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Incorporating microbial processes into soil biogeochemical models has received growing interest. However, determining the parameters that govern microbially driven biogeochemical processes typically requires case-specific model calibration in various soil and ecosystem types. Here each case refers to an independent and individual experimental unit subjected to repeated measurements. Using the Microbial-ENzyme Decomposition model, this study aimed to test whether a common set of microbially-relevant parameters (i.e., generalized parameters) could be obtained across multiple cases based on a two-year incubation experiment in which soil samples of four distinct soil series (i.e., Coland, Kesswick, Westmoreland, and Etowah) collected from forest and grassland were subjected to cellulose or no cellulose amendment. Results showed that a common set of parameters controlling microbial growth and maintenance as well as extracellular enzyme production and turnover could be generalized at the soil series level but not land cover type. This indicates that microbial model developments need to prioritize soil series type over plant functional types when implemented across various sites. This study also suggests that, in addition to heterotrophic respiration and microbial biomass data, extracellular enzyme data sets are needed to achieve reliable microbial-relevant parameters for large-scale soil model projections.

Unravelling biosphere feedback mechanisms is crucial for predicting the impacts of global warming. Soil priming, an effect of fresh plant-derived carbon (C) on native soil organic carbon (SOC) decomposition, is a key feedback mechanism that could release large amounts of soil C into the atmosphere. However, the impacts of climate warming on soil priming remain elusive. Here, we show that experimental warming accelerates soil priming by 12.7% in a temperate grassland. Warming alters bacterial communities, with 38% of unique active phylotypes detected under warming. The functional genes essential for soil C decomposition are also stimulated, which could be linked to priming effects. We incorporate lab-derived information into an ecosystem model showing that model parameter uncertainty can be reduced by 32-37%. Model simulations from 2010 to 2016 indicate an increase in soil C decomposition under warming, with a 9.1% rise in priming-induced CO2 emissions. If our findings can be generalized to other ecosystems over an extended period of time, soil priming could play an important role in terrestrial C cycle feedbacks and climate change.

Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme-mediated soil inorganic N processes in the Microbial-ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C-N fluxes, microbial C:N ratios, and functional gene abundances from a 12-year CO2  × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO2 fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C-N-associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO2 concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem-level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.

Switchgrass is a deep-rooted perennial native to the US prairies and an attractive feedstock for bioenergy production; when cultivated on marginal soils it can provide a potential mechanism to sequester and accumulate soil carbon (C). However, the impacts of switchgrass establishment on soil biotic/abiotic properties are poorly understood. Additionally, few studies have reported the effects of switchgrass cultivation on marginal lands that have low soil nutrient quality (N/P) or in areas that have experienced high rates of soil erosion. Here, we report a comparative analyses of soil greenhouse gases (GHG), soil chemistry, and microbial communities in two contrasting soil types (with or without switchgrass) over 17 months (1428 soil samples). These soils are highly eroded, 'Dust Bowl' remnant field sites in southern Oklahoma, USA. Our results revealed that soil C significantly increased at the sandy-loam (SL) site, but not at the clay-loam (CL) site. Significantly higher CO2 flux was observed from the CL switchgrass site, along with reduced microbial diversity (both alpha and beta). Strikingly, methane (CH4) consumption was significantly reduced by an estimated 39 and 47% at the SL and CL switchgrass sites, respectively. Together, our results suggest that soil C stocks and GHG fluxes are distinctly different at highly degraded sites when switchgrass has been cultivated, implying that carbon balance considerations should be accounted for to fully evaluate the sustainability of deep-rooted perennial grass cultivation in marginal lands.

Switchgrass is a deep-rooted perennial native to the US prairies and an attractive feedstock for bioenergy production; when cultivated on marginal soils it can provide a potential mechanism to sequester and accumulate soil carbon (C). However, the impacts of switchgrass establishment on soil biotic/abiotic properties are poorly understood. Additionally, few studies have reported the effects of switchgrass cultivation on marginal lands that have low soil nutrient quality (N/P) or in areas that have experienced high rates of soil erosion. Here, we report a comparative analyses of soil greenhouse gases (GHG), soil chemistry, and microbial communities in two contrasting soil types (with or without switchgrass) over 17 months (1428 soil samples). These soils are highly eroded, 'Dust Bowl' remnant field sites in southern Oklahoma, USA. Our results revealed that soil C significantly increased at the sandy-loam (SL) site, but not at the clay-loam (CL) site. Significantly higher CO2 flux was observed from the CL switchgrass site, along with reduced microbial diversity (both alpha and beta). Strikingly, methane (CH4) consumption was significantly reduced by an estimated 39 and 47% at the SL and CL switchgrass sites, respectively. Together, our results suggest that soil C stocks and GHG fluxes are distinctly different at highly degraded sites when switchgrass has been cultivated, implying that carbon balance considerations should be accounted for to fully evaluate the sustainability of deep-rooted perennial grass cultivation in marginal lands.

