Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Global contribution of invertebrates to forest litter decomposition

Forest litter decomposition is an essential component of global carbon and nutrient turnover. Invertebrates play important roles in litter decomposition, but the regional pattern of their effects is poorly understood. We examined 476 case studies across 93 sites and performed a meta-analysis to estimate regional effects of invertebrates on forest litter decomposition. We then assessed how invertebrate diversity, climate and soil pH drive regional variations in invertebrate-mediated decomposition. We found that (1) invertebrate contributions to litter decomposition are 1.4 times higher in tropical and subtropical forests than in forests elsewhere, with an overall contribution of 31% to global forest litter decomposition; and (2) termite diversity, together with warm, humid and acidic environments in the tropics and subtropics are positively associated with forest litter decomposition by invertebrates. Our results demonstrate the significant difference in invertebrate effects on mediating forest litter decomposition among regions. We demonstrate, also, the significance of termites in driving litter mass loss in the tropics and subtropics. These results are particularly pertinent in the tropics and subtropics where climate change and human disturbance threaten invertebrate biodiversity and the ecosystem services it provides.

Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data

Confidence in model estimates of soil CO2 flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data-model fusion with modified versions of the CN-SIM model and a 571-day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These "lagged-peak" samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest-performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co-metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth-system models.

Quantifying thermal adaptation of soil microbial respiration

Quantifying the rate of thermal adaptation of soil microbial respiration is essential in determining potential for carbon cycle feedbacks under a warming climate. Uncertainty surrounding this topic stems in part from persistent methodological issues and difficulties isolating the interacting effects of changes in microbial community responses from changes in soil carbon availability. Here, we constructed a series of temperature response curves of microbial respiration (given unlimited substrate) using soils sampled from around New Zealand, including from a natural geothermal gradient, as a proxy for global warming. We estimated the temperature optima ([Formula: see text]) and inflection point ([Formula: see text]) of each curve and found that adaptation of microbial respiration occurred at a rate of 0.29 °C ± 0.04 1SE for [Formula: see text] and 0.27 °C ± 0.05 1SE for [Formula: see text] per degree of warming. Our results bolster previous findings indicating thermal adaptation is demonstrably offset from warming, and may help quantifying the potential for both limitation and acceleration of soil C losses depending on specific soil temperatures.

Intracellular carbon storage by microorganisms is an overlooked pathway of biomass growth

The concept of biomass growth is central to microbial carbon (C) cycling and ecosystem nutrient turnover. Microbial biomass is usually assumed to grow by cellular replication, despite microorganisms' capacity to increase biomass by synthesizing storage compounds. Resource investment in storage allows microbes to decouple their metabolic activity from immediate resource supply, supporting more diverse microbial responses to environmental changes. Here we show that microbial C storage in the form of triacylglycerides (TAGs) and polyhydroxybutyrate (PHB) contributes significantly to the formation of new biomass, i.e. growth, under contrasting conditions of C availability and complementary nutrient supply in soil. Together these compounds can comprise a C pool 0.19 ± 0.03 to 0.46 ± 0.08 times as large as extractable soil microbial biomass and reveal up to 279 ± 72% more biomass growth than observed by a DNA-based method alone. Even under C limitation, storage represented an additional 16-96% incorporation of added C into microbial biomass. These findings encourage greater recognition of storage synthesis as a key pathway of biomass growth and an underlying mechanism for resistance and resilience of microbial communities facing environmental change.

Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks

The management of soil organic carbon (SOC) stocks remains at the forefront of greenhouse gas mitigation. However, unprecedented anthropogenic disturbances emanating from continued land-use change have significantly altered SOC distribution across global biomes leading to considerable carbon losses. Consequently, understanding the spatial distribution of SOC across different biomes, particularly at larger scales, is critical for climate change policy formulation and planning. Advancements in remote sensing, availability of big data, and deep learning architecture offer great potential in large-scale SOC mapping. In this regard, this study mapped SOC distribution across South Africa's major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep neural networks (CAE-DNN). From the different deep neural frameworks tested, the CAE-DNN model (developed from 26 selected covariates) achieved the best accuracy with an RMSE value of 7.91 t/ha (about 20 % of the mean). Results further showed that SOC stock correlated with general biome coverage, as the Grassland and Savanna biomes contributed the most (32.38 % and 31.28 %) to the overall SOC pool in South Africa. However, despite their smaller footprint, Forests (44.12 t/h) and the Indian Ocean Coastal Belt (43.05 t/h) biomes demonstrated the highest SOC sequestration capacity. The restoration of degraded biomes is advocated for, in order to boost SOC storage; but a balance between carbon sequestration capacity, biodiversity health, and the adequate provision of ecosystem services must be maintained. To this end, these findings provide a guideline to facilitate sustainable SOC stock management within South Africa's major biomes and indeed other regions of the world.

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.

Dark Matter Enhances Interactions within Both Microbes and Dissolved Organic Matter under Global Change

There are vast but uncharacterized microbial taxa and chemical metabolites (that is, dark matter) across the Earth's ecosystems. A lack of knowledge about dark matter hinders a complete understanding of microbial ecology and biogeochemical cycles. Here, we examine sediment bacteria and dissolved organic matter (DOM) in 300 microcosms along experimental global change gradients in subtropical and subarctic climate zones of China and Norway, respectively. We develop an indicator to quantify the importance of dark matter by comparing co-occurrence network patterns with and without dark matter in bacterial or DOM assemblages. In both climate zones, dark matter constitutes approximately 30-56% of bacterial taxa and DOM metabolites and changes connectivity within bacterial and DOM assemblages by between -15.5 and +61.8%. Dark matter is generally more important for changing network connectivity within DOM assemblages than those of microbes, especially in the subtropical zone. However, the importance of dark matter along global change gradients is strongly correlated between bacteria and DOM and consistently increased toward higher primary productivity because of increasing temperatures and nutrient enrichment. Our findings highlight the importance of microbial and chemical dark matter for changing biogeochemical interactions under global change.

Intense Pasture Management in Brazil in an Integrated Crop-Livestock System Simulated by the DayCent Model

Process-based models (PBM) are important tools for understanding the benefits of Integrated Crop-Livestock Systems (ICLS), such as increasing land productivity and improving environmental conditions. PBM can provide insights into the contribution of agricultural production to climate change and help identify potential greenhouse gas (GHG) mitigation and carbon sequestration options. Rehabilitation of degraded lands is a key strategy for achieving food security goals and can reduce the need for new agricultural land. This study focused on the calibration and validation of the DayCent PBM for a typical ICLS adopted in Brazil from 2018 to 2020. We also present the DayCent parametrization for two forage species (ruzigrass and millet) grown simultaneously, bringing some innovation in the modeling challenges. We used aboveground biomass to calibrate the model, randomly selecting data from 70% of the paddocks in the study area. The calibration obtained a coefficient of determination (R2) of 0.69 and a relative RMSE of 37.0%. During the validation, we used other variables (CO2 flux, grain biomass, and soil water content) measured in the ICLS and performed a double validation for plant growth to evaluate the robustness of the model in terms of generalization. R2 validations ranged from 0.61 to 0.73, and relative RMSE from 11.3 to 48.3%. Despite the complexity and diversity of ICLS results show that DayCent can be used to model ICLS, which is an important step for future regional analyses and large-scale evaluations of the impacts of ICLS.

