Carbon Cycle Research Articles

Fungi: friends or foes-an outreach science initiative for the collection of airborne fungal spores by high school students.

Fungi mostly reproduce through spores that are adapted for airborne dispersal; hence, fungal spores (and fungi) are found virtually everywhere. Fungi can be "friends or foes." Our friends include fungi used in the food and biotech industries, fungi that contribute to the cycling of carbon and nutrients, and those involved in the decontamination of polluted soils and/or water, to mention just a few examples. Many species, however, are foes-they are detrimental to plants, animals, and/or humans. Annually, >1.5 million people die due to invasive fungal infections. With the aim of enhancing microbiology literacy and the understanding of microbial concepts, we set up a project for the collection of airborne spores (the principal agent through which human airways are exposed to fungi). Students from five high schools in the Oeiras municipality partnered with us as citizen scientists; they carried out sampling by collecting fungal spores on adhesive stickers. The fungal spores collected by the students were subsequently processed in the schools and our research laboratory. Results obtained by the students themselves revealed a large variety of fungal species capable of growing in a rich medium at 30°C. In the research laboratory, using selective isolation conditions, 40 thermotolerant fungi were isolated, 32 of which were taxonomically identified as aspergilla, mostly from within the Aspergillus fumigatus taxa, yet exhibiting high genetic heterogeneity. The protocols and results were presented to the students, who were made aware of the local dispersal of airborne fungal spores, including some from potentially pathogenic fungi. Through carrying out scientific activities, the students developed both the interest and the self-confidence needed to implement future environmental investigations.

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.

Organic carbon sink dynamics and carbon sink-source balance in global lakes during the Anthropocene

The carbon burial and greenhouse gas emissions of lakes are pivotal in the global carbon cycle to offset or accelerate global warming. However, their balance or the magnitude of anthropogenic increase of carbon burial remains uncertain in global lakes. Here, we quantified the carbon sink dynamics and the sink-source balance in global lakes with effect-size metrics, that is, the log-response ratio of organic carbon burial between post-1950 and pre-1900 periods and the carbon balance ratio between carbon burial and greenhouse gas emissions, respectively. The organic carbon burial ratios revealed an average increase of 2.45 times in carbon burial rates during the Anthropocene, while the carbon balance ratios were negative in 82.68% of lakes, indicating that most lakes had lower burial rates than emission rates and acted as carbon sources rather than carbon sinks. The organic carbon burial ratios exhibited a significantly decreasing latitudinal trend and were mainly influenced by lake's trophic state with the explained variation of 44.79%. They were also indirectly influenced by climate, lake morphometry and catchment properties through their interactions with the lake's trophic state. The carbon balance ratios, however, showed a nonsignificant latitudinal trend. They were primarily affected by lake catchment properties with the explained variation of 26.21% and were also indirectly affected by climate variables via the interactions with catchment properties. Overall, our study highlights that human activities such as lake eutrophication and catchment changes have altered the carbon sink and source in global lakes during the Anthropocene, and are essential drivers for future evaluations of lake carbon budgets.

A US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy.

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. We discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

Reading tea leaves worldwide: Decoupled drivers of initial litter decomposition mass-loss rate and stabilization.

The breakdown of plant material fuels soil functioning and biodiversity. Currently, process understanding of global decomposition patterns and the drivers of such patterns are hampered by the lack of coherent large-scale datasets. We buried 36,000 individual litterbags (tea bags) worldwide and found an overall negative correlation between initial mass-loss rates and stabilization factors of plant-derived carbon, using the Tea Bag Index (TBI). The stabilization factor quantifies the degree to which easy-to-degrade components accumulate during early-stage decomposition (e.g. by environmental limitations). However, agriculture and an interaction between moisture and temperature led to a decoupling between initial mass-loss rates and stabilization, notably in colder locations. Using TBI improved mass-loss estimates of natural litter compared to models that ignored stabilization. Ignoring the transformation of dead plant material to more recalcitrant substances during early-stage decomposition, and the environmental control of this transformation, could overestimate carbon losses during early decomposition in carbon cycle models.

