Microbial carbon use efficiency promotes global soil carbon storage.

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Modelling optimal ligninolytic activity during plant litter decomposition.

A large fraction of plant litter comprises recalcitrant aromatic compounds (lignin and other phenolics). Quantifying the fate of aromatic compounds is difficult, because oxidative degradation of aromatic carbon (C) is a costly but necessary endeavor for microorganisms, and we do not know when gains from the decomposition of aromatic C outweigh energetic costs. To evaluate these tradeoffs, we developed a litter decomposition model in which the aromatic C decomposition rate is optimized dynamically to maximize microbial growth for the given costs of maintaining ligninolytic activity. We tested model performance against > 200 litter decomposition datasets collected from published literature and assessed the effects of climate and litter chemistry on litter decomposition. The model predicted a time-varying ligninolytic oxidation rate, which was used to calculate the lag time before the decomposition of aromatic C is initiated. Warmer conditions increased decomposition rates, shortened the lag time of aromatic C oxidation, and improved microbial C-use efficiency by decreasing the costs of oxidation. Moreover, a higher initial content of aromatic C promoted an earlier start of aromatic C decomposition under any climate. With this contribution, we highlight the application of eco-evolutionary approaches based on optimized microbial life strategies as an alternative parametrization scheme for litter decomposition models.

Temperature controls the relation between soil organic carbon and microbial carbon use efficiency.

Microbial carbon use efficiency (CUE) is an important variable mediating microbial effects on soil organic carbon (SOC) since it summarizes how much carbon is used for microbial growth or is respired. Yet, the role of CUE in regulating SOC storage remains debated, with evidence for both positive and negative SOC-CUE relations. Here, we use a combination of measured data around the world and numerical simulations to explore SOC-CUE relations accounting for temperature (T) effects on CUE. Results reveal that the sign of the CUE-T relation controls the direction of the SOC-CUE relations. A negative CUE-T relation leads to a positive SOC-CUE relation and vice versa, highlighting that CUE-T patterns significantly affect how organic carbon is used by microbes and hence SOC-CUE relations. Numerical results also confirm the observed negative SOC-T relation, regardless of the CUE-T patterns, implying that temperature plays a more dominant role than CUE in controlling SOC storage. The SOC-CUE relation is usually negative when temperature effects are isolated, even though it can become positive when nonlinear microbial turnover is considered. These results indicate a dominant role of CUE-T patterns in controlling the SOC-CUE relation. Our findings help to better understand SOC and microbial responses to a warming climate.

Recovery of Soil Microbial Metabolism After Rewetting Depends on Interacting Environmental Conditions and Changes in Functional Groups and Life History Strategies.

Climate change is causing an intensification of soil drying and rewetting events, altering microbial functioning and potentially destabilizing soil organic carbon. After rewetting, changes in microbial community carbon use efficiency (CUE), investment in life history strategies, and fungal to bacterial dominance co-occur. Still, we have yet to generalize what drives these dynamic responses. Here, we collated 123 time series of microbial community growth (G, sum of fungal and bacterial growth, evaluated by leucine and acetate incorporation, respectively) and respiration (R) after rewetting and calculated CUE = G/(G + R). First, we characterized CUE recovery by two metrics: maximum CUE and time to maximum CUE. Second, we translated microbial growth and respiration data into microbial investments in life history strategies (high yield (Y), resource acquisition (A), and stress tolerance (S)). Third, we characterized the temporal change in fungal to bacterial dominance. Finally, the metrics describing the CUE recovery, investment in life history strategies, and fungal to bacterial dominance after rewetting were explained by environmental factors and microbial properties. CUE increased after rewetting as fungal dominance declined, but the maximum CUE was explained by the CUE under moist conditions, rather than specific environmental factors. In contrast, higher soil pH and carbon availability accelerated the decline of microbial investment in stress tolerance and fungal dominance. We conclude that microbial CUE recovery is mostly driven by the shifting microbial community composition and the metabolic capacity of the community, whereas changes in microbial investment in life history strategies and fungal versus bacterial dominance depend on soil pH and carbon availability.