Temperature and pH mediate stoichiometric constraints of organically derived soil nutrients

It remains unclear how warming will affect resource flows during soil organic matter (SOM) decomposition, in part due to uncertainty in how exoenzymes produced by microbes and roots will function. Rising temperatures can enhance the activity of most exoenzymes, but soil pH can impose limitations on their catalytic efficiency. The effects of temperature and pH on enzyme activity are often examined in environmental samples, but purified enzyme kinetics reveal fundamental attributes of enzymes' intrinsic temperature responses and how relative release of decay-liberated resources (their flow ratios) can change with environmental conditions. In this paper, we illuminate the principle that fundamental, biochemical limitations on SOM release of C, N, and P during decay, and differential exoenzymes' responses to the environment, can exert biosphere-scale significance on the stoichiometry of bioavailable soil resources. To that end, we combined previously published intrinsic temperature sensitivities of two hydrolytic enzymes that release C and N during decay with a novel data set characterizing the kinetics of a P-releasing enzyme (acid phosphatase) across an ecologically relevant pH gradient. We use these data to estimate potential change in the flow ratios derived from these three enzymes' activities (C:N, C:P, and N:P) at the global scale by the end of the century, based on temperature projections and soil pH distribution. Our results highlight how the temperature sensitivity of these hydrolytic enzymes and the influence of pH on that sensitivity can govern the relative availability of bioavailable resources derived from these enzymes. The work illuminates the utility of weaving well-defined kinetic constraints of microbes' exoenzymes into models that incorporate changing SOM inputs and composition, nutrient availability, and microbial functioning into their efforts to project terrestrial ecosystem functioning in a changing climate.

Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.

Climate Effects on Subsoil Carbon Loss Mediated by Soil Chemistry

Subsoils store at least 50% of soil organic carbon (SOC) globally, but climate change may accelerate subsoil SOC (SOC_sub) decomposition and amplify SOC-climate feedbacks. The climate sensitivity of SOC_sub decomposition varies across systems, but we lack the mechanistic links needed to predict system-specific SOC_sub vulnerability as a function of measurable properties at larger scales. Here, we show that soil chemical properties exert significant control over SOC_sub decomposition under elevated temperature and moisture in subsoils collected across terrestrial National Ecological Observatory Network sites. Compared to a suite of soil and site-level variables, a divalent base cation-to-reactive metal gradient, linked to dominant mechanisms of SOC_sub mineral protection, was the best predictor of the climate sensitivity of SOC decomposition. The response was "U"-shaped, showing higher sensitivity to temperature and moisture when either extractable base cations or reactive metals were highest. However, SOC_sub in base cation-dominated subsoils was more sensitive to moisture than temperature, with the opposite relationship demonstrated in reactive metal-dominated subsoils. These observations highlight the importance of system-specific mechanisms of mineral stabilization in the prediction of SOC_sub vulnerability to climate drivers. Our observations also form the basis for a spatially explicit, scalable, and mechanistically grounded tool for improved prediction of SOC_sub response to climate change.

Soil carbon stock and stability under Eucalyptus-based silvopasture and other land-use systems in the Cerrado biodiversity hotspot

During the past few decades, commercial silvopastoral systems (SPS) with exotic Eucalyptus (hybrid) trees have become popular in the Brazilian Cerrado (savanna). With the increasing awareness about the role of carbon (C) storage in soils as a climate-change mitigation strategy and the relationship between the nature of soil aggregates and the soil's carbon sequestration potential, it is important to understand the influence of such SPS systems on soil organic carbon (SOC) storage. We studied C content in three aggregate size classes in six land-use systems on Oxisols in Minas Gerais, Brazil. The systems were planted forest, native secondary forest, managed pasture, and three 8-year-old SPS, differing in their tree-planting configurations. Eucalyptus hybrid was the tree in SPS and planted forest treatments, and Urochloa decumbens was the grass in SPS and pasture treatments. From each treatment, replicated soil samples were collected from four depth-classes (0-10, 10-30, 30-60, and 60-100 cm), fractionated by wet sieving into the three aggregate-size classes, 2000 to 250 μm, 250 to 53 μm, and <53 μm size classes representing macroaggregates, microaggregates, and silt + clay, respectively, and their C contents determined. Down to 1 m, total SOC stock values ranged from 260 Mg ha^-1 under pasture to 167 Mg ha^-1 under native forest, with 174 Mg ha^-1 for Eucalyptus plantation and about 195 Mg ha^-1 for the three SPS. Compared to the degraded native forest, the pasture system had significantly higher SOC in the whole soil and the aggregate size fractions, especially in the lower soil-depth classes. The lower SOC stock of Eucalyptus hybrid SPS compared to open pasture differs from the general trend of SPS having higher stock. Given that the Cerrado biome is a biodiversity hotspot, the use of native nitrogen-fixing trees, of which there are several, is worth investigating. In addition, the conversion from Eucalyptus monocultures to SPS could be considered as a strategy to increase the SOC stock.