Estimation aboveground biomass in subtropical bamboo forests based on an interpretable machine learning framework

Forest biomass is an essential indicator of forest ecosystem carbon cycle and global climate change research, and traditional machine learning cannot explain the mechanism of feature variable impact on forest aboveground biomass (AGB). Therefore, we proposed an interpretable bamboo forest AGB prediction method based on Shaply Additive exPlanation (SHAP) and XGBoost model to explain the impact mechanism of feature variables on AGB. The bamboo forest AGB is estimated using the monthly and annual scale leaf area index (LAI), enhanced vegetation index (EVI), ratio vegetation index (RVI), precipitation (Pre), maximum temperature (Tmax), minimum temperature (Tmin) and solar radiation (Rad) data. The results showed that the method could be effectively predict AGB, and precipitation more important than temperature. The framework revealed the threshold effect, exceeded the threshold value, the impacts of LAI_Ann, EVI_Ann, and Pre_11 on AGB were stable. The SHAP interaction value between LAI_Ann and EVI_Ann decreased with increasing EVI_Ann and LAI_Ann. By contrast, when Pre_11 increased, the SHAP interaction value between LAI_Ann and Pre_11 increased with increasing LAI_Ann. The framework could also be easily implemented, providing an interpretable machine learning model of forest AGB.

Prediction of soil organic carbon stock combining Sentinel-1 and Sentinel-2 images in the Zoige Plateau, the northeastern Qinghai-Tibet Plateau

Prediction of soil organic carbon stock combining Sentinel-1 and Sentinel-2 images in the Zoige Plateau, the northeastern Qinghai-Tibet Plateau

Linking Dissolved Organic Matter Composition to Landscape Properties in Wetlands Across the United States of America

AbstractWetlands are integral to the global carbon cycle, serving as both a source and a sink for organic carbon. Their potential for carbon storage will likely change in the coming decades in response to higher temperatures and variable precipitation patterns. We characterized the dissolved organic carbon (DOC) and dissolved organic matter (DOM) composition from 12 different wetland sites across the USA spanning gradients in climate, landcover, sampling depth, and hydroperiod for comparison to DOM in other inland waters. Using absorption spectroscopy, parallel factor analysis modeling, and ultra‐high resolution mass spectroscopy, we identified differences in DOM sourcing and processing by geographic site. Wetland DOM composition was driven primarily by differences in landcover where forested sites contained greater aromatic and oxygenated DOM content compared to grassland/herbaceous sites which were more aliphatic and enriched in N and S molecular formulae. Furthermore, surface and porewater DOM was also influenced by properties such as soil type, organic matter content, and precipitation. Surface water DOM was relatively enriched in oxygenated higher molecular weight formulae representing HUPHigh O/C compounds than porewaters, whose DOM composition suggests abiotic sulfurization from dissolved inorganic sulfide. Finally, we identified a group of persistent molecular formulae (3,489) present across all sites and sampling depths (i.e., the signature of wetland DOM) that are likely important for riverine‐to‐coastal DOM transport. As anthropogenic disturbances continue to impact temperate wetlands, this study highlights drivers of DOM composition fundamental for understanding how wetland organic carbon will change, and thus its role in biogeochemical cycling.

Warming the MATRIX: Uncertainty and heterogeneity in climate change impacts and policy targets in the Euro Area

This paper explores the potential impacts of climate change and mitigation policies in the Euro Area, considering the uncertainty and heterogeneity in both climate and economic systems. Using the MATRIX model, a multi-sector and multi-agent macroeconomic model, we simulate various climate scenarios by employing different carbon cycle models, damage functions, and marginal abatement curves found in the literature. We find that heterogeneous climate damages amplify both the magnitude and the volatility of GDP losses associated with global warming. By the end of the century, we estimate that assuming homogeneous shocks may underestimate the effects of climate change on aggregate output by up to one-third. Moreover, we find that the speed and feasibility of a low-carbon transition crucially depend on (i) the ambition in emission reduction targets set by the policymaker, which determine the level of a carbon tax, and (ii) the rate of technological progress, which influences the shape of the abatement cost curve.

