of diversity. Like macroscopic taxa, soil microbes are hypothesized to be strongly structured by montane gradients, and recently there has been important progress in understanding how microbes are shaped by these conditions. Here, we summarize this literature and synthesize patterns of microbial diversity on mountains. Unlike flora and fauna that often display a mid-elevation peak in diversity, we found a decline (34% of the time) or no trend (33%) in total microbial diversity with increasing elevation. Diversity of functional groups also varied with elevation (e.g. saprotrophic fungi declined 83% of the time). Most studies (82%) found that climate and soils (especially pH) were the primary mechanisms driving shifts in composition, and drivers differed across taxa-fungi were mostly determined by climate, while bacteria (48%) and archaea (71%) were structured primarily by soils. We hypothesize that the central role of soils-which can vary independently of other abiotic and geographic gradients-in structuring microbial communities weakens diversity patterns expected on montane gradients. Moving forward, we need improved cross-study comparability of microbial diversity indices (i.e. standardizing sequencing) and more geographic replication using experiments to broaden our knowledge of microbial biogeography on global gradients.

A unified theory for organic matter accumulation

Organic matter constitutes a key reservoir in global elemental cycles. However, our understanding of the dynamics of organic matter and its accumulation remains incomplete. Seemingly disparate hypotheses have been proposed to explain organic matter accumulation: the slow degradation of intrinsically recalcitrant substrates, the depletion to concentrations that inhibit microbial consumption, and a dependency on the consumption capabilities of nearby microbial populations. Here, using a mechanistic model, we develop a theoretical framework that explains how organic matter predictably accumulates in natural environments due to biochemical, ecological, and environmental factors. Our framework subsumes the previous hypotheses. Changes in the microbial community or the environment can move a class of organic matter from a state of functional recalcitrance to a state of depletion by microbial consumers. The model explains the vertical profile of dissolved organic carbon in the ocean and connects microbial activity at subannual timescales to organic matter turnover at millennial timescales. The threshold behavior of the model implies that organic matter accumulation may respond nonlinearly to changes in temperature and other factors, providing hypotheses for the observed correlations between organic carbon reservoirs and temperature in past earth climates.

C-STABILITY an innovative modeling framework to leverage the continuous representation of organic matter

The understanding of soil organic matter (SOM) dynamics has considerably advanced in recent years. It was previously assumed that most SOM consisted of recalcitrant compounds, whereas the emerging view considers SOM as a range of polymers continuously processed into smaller molecules by decomposer enzymes. Mainstreaming this new paradigm in current models is challenging because of their ill-adapted framework. We propose the C-STABILITY model to resolve this issue. Its innovative framework combines compartmental and continuous modeling approaches to accurately reproduce SOM cycling processes. C-STABILITY emphasizes the influence of substrate accessibility on SOM turnover and makes enzymatic and microbial biotransformations of substrate explicit. Theoretical simulations provide new insights on how depolymerization and decomposers ecology impact organic matter chemistry and amount during decomposition and at steady state. The flexible mathematical structure of C-STABILITY offers a promising foundation for exploring new mechanistic hypotheses and supporting the design of future experiments.

Redox-informed models of global biogeochemical cycles

Microbial activity mediates the fluxes of greenhouse gases. However, in the global models of the marine and terrestrial biospheres used for climate change projections, typically only photosynthetic microbial activity is resolved mechanistically. To move forward, we argue that global biogeochemical models need a theoretically grounded framework with which to constrain parameterizations of diverse microbial metabolisms. Here, we explain how the key redox chemistry underlying metabolisms provides a path towards this goal. Using this first-principles approach, the presence or absence of metabolic functional types emerges dynamically from ecological interactions, expanding model applicability to unobserved environments.

“Nothing is less real than realism. It is only by selection, by elimination, by emphasis, that we get at the real meaning of things.” –Georgia O’Keefe

Marine microbial activities fuel biogeochemical cycles that impact the climate, but global models do not account for the myriad physiological processes that microbes perform. Here the authors argue for a model framework that reinterprets the ocean as physics coupled to biologically-driven redox chemistry.