Multisubstrate DNA stable isotope probing reveals guild structure of bacteria that mediate soil carbon cycling

Soil microorganisms determine the fate of soil organic matter (SOM), and their activities compose a major component of the global carbon (C) cycle. We employed a multisubstrate, DNA-stable isotope probing experiment to track bacterial assimilation of C derived from distinct sources that varied in bioavailability. This approach allowed us to measure microbial contributions to SOM processing by measuring the C assimilation dynamics of diverse microorganisms as they interacted within soil. We identified and tracked 1,286 bacterial taxa that assimilated 13C in an agricultural soil over a period of 48 d. Overall 13C-assimilation dynamics of bacterial taxa, defined by the source and timing of the 13C they assimilated, exhibited low phylogenetic conservation. We identified bacterial guilds composed of taxa that had similar 13C assimilation dynamics. We show that C-source bioavailability explained significant variation in both C mineralization dynamics and guild structure, and that the growth dynamics of bacterial guilds differed significantly in response to C addition. We also demonstrate that the guild structure explains significant variation in the biogeographical distribution of bacteria at continental and global scales. These results suggest that an understanding of in situ growth dynamics is essential for understanding microbial contributions to soil C cycling. We interpret these findings in the context of bacterial life history strategies and their relationship to terrestrial C cycling.

Leveraging observed soil heterotrophic respiration fluxes as a novel constraint on global-scale models

Microbially explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global-scale soil heterotrophic respiration (R_H ), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global R_H products, as well as 7821 daily site-scale R_H measurements, to evaluate the spatiotemporal performance of one first-order decay model (CASA-CNP) and two microbially explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different input datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the input data used to drive them. Spatial variability in R_H fluxes was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of R_H well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise but also needs to be improved for realistic spatiotemporal variability of R_H . Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models.

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.

Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners

Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. We advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.

A trade-off between plant and soil carbon storage under elevated CO2

Terrestrial ecosystems remove about 30 per cent of the carbon dioxide (CO₂) emitted by human activities each year¹, yet the persistence of this carbon sink depends partly on how plant biomass and soil organic carbon (SOC) stocks respond to future increases in atmospheric CO₂ (refs. ^2,3). Although plant biomass often increases in elevated CO₂ (eCO₂) experiments^4-6, SOC has been observed to increase, remain unchanged or even decline⁷. The mechanisms that drive this variation across experiments remain poorly understood, creating uncertainty in climate projections^8,9. Here we synthesized data from 108 eCO₂ experiments and found that the effect of eCO₂ on SOC stocks is best explained by a negative relationship with plant biomass: when plant biomass is strongly stimulated by eCO₂, SOC storage declines; conversely, when biomass is weakly stimulated, SOC storage increases. This trade-off appears to be related to plant nutrient acquisition, in which plants increase their biomass by mining the soil for nutrients, which decreases SOC storage. We found that, overall, SOC stocks increase with eCO₂ in grasslands (8 ± 2 per cent) but not in forests (0 ± 2 per cent), even though plant biomass in grasslands increase less (9 ± 3 per cent) than in forests (23 ± 2 per cent). Ecosystem models do not reproduce this trade-off, which implies that projections of SOC may need to be revised.