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.

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.

Meta-analysis reveals that the effects of precipitation change on soil and litter fauna in forests depend on body size.

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

Energetic scaling in microbial growth.

Microbial growth is a clear example of organization and structure arising in nonequilibrium conditions. Due to the complexity of the microbial metabolic network, elucidating the fundamental principles governing microbial growth remains a challenge. Here, we present a systematic analysis of microbial growth thermodynamics, leveraging an extensive dataset on energy-limited monoculture growth. A consistent thermodynamic framework based on reaction stoichiometry allows us to quantify how much of the available energy microbes can efficiently convert into new biomass while dissipating the remaining energy into the environment and producing entropy. We show that dissipation mechanisms can be linked to the electron donor uptake rate, a fact leading to the central result that the thermodynamic efficiency is related to the electron donor uptake rate by the scaling law [Formula: see text] and to the growth yield by [Formula: see text] These findings allow us to rederive the Pirt equation from a thermodynamic perspective, providing a means to compute its coefficients, as well as a deeper understanding of the relationship between growth rate and yield. Our results provide rather general insights into the relation between mass and energy conversion in microbial growth with potentially wide application, especially in ecology and biotechnology.

Natural forests promote phosphorus retention in soil.

Soil phosphorus (P) availability often limits plant productivity. Classical theories suggest that total P content declines at the temporal scale of pedogenesis, and ecosystems develop toward the efficient use of scarce P during succession. However, the trajectory of ecosystem P within shorter time scales of succession remains unclear. We analyzed changes to P pools at the early (I), middle (II), and late (III) stages of growth of plantation forests (PFs) and the successional stages of natural forests (NFs) at 1969 sites in China. We found significantly lower P contents at later growth stages compared to earlier ones in the PF (p < .05), but higher contents at late successional stages than in earlier stages in the NF (p < .05). Our results indicate that increasing P demand of natural vegetation during succession, may raise, retain, and accumulate P from deeper soil layers. In contrast, ecosystem P in PF was depleted by the more rapidly increasing demand outpacing the development of a P-efficient system. We advocate for more studies to illuminate the mechanisms for determining the divergent changes, which would improve forest management and avoid the vast degradation of PF ecosystems suffering from the ongoing depletion of P.

The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs.

Aim: Our goal was to quantify nitrogen flows and stocks in green-brown food webs in different ecosystems, how they differ across ecosystems and how they respond to nutrient enrichment. Location: Global. Time period: Contemporary. Major taxa studied: Plants, phytoplankton, macroalgae, invertebrates, vertebrates and zooplankton. Methods: Data from >500 studies were combined to estimate nitrogen stocks and fluxes in green-brown food webs in forests, grasslands, brackish environments, seagrass meadows, lakes and oceans. We compared the stocks, fluxes and metabolic rates of different functional groups within each food web. We also used these estimates to build a dynamical model to test the response of the ecosystems to nutrient enrichment. Results: We found surprising symmetries between the green and brown channels across ecosystems, in their stocks, fluxes and consumption coefficients and mortality rates. We also found that nitrogen enrichment, either organic or inorganic, can disrupt this balance between the green and brown channels. Main conclusions: Linking green and brown food webs reveals a previously hidden symmetry between herbivory and detritivory, which appears to be a widespread property of natural ecosystems but can be disrupted by anthropogenic nitrogen additions.

Spatial Control of Microbial Pesticide Degradation in Soil: A Model-Based Scenario Analysis.

Microbial pesticide degraders are heterogeneously distributed in soil. Their spatial aggregation at the millimeter scale reduces the frequency of degrader-pesticide encounter and can introduce transport limitations to pesticide degradation. We simulated reactive pesticide transport in soil to investigate the fate of the widely used herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) in response to differently aggregated distributions of degrading microbes. Four scenarios were defined covering millimeter scale heterogeneity from homogeneous (pseudo-1D) to extremely heterogeneous degrader distributions and two precipitation scenarios with either continuous light rain or heavy rain events. Leaching from subsoils did not occur in any scenario. Within the topsoil, increasing spatial heterogeneity of microbial degraders reduced macroscopic degradation rates, increased MCPA leaching, and prolonged the persistence of residual MCPA. In heterogeneous scenarios, pesticide degradation was limited by the spatial separation of degrader and pesticide, which was quantified by the spatial covariance between MCPA and degraders. Heavy rain events temporarily lifted these transport constraints in heterogeneous scenarios and increased degradation rates. Our results indicate that the mild millimeter scale spatial heterogeneity of degraders typical for arable topsoil will have negligible consequences for the fate of MCPA, but strong clustering of degraders can delay pesticide degradation.