Improving MODIS Gross Primary Productivity by Bridging Big‐Leaf and Two‐Leaf Light Use Efficiency Models

AbstractGross primary productivity (GPP) is an important component of the terrestrial carbon cycle in climate change research. The global GPP product derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data is perhaps the most widely used. Unfortunately, many studies have indicated evident error patterns in the MODIS GPP product. One of the main reasons for this is that the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. In this study, we developed a model for correcting the errors in the MODIS GPP product by bridging BL and two‐leaf (TL) light use efficiency (LUE) models (CTL‐MOD17). With the available MODIS GPP product, which considers environmental stress factors, the CTL‐MOD17 model only needs to reuse the two inputs of the leaf area index (LAI) and incoming radiation. The CTL‐MOD17 model was calibrated and validated at 153 global FLUXNET eddy covariance (EC) sites. The results indicate that the modeled GPP obtained with the correction model matches better with the EC GPP than the original MODIS GPP product at different time scales, with an improvement of 0.07 in R2 and a reduction in root‐mean‐square error (RMSE) of 117.08 g C m−2 year−1. The improvements are more significant in the green season when the contribution of shaded leaves is larger. In terms of the global spatial pattern, the obvious underestimation in the regions with high LAI and the overestimation in the low LAI regions of the MODIS GPP product is effectively corrected by the CTL‐MOD17 model. This paper not only bridges the BL and TL LUE models, but also provides a new and simple method to obtain accurate GPP through reusing two inputs used in producing the MODIS GPP product.

The ecology of the sewer systems: Microbial composition, function, assembly, and network in different spatial locations

Microbial induced concrete corrosion (MICC) is the primary deterioration affecting global sewers. Disentangling ecological mechanisms in the sewer system is meaningful for implementing policies to protect sewer pipes using trenchless technology. It is necessary to understand microbial compositions, interaction networks, functions, alongside assembly processes in sewer microbial communities. In this study, sewer wastewater samples and microbial samples from the upper part (UP), middle part (MP) and bottom part (BP) of different pipes were collected for 16S rRNA gene amplicon analysis. It was found that BP harbored distinct microbial communities and the largest proportion of unique species (1141) compared to UP and MP. The community in BP tended to be more clustered. Furthermore, significant differences in microbial functions existed in different spatial locations, including the carbon cycle, nitrogen cycle and sulfur cycle. Active microbial sulfur cycling indicated the corrosion risk of MICC. Among the environmental factors, the oxidation‒reduction potential drove changes in BP, while sulfate managed changes in UP and BP. Stochasticity dominated community assembly in the sewer system. Additionally, the sewer microbial community exhibited numerous positive links. BP possessed a more complex, modular network with higher modularity. These deep insights into microbial ecology in the sewer system may guide engineering safety and disaster prevention in sewer infrastructure.

Planktic foraminifer response and paleoceanographic changes across the Danian – Selandian interval in the southern Tethys (Elles section, Central Tunisia)

The Danian – Selandian interval experienced several paleoceanographic changes with two prominent carbon cycle perturbations: the Dan-C2 and Latest Danian Event (LDE). We present integrated planktic foraminifer biostratigraphy, carbon isotope stratigraphy, and paleoceanographic interpretations from the Danian-Selandian at Elles, central Tunisia. Whereas the response of the planktic foraminifers to these events has been widely explored in deep sea sites, this shallow Tethyan shelf setting is significant for evaluating the expression of these global events in proximal environments. Both the Dan-C2 and LDE events are distinguished in our δ13Cbenthic record by biostratigraphically-constrained negative carbon isotope excursions. Recognition of these reliably dated excursions enables assessment of bioevent synchroneity. The lowest occurrence of Subbotina triloculinoides was diachronous at DanC2, but the highest occurrence of Praemurica was broadly synchronous at the onset of the LDE. Subsequent lowest occurrence of Igorina albeari shows latitudinal diachroneity between ocean basins. The planktic foraminifer assemblage composition indicates mesotrophic and stratified conditions in the early Danian, warming and increasing oligotrophy during the P2 Zone, peak oligotrophy and enhanced stratification across the LDE, shallowing-induced disruption of the upper water column niches in the latest Danian, and recovery with high diversity in the early Selandian. The Dan-C2 excursion shows a small 0.3‰ negative δ13C shift coinciding with increased abundance of low oxygen tolerant biserial Chiloguembelina, suggesting slightly stressed conditions. In contrast, the larger 0.6‰ LDE excursion coincides with the extinction of Praemurica and proliferation of Morozovella, reflecting an oligotrophic surge with warming in the surface ocean.