Using metacommunity ecology to understand environmental metabolomes

Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

Despite growing interest in environmental metabolomics, we lack conceptual frameworks for considering how metabolites vary across space and time in ecological systems. Here, the authors apply (species) community assembly concepts to metabolomics data, offering a way forward in understanding the assembly of metabolite assemblages.

Soil Bacterial and Fungal Richness Forecast Patterns of Early Pine Litter Decomposition

Discovering widespread microbial processes that drive unexpected variation in carbon cycling may improve modeling and management of soil carbon (Prescott, 2010; Wieder et al., 2015a, 2018). A first step is to identify community features linked to carbon cycle variation. We addressed this challenge using an epidemiological approach with 206 soil communities decomposing Ponderosa pine litter in 618 microcosms. Carbon flow from litter decomposition was measured over a 6-week incubation. Cumulative CO2 from microbial respiration varied two-fold among microcosms and dissolved organic carbon (DOC) from litter decomposition varied five-fold, demonstrating large functional variation despite constant environmental conditions where strong selection is expected. To investigate microbial features driving DOC concentration, two microbial community cohorts were delineated as "high" and "low" DOC. For each cohort, communities from the original soils and from the final microcosm communities after the 6-week incubation with litter were taxonomically profiled. A logistic model including total biomass, fungal richness, and bacterial richness measured in the original soils or in the final microcosm communities predicted the DOC cohort with 72 (P < 0.05) and 80 (P < 0.001) percent accuracy, respectively. The strongest predictors of the DOC cohort were biomass and either fungal richness (in the original soils) or bacterial richness (in the final microcosm communities). Successful forecasting of functional patterns after lengthy community succession in a new environment reveals strong historical contingencies. Forecasting future community function is a key advance beyond correlation of functional variance with end-state community features. The importance of taxon richness-the same feature linked to carbon fate in gut microbiome studies-underscores the need for increased understanding of biotic mechanisms that can shape richness in microbial communities independent of physicochemical conditions.

Structure and parameter uncertainty in centennial projections of forest community structure and carbon cycling

Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi-decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand-replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub-model-parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under-predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant-soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED-2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.

Impact of climate, rising atmospheric carbon dioxide, and other environmental factors on water-use efficiency at multiple land cover types

Rising atmospheric CO₂, changing climate, and other environmental factors such as nitrogen deposition and aerosol concentration influence carbon and water fluxes significantly. Water-use efficiency (WUE) was used to analyze these factors over 3 decades (1981–2010) using the Community Land Model 5.0 (CLM5.0). The study analyzes the effects of climate and other environmental factors on multiple land cover types (forest, grassland, and cropland) with divided study periods (1981–2000 and 2001–2010). Ecosystem WUE (EWUE) and transpiration WUE (TWUE) increased at the forest site due to the CO₂ fertilization effect but decreased at the grassland and cropland sites due to lower gross primary production and higher/lower (cropland/grassland) evapotranspiration as consequences of rising temperature and water availability. Inherent WUE confirmed that EWUE and TWUE trends were controlled by the rising temperature and CO₂-induced warming through an increase in vapor pressure deficit. In this way, forest and cropland sites showed warming patterns, while the grassland site showed a drier climate. The later period (2001–2010) showed steeper trends in WUE compared with the earlier period at all sites, implying a change in climate. The results showed implications for rising temperature due to increased CO₂ concentration at multiple land cover types.

A continental scale spatial investigation of lake sediment organic compositions using sedimentomics

Sedimentomics is a new method used to investigate carbon cycling in sediment organic matter. This untargeted method, based on metabolomics workflows, was used to investigate the molecular composition of sediment organic matter across northern Canada (Nunavut and Northwest Territories). Unique "lake districts" were defined using unsupervised clustering based on changes in sediment organic carbon compositions across space. Supervised machine learning analyses were used to compare the "lake districts" to commonly used regional classification systems like the treeline, ecozones, and/or georegions. Treeline was the best model to explain the compositional variance of sediment organic carbon from lakes across Canada, closely followed by the georegions model. A novel sediment metaphenomics analysis was also applied to determine how well environmental constraints explain the variation of sediment organic matter composition across a continent. We determined that sedimentomics is more informative than traditional measurements (such as total organic carbon) and can be integrated with other "omics" techniques.

Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon

First-order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling; these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial-MIneral Carbon Stabilization model), which we compared to the widely used first-order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.

Spatial and temporal turnover of soil microbial communities is not linked to function in a primary tropical forest

The spatial and temporal linkages between turnover of soil microbial communities and their associated functions remain largely unexplored in terrestrial ecosystems. Yet defining these relationships and how they vary across ecosystems and microbial lineages is key to incorporating microbial communities into ecological forecasts and ecosystem models. To define linkages between turnover of soil bacterial and fungal communities and their function we sampled fungal and bacterial composition, abundance, and enzyme activities across a 3-ha area of wet tropical primary forest over 2 yr. We show that fungal and bacterial communities both exhibited temporal turnover, but turnover of both groups was much lower than in temperate ecosystems. Turnover over time was driven by gain and loss of microbial taxa and not changes in abundance of individual species present in multiple samples. Only fungi varied over space with idiosyncratic variation that did not increase linearly with distance among sampling locations. Only phosphorus-acquiring enzyme activities were linked to shifts in septate, decomposer fungal abundance; no enzymes were affected by composition or diversity of fungi or bacteria. Although temporal and spatial variation in composition was appreciable, because turnover of microbial communities did not alter the functional repertoire of decomposing enzymes, functional redundancy among taxa may be high in this ecosystem. Slow temporal turnover of tropical soil microbial communities and large functional redundancy suggests that shifts in abundance of particular functional groups may capture ecosystem function more accurately than composition in these heterogeneous ecosystems.

Microbial taxonomic and functional attributes consistently predict soil CO2 emissions across contrasting croplands

Despite distinct roles of soil microbes in regulating carbon (C) respiration in diverse environments, it remains unclear whether microbial taxonomic and functional attributes can consistently predict soil C emissions across contrasting ecosystems. Here, we conducted a large-scale sampling event across two contrasting croplands (rice and wheat-corn crop rotation) to identify specific soil microbial phylotypes and functional genes associated with soil respiration rates. The results of structural equation modeling indicated that bacterial community composition had a strong link with C respiration rates in the two contrasting cropland types; however, this link was weaker for fungal communities. More importantly, we found that the relative abundances of bacterial Solirubrobacterales_480-2, Myxococcales_mle1-27 and fungal Westerdykella had consistently negative correlation with respiration rates across paddy and upland soils. We also identified taxa that are significantly correlated to C respiration in the paddy (e.g. Methylocaldum) and upland soils (e.g. Kribbella), respectively. Further, we found multiple associations between functional genes involved in microbial C metabolism and soil respiration rates. Our findings provide novel insights into understanding microbial predictors of soil CO₂ emissions in diverse croplands, which have important implications for improving C emission predictions in terrestrial ecosystems.

The global soil community and its influence on biogeochemistry

Soil organisms represent the most biologically diverse community on land and govern the turnover of the largest organic matter pool in the terrestrial biosphere. The highly complex nature of these communities at local scales has traditionally obscured efforts to identify unifying patterns in global soil biodiversity and biogeochemistry. As a result, environmental covariates have generally been used as a proxy to represent the variation in soil community activity in global biogeochemical models. Yet over the past decade, broad-scale studies have begun to see past this local heterogeneity to identify unifying patterns in the biomass, diversity, and composition of certain soil groups across the globe. These unifying patterns provide new insights into the fundamental distribution and dynamics of organic matter on land.

