We developed a microbial-enzyme-mediated decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). ...These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted that an increase of 2°C (baseline temperature 12°C) caused the pools of POC-cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the changes in pool size of POC, MOC, and total SOC varied from −8% to 8% under +2°C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
Adsorption on soil and sediment solids may decrease aqueous uranium concentrations and limit its propensity for migration in natural and contaminated settings. Uranium adsorption will be controlled ...in large part by its aqueous speciation, with a particular dependence on the presence of dissolved calcium and carbonate. Here we quantify the impact of uranyl speciation on adsorption to both goethite and sediments from the Hanford Clastic Dike and Oak Ridge Melton Branch Ridgetop formations. Hanford sediments were preconditioned with sodium acetate and acetic acid to remove carbonate grains, and Ca and carbonate were reintroduced at defined levels to provide a range of aqueous uranyl species. U(VI) adsorption is directly linked to UO2 2+ speciation, with the extent of retention decreasing with formation of ternary uranyl−calcium−carbonato species. Adsorption isotherms under the conditions studied are linear, and K d values decrease from 48 to 17 L kg−1 for goethite, from 64 to 29 L kg −1 for Hanford sediments, and from 95 to 51 L kg−1 for Melton Branch sediments as the Ca concentration increases from 0 to 1 mM at pH 7. Our observations reveal that, in carbonate-bearing waters, neutral to slightly acidic pH values (∼5) and limited dissolved calcium are optimal for uranium adsorption.
Full text
Available for:
IJS, KILJ, NUK, PNG, UL, UM
3.
The Millennial model Abramoff, Rose; Xu, Xiaofeng; Hartman, Melannie ...
Biogeochemistry,
01/2018, Volume:
137, Issue:
1/2
Journal Article
Peer reviewed
Open access
Soil organic carbon (SOC) can be defined by measurable chemical and physical pools, such as mineral-associated carbon, carbon physically entrapped in aggregates, dissolved carbon, and fragments of ...plant detritus. Yet, most soil models use conceptual rather than measurable SOC pools. What would the traditional pool-based soil model look like if it were built today, reflecting the latest understanding of biological, chemical, and physical transformations in soils? We propose a conceptual model—the Millennial model—that defines pools as measurable entities. First, we discuss relevant pool definitions conceptually and in terms of the measurements that can be used to quantify pool size, formation, and destabilization. Then, we develop a numerical model following the Millennial model conceptual framework to evaluate against the Century model, a widely-used standard for estimating SOC stocks across space and through time. The Millennial model predicts qualitatively similar changes in total SOC in response to single factor perturbations when compared to Century, but different responses to multiple factor perturbations. We review important conceptual and behavioral differences between the Millennial and Century modeling approaches, and the field and lab measurements needed to constrain parameter values. We propose the Millennial model as a simple but comprehensive framework to model SOC pools and guide measurements for further model development.
Full text
Available for:
BFBNIB, DOBA, EMUNI, FZAB, GEOZS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NMLJ, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties. ...To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and the major parameters are the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The combination of extended dry periods and high intensity rainfall, common in the southeastern US, leads to greater variability in soil moisture and consequently increases uncertainty to microbial ...processes pertinent to soil carbon (C) mineralization. However, field-based findings on soil moisture sensitivity to soil C cycling are very limited. Therefore, a field experiment was conducted in 2018 and 2019 on a soybean (Glycine max L.) cropland in the southeastern US with three soil moisture treatments: drought (simulated using rainout-shelter from June to October in each year), rainfed (natural precipitation), and irrigated (irrigation and precipitation). Soil respiration was measured weekly from May to November in both years. Soil samples were collected multiple times each year from 0-5, 5-15, and 15-30 cm depths to determine microbial biomass C (MBC), extractable organic C (EOC), hydrolytic enzyme activities, and fungal abundance. The cumulative respiration under drought compared to other treatments was lower by 32% to 33% in 2018 and 38% to 45% in 2019. Increased MBC, EOC, and fungal abundance were observed under drought than other treatments. Specific enzyme activity indicated fewer metabolically active microbes under drought treatment compared to rainfed and irrigated treatments. Also, maintenance of enzyme pool was observed under drought condition. These results provide critical insights on microbial metabolism in response to soil moisture variation and how that influences different pools of soil C under field conditions.