A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model.

Axially loaded beam-like structures represent a challenging case study for unsupervised learning vibration-based damage detection. Under real environmental and operational conditions, changes in axial load cause changes in the characteristics of the dynamic response that are significantly greater than those due to damage at an early stage. In previous works, the authors proposed the adoption of a multivariate damage feature composed of eigenfrequencies of multiple vibration modes. Successful results were obtained by framing the problem of damage detection as that of unsupervised outlier detection, adopting the well-known Mahalanobis squared distance (MSD) to define an effective damage index. Starting from these promising results, a novel approach based on unsupervised learning data clustering is proposed in this work, which increases the sensitivity to damage and significantly reduces the uncertainty associated with the results, allowing for earlier damage detection. The novel approach, which is based on Gaussian mixture model, is compared with the benchmark one based on the MSD, under the effects of an uncontrolled environment and, most importantly, in the presence of real damage due to corrosion.

Automatic Detection of Real Damage in Operating Tie-Rods.

Many researchers have proposed vibration-based damage-detection approaches for continuous structural health monitoring. Translation to real applications is not always straightforward because the proposed methods have mostly been developed and validated in controlled environments, and they have not proven to be effective in detecting real damage when considering real scenarios in which environmental and operational variations are not controlled. This work was aimed to develop a fully-automated strategy to detect damage in operating tie-rods that only requires one sensor and that can be carried out without knowledge of physical variables, e.g., the axial load. This strategy was created by defining a damage feature based on tie-rod eigenfrequencies and developing a data-cleansing strategy that could significantly improve performance of outlier detection based on the Mahalanobis squared distance in real applications. Additionally, the majority of damage-detection algorithms presented in the literature related to structural health monitoring were validated in controlled environments considering simulated damage conditions. On the contrary, the approach proposed in this paper was shown to allow for the early detection of real damage associated with a corrosion attack under the effects of an intentionally uncontrolled environment.

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

Modelling water fluxes in plants: from tissues to biosphere.

Contents Summary 1207 I. Introduction 1207 II. A brief history of modelling plant water fluxes 1208 III. Main components of plant water transport models 1208 IV. Stand-scale water fluxes and coupling to climate and soil 1213 V. Water fluxes in terrestrial biosphere models and feedbacks to community dynamics 1215 VI. Outstanding challenges in modelling water fluxes in the soil-plant-atmosphere continuum 1217 Acknowledgements 1218 References 1218 SUMMARY: Models of plant water fluxes have evolved from studies focussed on understanding the detailed structure and functioning of specific components of the soil-plant-atmosphere (SPA) continuum to architectures often incorporated inside eco-hydrological and terrestrial biosphere (TB) model schemes. We review here the historical evolution of this field, examine the basic structure of a simplified individual-based model of plant water transport, highlight selected applications for specific ecological problems and conclude by examining outstanding issues requiring further improvements in modelling vegetation water fluxes. We particularly emphasise issues related to the scaling from tissue-level traits to individual-based predictions of water transport, the representation of nonlinear and hysteretic behaviour in soil-xylem hydraulics and the need to incorporate knowledge of hydraulics within broader frameworks of plant ecological strategies and their consequences for predicting community demography and dynamics.

Leaf economics and plant hydraulics drive leaf : wood area ratios.