Anthropogenic N deposition, fungal gene expression, and an increasing soil carbon sink in the Northern Hemisphere

Terrestrial ecosystems in the Northern Hemisphere are a globally important sink for anthropogenic CO₂ in the Earth's atmosphere, slowing its accumulation as well as the pace of climate warming. With the use of a long-term field experiment (ca. 20 yr), we show that the expression of fungal class II peroxidase genes, which encode enzymes mediating the rate-limiting step of organic matter decay, are significantly downregulated (-60 to -80%) because of increases in anthropogenic N deposition; this response was consistent with a decline in extracellular peroxidase enzyme activity in soil, the slowing of organic-matter decay, and greater soil C storage. The reduction in peroxidase expression we document here occurred in the absence of a compositional shift in metabolically active fungi, indicating that an overall reduction in peroxidase expression underlies the slowing of decay and increases in soil C storage. This molecular mechanism has global implications for soil C storage and should be represented in coupled climate-biogeochemical models simulating the influence of enhanced terrestrial C storage on atmospheric CO₂ and the future climate of an N-enriched Earth.

Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation

Climate warming may stimulate microbial metabolism of soil carbon, causing a carbon-cycle-climate feedback whereby carbon is redistributed from the soil to atmospheric CO₂. The magnitude of this feedback is uncertain, in part because warming-induced shifts in microbial physiology and/or community composition could retard or accelerate soil carbon losses. Here, we measure microbial respiration rates for soils collected from 22 sites in each of 3 years, at locations spanning boreal to tropical climates. Respiration was measured in the laboratory with standard temperatures, moisture and excess carbon substrate, to allow physiological and community effects to be detected independent of the influence of these abiotic controls. Patterns in respiration for soils collected across the climate gradient are consistent with evolutionary theory on physiological responses that compensate for positive effects of temperature on metabolism. Respiration rates per unit microbial biomass were as much as 2.6 times higher for soils sampled from sites with a mean annual temperature of -2.0 versus 21.7 °C. Subsequent 100-d incubations suggested differences in the plasticity of the thermal response among microbial communities, with communities sampled from sites with higher mean annual temperature having a more plastic response. Our findings are consistent with adaptive metabolic responses to contrasting thermal regimes that are also observed in plants and animals. These results may help build confidence in soil-carbon-climate feedback projections by improving understanding of microbial processes represented in biogeochemical models.

Multi 'omics comparison reveals metabolome biochemistry, not microbiome composition or gene expression, corresponds to elevated biogeochemical function in the hyporheic zone

Biogeochemical hotspots are pervasive at terrestrial-aquatic interfaces, particularly within groundwater-surface water mixing zones (hyporheic zones), and they are critical to understanding spatiotemporal variation in biogeochemical cycling. Here, we use multi 'omic comparisons of hotspots to low-activity sediments to gain mechanistic insight into hyporheic zone organic matter processing. We hypothesized that microbiome structure and function, as described by metagenomics and metaproteomics, would distinguish hotspots from low-activity sediments by shifting metabolism towards carbohydrate-utilizing pathways and elucidate discrete mechanisms governing organic matter processing in each location. We also expected these differences to be reflected in the metabolome, whereby hotspot carbon (C) pools and metabolite transformations therein would be enriched in sugar-associated compounds. In contrast to expectations, we found pronounced phenotypic plasticity in the hyporheic zone microbiome that was denoted by similar microbiome structure, functional potential, and expression across sediments with dissimilar metabolic rates. Instead, diverse nitrogenous metabolites and biochemical transformations characterized hotspots. Metabolomes also corresponded more strongly to aerobic metabolism than bulk C or N content only (explaining 67% vs. 42% and 37% of variation respectively), and bulk C and N did not improve statistical models based on metabolome composition alone. These results point to organic nitrogen as a significant regulatory factor influencing hyporheic zone organic matter processing. Based on our findings, we propose incorporating knowledge of metabolic pathways associated with different chemical fractions of C pools into ecosystem models will enhance prediction accuracy.