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of ...microbial life-history traits and functions may be necessary to predict climate feedbacks owing to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here we developed the microbial enzyme-mediated decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, that is, MEND with dormancy (MEND) and MEND without dormancy (MEND_wod), against long-term (270 days) carbon decomposition data from laboratory incubations of four soils with isotopically labeled substrates. MEND_wod adequately fitted multiple observations (total C-CO2 and (14)C-CO2 respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. MEND improved estimates of microbial biomass by 20-71% over MEND_wod. We also quantified uncertainties in parameters and model simulations using the Critical Objective Function Index method, which is based on a global stochastic optimization algorithm, as well as model complexity and observational data availability. Together our model extrapolations of the incubation study show that long-term soil incubations with experimental data for multiple carbon pools are conducive to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations, and enable more confident predictions of feedbacks between environmental change and carbon cycling.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, SBNM, UL, UM, UPUK
Seasonality is a key feature of the biosphere and the seasonal dynamics of soil carbon (C) emissions represent a fundamental mechanism regulating the terrestrial–climate interaction. We applied a ...microbial explicit model—CLM‐Microbe—to evaluate the impacts of microbial seasonality on soil C cycling in terrestrial ecosystems. The CLM‐Microbe model was validated in simulating belowground respiratory fluxes, that is, microbial respiration, root respiration, and soil respiration at the site level. On average, the CLM‐Microbe model explained 72% (n = 19, p < 0.0001), 65% (n = 19, p < 0.0001), and 71% (n = 18, p < 0.0001) of the variation in microbial respiration, root respiration, and soil respiration, respectively. We then compared the model simulations of soil respiratory fluxes and soil organic C content in top 1 m between the CLM‐Microbe model with (CLM‐Microbe) and without (CLM‐Microbe_wos) seasonal dynamics of soil microbial biomass in natural biomes. Removing soil microbial seasonality reduced model performance in simulating microbial respiration and soil respiration, but led to slight differences in simulating root respiration. Compared with the CLM‐Microbe, the CLM‐Microbe_wos underestimated the annual flux of microbial respiration by 0.6%–32% and annual flux of soil respiration by 0.4%–29% in natural biomes. Correspondingly, the CLM‐Microbe_wos estimated higher soil organic C content in top 1 m (0.2%–7%) except for the sites in Arctic and boreal regions. Our findings suggest that soil microbial seasonality enhances soil respiratory C emissions, leading to a decline in SOC storage. An explicit representation of soil microbial seasonality represents a critical improvement for projecting soil C decomposition and reducing the uncertainties in global C cycle projection under the changing climate.
We applied a microbial explicit model – CLM‐Microbe – to evaluate the impacts of microbial seasonality on soil carbon cycling in terrestrial ecosystems. Microbial seasonality overall stimulates microbial activities at an annual scale and leads to higher microbial respiration and thus lower soil carbon storage.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Major global change factors, including carbon dioxide (CO
2
) fertilization, warming, change in precipitation, nitrogen deposition, and land-use change have the potential to significantly affect ...future stocks of soil organic carbon (SOC). These factors, individually or by interacting with each other, can also trigger positive or negative feedback to the processes affecting the rate of SOC formation or loss. Despite rapid progress in the understanding of carbon (C) cycling processes in the last few decades, much uncertainty remains in our ability to precisely forecast potential changes in SOC stocks in the rapidly changing future world. Stable C isotopes have been extensively used in natural observational studies as well as in laboratory and field experiments that manipulate CO
2
concentration, temperature, moisture, nitrogen fertilization, and vegetation type to understand the complex interactions and feedbacks that result from changing climate, plants and their herbivores, as well as soil microorganisms. Newly developed tools such as compound-specific isotope analysis, nano-SIMS (secondary ion mass spectroscopy), and stable isotope probing (SIP) permit isotope tracing in a specific ecosystem pool into specific C compounds and processes, thus providing in-depth insights into many processes affecting C biogeochemistry. The recent availability of affordable and reliable field-deployable optical isotope monitoring devices has provided researchers with a new set of tools for continuously tracking the
13
C-CO
2
fluxes at the ecosystem level, enabling deeper insights into C biogeochemistry under changing environmental conditions. Despite these great strides, there is a scarcity of review studies that have comprehensively examined the use of C isotopes in studying SOC responses under global change factors. This review highlights recent progress in understanding the effect of major global change factors on SOC fluxes and stocks using selected examples covering scales from plant rhizospheres to geographic regions. Moreover, we discuss the strengths and limitations of current approaches and recent scientific advancements to highlight the new prospects evolving from the exceptional temporal and spatial resolution of stable isotope analysis in studying how global change affects SOC. Finally, we suggest that studies using stable C isotopes are well-poised to focus on identifying how dominant SOC cycling processes respond to environment-specific limiting factors and any thresholds and tipping points that define those relationships.