It's time to broaden what we consider a 'blue carbon ecosystem'.

Photoautotrophic marine ecosystems can lock up organic carbon in their biomass and the associated organic sediments they trap over millennia and are thus regarded as blue carbon ecosystems. Because of the ability of marine ecosystems to lock up organic carbon for millennia, blue carbon is receiving much attention within the United Nations' 2030 Agenda for Sustainable Development as a nature-based solution (NBS) to climate change, but classically still focuses on seagrass meadows, mangrove forests, and tidal marshes. However, other coastal ecosystems could also be important for blue carbon storage, but remain largely neglected in both carbon cycling budgets and NBS strategic planning. Using a meta-analysis of 253 research publications, we identify other coastal ecosystems-including mud flats, fjords, coralline algal (rhodolith) beds, and some components or coral reef systems-with a strong capacity to act as blue carbon sinks in certain situations. Features that promote blue carbon burial within these 'non-classical' blue carbon ecosystems included: (1) balancing of carbon release by calcification via carbon uptake at the individual and ecosystem levels; (2) high rates of allochthonous organic carbon supply because of high particle trapping capacity; (3) high rates of carbon preservation and low remineralization rates; and (4) location in depositional environments. Some of these features are context-dependent, meaning that these ecosystems were blue carbon sinks in some locations, but not others. Therefore, we provide a universal framework that can evaluate the likelihood of a given ecosystem to behave as a blue carbon sink for a given context. Overall, this paper seeks to encourage consideration of non-classical blue carbon ecosystems within NBS strategies, allowing more complete blue carbon accounting.

The changes in soil organic carbon stock and quality across a subalpine forest successional series

Soil organic carbon (SOC) affects the function of terrestrial ecosystem and plays a vital role in global carbon cycle. Yet, large uncertainty still existed regarding the changes in SOC stock and quality with forest succession. Here, the stock and quality of SOC at 1 m soil profile were investigated across a subalpine forest series, including shrub, deciduous broad-leaved forest, broadleaf-conifer mixed forest, middle-age coniferous forest and mature coniferous forest, which located at southeast of Tibetan Plateau. The results showed that SOC stock ranged from 9.8 to 29.9 kg·m–2, and exhibited a hump-shaped response pattern across the forest successional series. The highest and lowest SOC stock was observed in the mixed forest and shrub forest, respectively. The SOC stock had no significant relationships with soil temperature and litter stock, but was positively correlated with wood debris stock. Meanwhile, the average percentages of polysaccharides, lignins, aromatics and aliphatics based on FTIR spectroscopy were 79.89%, 0.94%, 18.87% and 0.29%, respectively. Furthermore, the percentage of polysaccharides exhibited an increasing pattern across the forest successional series except for the sudden decreasing in the mixed forest, while the proportions of lignins, aromatics and aliphatics exhibited a decreasing pattern across the forest successional series except for the sudden increasing in the mixed forest. Consequently, the humification indices (HIs) were highest in the mixed forest compared to the other four successional stages, which means that the SOC quality in mixed forest was worse than other successional stages. In addition, The SOC stock, recalcitrant fractions and HIs decreased with increasing soil depth, while the polysaccharides exhibited an increasing pattern. These findings demonstrate that the mixed forest had higher SOC stock and worse SOC quality than other successional stages. The high proportion of SOC stock (66% at depth of 20–100 cm) and better SOC quality (lower HIs) indicate that deep soil have tremendous potential to store SOC and needs more attention under global change.

Magnitude of and Hydroclimatic Controls on CO2 and CH4 Emissions in the Subtropical Monsoon Pearl River Basin