Whether and how CO2 and nitrogen (N) availability interact to influence carbon (C) cycling processes such as soil respiration remains a question of considerable uncertainty in projecting future C-climate feedbacks, which are strongly influenced by multiple global change drivers, including elevated atmospheric CO2 concentrations (eCO2) and increased N deposition. However, because decades of research on the responses of ecosystems to eCO2 and N enrichment have been done largely independently, their interactive effects on soil respiratory CO2 efflux remain unresolved. Here, we show that in a multifactor free-air CO2 enrichment experiment, BioCON (Biodiversity, CO2, and N deposition) in Minnesota, the positive response of soil respiration to eCO2 gradually strengthened at ambient (low) N supply but not enriched (high) N supply for the 12-y experimental period from 1998 to 2009. In contrast to earlier years, eCO2 stimulated soil respiration twice as much at low than at high N supply from 2006 to 2009. In parallel, microbial C degradation genes were significantly boosted by eCO2 at low but not high N supply. Incorporating those functional genes into a coupled C-N ecosystem model reduced model parameter uncertainty and improved the projections of the effects of different CO2 and N levels on soil respiration. If our observed results generalize to other ecosystems, they imply widely positive effects of eCO2 on soil respiration even in infertile systems.

BackgroundIn a warmer world, microbial decomposition of previously frozen organic carbon (C) is one of the most likely positive climate feedbacks of permafrost regions to the atmosphere. However, mechanistic understanding of microbial mediation on chemically recalcitrant C instability is limited; thus, it is crucial to identify and evaluate active decomposers of chemically recalcitrant C, which is essential for predicting C-cycle feedbacks and their relative strength of influence on climate change. Using stable isotope probing of the active layer of Arctic tundra soils after depleting soil labile C through a 975-day laboratory incubation, the identity of microbial decomposers of lignin and, their responses to warming were revealed.ResultsThe β-Proteobacteria genus Burkholderia accounted for 95.1% of total abundance of potential lignin decomposers. Consistently, Burkholderia isolated from our tundra soils could grow with lignin as the sole C source. A 2.2 °C increase of warming considerably increased total abundance and functional capacities of all potential lignin decomposers. In addition to Burkholderia, α-Proteobacteria capable of lignin decomposition (e.g. Bradyrhizobium and Methylobacterium genera) were stimulated by warming by 82-fold. Those community changes collectively doubled the priming effect, i.e., decomposition of existing C after fresh C input to soil. Consequently, warming aggravates soil C instability, as verified by microbially enabled climate-C modeling.ConclusionsOur findings are alarming, which demonstrate that accelerated C decomposition under warming conditions will make tundra soils a larger biospheric C source than anticipated. Video Abstract.

Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration (Q10) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5-19%, and reduces model parametric uncertainty by 55-71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.

Censuses of tropical forest plots reveal large variation in biomass and plant composition. This paper evaluates whether such variation can emerge solely from realistic variation in a set of commonly measured soil chemical and physical properties. Controlled simulations were performed using a mechanistic model that includes forest dynamics, microbe-mediated biogeochemistry, and competition for nitrogen and phosphorus. Observations from 18 forest inventory plots in Guanacaste, Costa Rica were used to determine realistic variation in soil properties. In simulations of secondary succession, the across-plot range in plant biomass reached 30% of the mean and was attributable primarily to nutrient limitation and secondarily to soil texture differences that affected water availability. The contributions of different plant functional types to total biomass varied widely across plots and depended on soil nutrient status. In Central America, soil-induced variation in plant biomass increased with mean annual precipitation because of changes in nutrient limitation. In Central America, large variation in plant biomass and ecosystem composition arises mechanistically from realistic variation in soil properties. The degree of biomass and compositional variation is climate sensitive. In general, model predictions can be improved through better representation of soil nutrient processes, including their spatial variation.

Global soil carbon (C) stocks are expected to decline with warming, and changes in microbial processes are key to this projection. However, warming responses of critical microbial parameters such as carbon use efficiency (CUE) and biomass turnover (rB) are not well understood. Here, we determine these parameters using a probabilistic inversion approach that integrates a microbial-enzyme model with 22 years of carbon cycling measurements at Harvard Forest. We find that increasing temperature reduces CUE but increases rB, and that two decades of soil warming increases the temperature sensitivities of CUE and rB. These temperature sensitivities, which are derived from decades-long field observations, contrast with values obtained from short-term laboratory experiments. We also show that long-term soil C flux and pool changes in response to warming are more dependent on the temperature sensitivity of CUE than that of rB. Using the inversion-derived parameters, we project that chronic soil warming at Harvard Forest over six decades will result in soil C gain of