Full text
Available for:
DOBA, EMUNI, FZAB, GEOZS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Global ecosystem models may require microbial components to accurately predict feedbacks between climate warming and soil decomposition, but it is unclear what parameters and levels of complexity are ...ideal for scaling up to the globe. Here we conducted a model comparison using a conventional model with first-order decay and three microbial models of increasing complexity that simulate short- to long-term soil carbon dynamics. We focused on soil carbon responses to microbial carbon use efficiency (CUE) and temperature. Three scenarios were implemented in all models: constant CUE (held at 0.31), varied CUE (−0.016 °C⁻¹), and 50 % acclimated CUE (−0.008 °C⁻¹). Whereas the conventional model always showed soil carbon losses with increasing temperature, the microbial models each predicted a temperature threshold above which warming led to soil carbon gain. The location of this threshold depended on CUE scenario, with higher temperature thresholds under the acclimated and constant scenarios. This result suggests that the temperature sensitivity of CUE and the structure of the soil carbon model together regulate the long-term soil carbon response to warming. Equilibrium soil carbon stocks predicted by the microbial models were much less sensitive to changing inputs compared to the conventional model. Although many soil carbon dynamics were similar across microbial models, the most complex model showed less pronounced oscillations. Thus, adding model complexity (i.e. including enzyme pools) could improve the mechanistic representation of soil carbon dynamics during the transient phase in certain ecosystems. This study suggests that model structure and CUE parameterization should be carefully evaluated when scaling up microbial models to ecosystems and the globe.
Full text
Available for:
DOBA, EMUNI, FZAB, GEOZS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Continuous application of P fertilizers under different irrigation patterns can change soil phosphorus (P) chemical behavior and increase soil P levels that are of environmental concern. To assess ...the effect of long-term different irrigation patterns on soil P fractions and availability, this study examined sequential changes in soil organic P and inorganic P from furrow irrigation (FI), surface drip irrigation (SUR), and subsurface drip irrigation (SDI) in the brown soil zone (0-60 cm) during 1998 to 2011. Analyses of soil P behavior showed that the levels of total P are frequently high on top soil layers. The total P (TP) contents of the entire soil profiles under three irrigation treatments were 830.2-3180.1 mg/kg. The contents of available P (AP) were 72.6-319.3 mg P/kg soil through soil profiles. The greatest TP and AP contents were obtained within the upper soil layers in FI. Results of Hedley's P fractionation indicate that HCl-P is a dominant form and the proportion to TP ranges from 29% to 43% in all three methods. The contents of various fractions of P were positively correlated with the levels of total carbon (TC), total inorganic carbon (TIC), and calcium (Ca), whereas the P fractions had negative correlation with pH in all soil samples. Regression models proved that NaHCO3-Po was an important factor in determining the amount of AP in FI. H2O-Po, NaHCO3-Po, and NaOH-Pi were related to available P values in SUR. NaHCO3-Po and NaOH-Po played important roles in SDI. The tomato yield under SUR was higher than SDI and FI. The difference of P availability was also controlled by the physicochemical soil properties under different irrigation schedule. SUR was a reasonable irrigation pattern to improve the utilization efficiency of water and fertilizer.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