Biomass and area ratios between leaves, stems and roots regulate many physiological and ecological processes. The Huber value Hv (sapwood area/leaf area ratio) is central to plant water balance and drought responses. However, its coordination with key plant functional traits is poorly understood, and prevents developing trait-based prediction models. Based on theoretical arguments, we hypothesise that global patterns in Hv of terminal woody branches can be predicted from variables related to plant trait spectra, that is plant hydraulics and size and leaf economics. Using a global compilation of 1135 species-averaged Hv , we show that Hv varies over three orders of magnitude. Higher Hv are seen in short small-leaved low-specific leaf area (SLA) shrubs with low Ks in arid relative to tall large-leaved high-SLA trees with high Ks in moist environments. All traits depend on climate but climatic correlations are stronger for explanatory traits than Hv . Negative isometry is found between Hv and Ks , suggesting a compensation to maintain hydraulic supply to leaves across species. This work identifies the major global drivers of branch sapwood/leaf area ratios. Our approach based on widely available traits facilitates the development of accurate models of above-ground biomass allocation and helps predict vegetation responses to drought.

Can leaf net photosynthesis acclimate to rising and more variable temperatures?

Under future climates, leaf temperature (Tl ) will be higher and more variable. This will affect plant carbon (C) balance because photosynthesis and respiration both respond to short-term (subdaily) fluctuations in Tl and acclimate in the longer term (days to months). This study asks the question: To what extent can the potential and speed of photosynthetic acclimation buffer leaf C gain from rising and increasing variable Tl ? We quantified how increases in the mean and variability of growth temperature affect leaf performance (mean net CO2 assimilation rates, Anet ; its variability; and time under near-optimal photosynthetic conditions), as mediated by thermal acclimation. To this aim, the probability distribution of Anet was obtained by combining a probabilistic description of short- and long-term changes in Tl with data on Anet responses to these changes, encompassing 75 genera and 111 species, including both C3 and C4 species. Our results show that (a) expected increases in Tl variability will decrease mean Anet and increase its variability, whereas the effects of higher mean Tl depend on species and initial Tl , and (b) acclimation reduces the effects of leaf warming, maintaining Anet at >80% of its maximum under most thermal regimes.

Beyond isohydricity: The role of environmental variability in determining plant drought responses.

Despite the appeal of the iso/anisohydric framework for classifying plant drought responses, recent studies have shown that such classifications can be strongly affected by a plant's environment. Here, we present measured in situ drought responses to demonstrate that apparent isohydricity can be conflated with environmental conditions that vary over space and time. In particular, we (a) use data from an oak species (Quercus douglasii) during the 2012-2015 extreme drought in California to demonstrate how temporal and spatial variability in the environment can influence plant water potential dynamics, masking the role of traits; (b) explain how these environmental variations might arise from climatic, topographic, and edaphic variability; (c) illustrate, through a "common garden" thought experiment, how existing trait-based or response-based isohydricity metrics can be confounded by these environmental variations, leading to Type-1 (false positive) and Type-2 (false negative) errors; and (d) advocate for the use of model-based approaches for formulating alternate classification schemes. Building on recent insights from greenhouse and vineyard studies, we offer additional evidence across multiple field sites to demonstrate the importance of spatial and temporal drivers of plants' apparent isohydricity. This evidence challenges the use of isohydricity indices, per se, to characterize plant water relations at the global scale.

The ecohydrological context of drought and classification of plant responses.

Many recent studies on drought-induced vegetation mortality have explored how plant functional traits, and classifications of such traits along axes of, for example, isohydry-anisohydry, might contribute to predicting drought survival and recovery. As these studies proliferate, the consistency and predictive value of such classifications need to be carefully examined. Here, we outline the basis for a systematic classification of plant drought responses that accounts for both environmental conditions and functional traits. We use non-dimensional analysis to integrate plant traits and metrics of environmental variation into groups that can be associated with alternative drought stress pathways (hydraulic failure and carbon limitation), and demonstrate that these groupings predict physiological drought outcomes using both synthetic and measured data. In doing so, we aim to untangle some confounding effects of environment and trait variations that undermine current classification schemes, advocate for more careful treatment of the environmental context within which plants experience and respond to drought, and outline a pathway towards a general classification of drought vulnerability.