AbstractRivers are important ecosystems for carbon emissions and play a crucial role in the global carbon cycle. However, CO2 and CH4 emissions from subtropical rivers are substantially under‐represented in global‐scale estimates. Here, we explored the regional patterns of riverine CO2 and CH4 dynamics in the Pearl River basin with a subtropical monsoon climate. We found that its CO2 and diffusive CH4 emissions showed a decreasing trend with increasing stream order. Seasonality in CO2 and diffusive CH4 emissions was primarily driven by variations in partial pressure of CO2 (pCO2) and CH4 (pCH4) and gas transfer velocities, which were strongly regulated by hydrology and climate. We further estimated the basin‐wide CO2 and diffusive CH4 fluxes at 17.8 ± 7.4 Tg C yr−1 and 191.5 ± 139.9 Gg C yr−1, respectively. When normalized to the water surface, the mean diffusive fluxes were 790.1 and 8.5 mmol m−2 d−1 for CO2 and CH4, respectively, which were 1.3 and 2.5 times higher than the global mean riverine CO2 and CH4 fluxes, respectively. This suggests that the global significance of subtropical rivers is probably underestimated because their substantially higher CH4 fluxes are unaccounted for. Furthermore, compared with measured pCO2, the alkalinity‐based pCO2 could introduce significant errors by 20% at ∼30% of the sampling sites, underscoring the necessity of direct measurements to reduce uncertainty. This study provides the first estimate of basin‐wide CO2 and diffusive CH4 emissions in the PRB through direct pCO2 and pCH4 measurements, and highlights the role of hydrologic and climatic factors in governing riverine carbon emissions.

The influence patterns of carbon flux in different climatic zones in China —Based on the complex network approach

Research on ecosystem carbon flux can provide important methodological and strategic support for climate change mitigation. The existing studies focus on the calculation of carbon flux, ignoring the intertwined effects between regions. The quantification and analysis of the interaction patterns of carbon flux is crucial for understanding the global carbon cycle process, forecasting and coping with climate change. In this study, carbon flux network model sequences are established based on complex network theory using carbon flux data spanning from December 1, 2005, to November 30, 2020. The time delay effect is introduced to accurately quantify the influence patterns of carbon flux within climate zones across China. The findings indicate that the probability distribution function of the link weights during the various seasons of each year exhibits a bimodal distribution with distinct positive and negative components. The delay probability distribution function reveals the significant impact of delay effects, which are especially pronounced and mostly significant long-term lag effects in nodes with negative weights. Further, the results of the interactions of carbon flux among climate zones demonstrate that changes in carbon flux in the plateau and southern temperate regions have either positive or negative impacts on other climate zones. Therefore, controlling carbon flux changes in these climatic zones can effectively optimize the distribution of carbon flux. The modeling framework and results presented in this paper provide useful insights for the regulation and distribution optimization of carbon flux in China.

Carbon Stocks and Fluxes From a Boreal Conifer Swamp: Filling a Knowledge Gap for Understanding the Boreal C Cycle

AbstractThe carbon (C) dynamics of boreal coniferous swamps are a largely understudied component of wetland carbon cycling. We investigated the above‐ and below‐ground carbon stocks and growing season carbon dioxide (CO2) and methane (CH4) fluxes from a representative boreal coniferous swamp in northern Alberta, Canada in 2022. Tree inventories, understory vegetation biomass and peat cores were collected across three sub‐sites within the broader swamp, with gas flux collars placed in the dominant plant communities present. Alongside the C flux measurements, environmental variables such as water table depth, soil temperature and growing season understory green leaf phenology were measured. Our results show that these boreal coniferous swamps store large volumes of organic C in their biomass and soil (134 kg C m−2), comparable with other wetland and forest types, although 95% of the total C stock at our site was within the soil organic carbon. We also found that understory CO2 and CH4 fluxes indicated that the ground layer of the site is a source of greenhouse gases (GHGs) to the atmosphere across the growing season. However, we did not measure litterfall input, tree GHG fluxes or net primary productivity of the overstory, therefore we are not able to say whether the site is an overall source of C to the atmosphere. This study provides a much‐needed insight into the C dynamics of these under‐valued wetland ecosystems, and we highlight the need for a coordinated effort across boreal regions to try to improve inventories of C stocks and fluxes.