Data transformation of the reference soil organic matter (SOM) decomposition rates (kref), often derived as turnover times or in alternative formats, is commonly used to develop ecological models for projecting the persistence of SOM. However, the effects of reciprocal or logarithmic transformation of kref on model performance and edaphic‐climatic patterns remain uncertain. Here, we convert published kref values into reciprocal or logarithmic formats and establish machine learning models between the transformed kref and edaphic‐climatic predictors. We show that models trained with the transformed kref exhibit a 11.6%−68.4% reduction in performance upon re‐conversion to kref compared to those trained with the original kref. The variable importance analysis identifies distinct key predictors governing the original kref and its transformed counterparts. This suggests that data transformation alters the relative significance of predictors without necessarily improving kref prediction performance. Consequently, our study underscores the importance of directly focusing on the original values rather than alternative representations when dissecting a given variable's patterns and mechanisms in ecological modeling. [ABSTRACT FROM AUTHOR]

Soils contain more carbon than plants or the atmosphere, and sensitivities of soil organic carbon (SOC) stocks to changing climate and plant productivity are a major uncertainty in global carbon cycle projections. Despite a consensus that microbial degradation and mineral stabilization processes control SOC cycling, no systematic synthesis of long-term warming and litter addition experiments has been used to test process-based microbe-mineral SOC models. We explored SOC responses to warming and increased carbon inputs using a synthesis of 147 field manipulation experiments and five SOC models with different representations of microbial and mineral processes. Model projections diverged but encompassed a similar range of variability as the experimental results. Experimental measurements were insufficient to eliminate or validate individual model outcomes. While all models projected that CO2 efflux would increase and SOC stocks would decline under warming, nearly one-third of experiments observed decreases in CO2 flux and nearly half of experiments observed increases in SOC stocks under warming. Long-term measurements of C inputs to soil and their changes under warming are needed to reconcile modeled and observed patterns. Measurements separating the responses of mineral-protected and unprotected SOC fractions in manipulation experiments are needed to address key uncertainties in microbial degradation and mineral stabilization mechanisms. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of SOC responses to global changes.

The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results. Plain Language Summary: Frequent urban flooding and waterlogging pose significant threats to human life and property. Accurate modeling urban floods is crucial for minimizing damages caused by urban flooding. However, the delayed initiation of urban observations has resulted in prevalent data scarcity in urban areas, leading to difficulties and low accuracy in estimating model parameters for ungauged urban catchments. This study proposes a parameter regionalization framework for an urban hydrological model to improve the accuracy of flood simulations in ungauged urban catchments. This framework involves classifying gauged catchments into multiple groups and identifying the optimal regression‐based methods in each group for parameter regionalization. The flood simulation performance and stability of the parameter regionalization framework is evaluated by comparing it with eight widely‐used regionalization methods in 37 urban catchments located in Shenzhen City, China. Our results show that the classification of gauged catchments can significantly improve the simulation performance of linear regression methods. Additionally, the parameter regionalization framework outperforms the other regionalization methods for both urban flood simulation in the study area and urban flood prediction in the ungauged urban catchments. This study provides a novel and efficient parameterization method for accurate flood prediction in ungauged urban catchments to reduce potential losses. Key Points: TVGM_Urban presents satisfying performance in urban flood modelingThe PRF integrating donor catchment clustering and optimal regression methods in each cluster outperforms single regionalization methodsCurve number, land cover area ratio, and slope are critical factors for runoff generation parameters of TVGM_Urban [ABSTRACT FROM AUTHOR]

Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. Here, we employed a microbial ecological model (MEND), driven by downscaled output data from six Earth system models under two Shared Socio‐economic Pathways (SSP1‐2.6 and SSP5‐8.5) scenarios, to simulate long‐term soil biogeochemical processes. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). For the entire soil layer in both forests, we found that, compared to the baseline period of 2009–2020, the mean SOC during 2081–2100 increased by 40.9%–90.6% under the L or T change scenarios, versus 5.2%–31.0% under the W change scenario. However, soil moisture emerged as a key regulator of SOC, MBC and inorganic N dynamics in the topsoil of BF and PF. For example, W change led to SOC gain of 5.5%–37.2%, compared to the SOC loss of 15.5%–18.0% under L or T scenario. Additionally, a further reduction in soil pH by 0.2 units in the BF, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the LTW (simultaneous changes in the three factors) scenario. These results indicate that the results derived solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. Plain Language Summary: Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). We found that soil moisture change would be beneficial for SOC accumulation and serves as a key regulator of MBC and inorganic N in topsoil, whereas the change in litterfall or soil temperature are favorable for SOC accumulation in the entire soil profile. Additionally, a further reduction in soil pH by 0.2 units, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the scenario with simultaneous changes in L, W, and T. These results indicate that findings solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. Key Points: Soil C responses to climate change are depth dependent, therefore, results from just the topsoil may not apply to the entire soil profileSoil moisture change benefits topsoil SOC accumulation, whereas litterfall and soil temperature changes favor SOC accumulation in the entire soil profileWe need to pay more attention to the effects of soil moisture and pH rather than temperature and litter‐input on soil biogeochemical processes [ABSTRACT FROM AUTHOR]

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. © 2014 Springer Science+Business Media Dordrecht.