Multivariate and scale-dependent controls of deep soil carbon after afforestation in a typical loess-covered region

Afforestation is beneficial to improving soil carbon pools. However, due to the lack of deep databases, the variations in soil carbon and the combined effects of multiple factors after afforestation have yet to be adequately explored in >1 m deep soils, especially in areas with deep-rooted plants and thick vadose zones. This study examined the multivariate controls of soil organic carbon (SOC) and inorganic carbon (SIC) in 0–18 m deep under farmland, grassland, willow, and poplar in loess deposits. The novelty of this study is that the factors concurrently affecting deep soil carbon were investigated by multiwavelet coherence and structural equation models. On average, the SOC density (53.1 ± 5.0 kg m−2) was only 12% of SIC density (425.4 ± 13.8 kg m−2), with depth-dependent variations under different land use types. In the soil profiles, the variations in SOC were more obvious in the 0–6 m layer, while SIC variations were mainly observed in the 6–12 m layer. Compared with farmland (SOC: 17.0 kg m−2; SIC: 122.9 kg m−2), the plantation of deciduous poplar (SOC: 28.5 kg m−2; SIC: 144.2 kg m−2) increased the SOC and SIC density within the 0–6 m layer (p < 0.05), but grassland and evergreen willow impacted SOC and SIC density insignificantly. The wavelet coherence analysis showed that, at the large scale (>4 m), SOC and SIC intensities were affected by total nitrogen-magnetic susceptibility and magnetic susceptibility-water content, respectively. The structural equation model further identified that SOC density was directly controlled by total nitrogen (path coefficient = 0.64) and indirectly affected by magnetic susceptibility (path coefficient = 0.36). Further, SOC stimulated the SIC deposition by improving water conservation and electrical conductivity. This study provides new insights into afforestation-induced deep carbon cycles, which have crucial implications for forest management and enhancing ecosystem sustainability in arid regions.

Controls and relationships of soil organic carbon abundance and persistence vary across pedo-climatic regions.

One of the largest uncertainties in the terrestrial carbon cycle is the timing and magnitude of soil organic carbon (SOC) response to climate and vegetation change. This uncertainty prevents models from adequately capturing SOC dynamics and challenges the assessment of management and climate change effects on soils. Reducing these uncertainties requires simultaneous investigation of factors controlling the amount (SOC abundance) and duration (SOC persistence) of stored C. We present a global synthesis of SOC and radiocarbon profiles (nProfile = 597) to assess the timescales of SOC storage. We use a combination of statistical and depth-resolved compartment models to explore key factors controlling the relationships between SOC abundance and persistence across pedo-climatic regions and with soil depth. This allows us to better understand (i) how SOC abundance and persistence covary across pedo-climatic regions and (ii) how the depth dependence of SOC dynamics relates to climatic and mineralogical controls on SOC abundance and persistence. We show that SOC abundance and persistence are differently related; the controls on these relationships differ substantially between major pedo-climatic regions and soil depth. For example, large amounts of persistent SOC can reflect climatic constraints on soils (e.g., in tundra/polar regions) or mineral absorption, reflected in slower decomposition and vertical transport rates. In contrast, lower SOC abundance can be found with lower SOC persistence (e.g., in highly weathered tropical soils) or higher SOC persistence (e.g., in drier and less productive regions). We relate variable patterns of SOC abundance and persistence to differences in the processes constraining plant C input, microbial decomposition, vertical C transport and mineral SOC stabilization potential. This process-oriented grouping of SOC abundance and persistence provides a valuable benchmark for global C models, highlighting that pedo-climatic boundary conditions are crucial for predicting the effects of climate change and soil management on future C abundance and persistence.

Energy flux couples sulfur isotope fractionation to proteomic and metabolite profiles in Desulfovibrio vulgaris.

Microbial sulfate reduction is central to the global carbon cycle and the redox evolution of Earth's surface. Tracking the activity of sulfate reducing microorganisms over space and time relies on a nuanced understanding of stable sulfur isotope fractionation in the context of the biochemical machinery of the metabolism. Here, we link the magnitude of stable sulfur isotopic fractionation to proteomic and metabolite profiles under different cellular energetic regimes. When energy availability is limited, cell-specific sulfate respiration rates and net sulfur isotope fractionation inversely covary. Beyond net S isotope fractionation values, we also quantified shifts in protein expression, abundances and isotopic composition of intracellular S metabolites, and lipid structures and lipid/water H isotope fractionation values. These coupled approaches reveal which protein abundances shift directly as a function of energy flux, those that vary minimally, and those that may vary independent of energy flux and likely do not contribute to shifts in S-isotope fractionation. By coupling the bulk S-isotope observations with quantitative proteomics, we provide novel constraints for metabolic isotope models. Together, these results lay the foundation for more predictive metabolic fractionation models, alongside interpretations of environmental sulfur and sulfate reducer lipid-H isotope data.