Microbial models with data-driven parameters predict stronger soil carbon responses to climate change

Vegetation restoration enhancing soil carbon sequestration in karst rocky desertification ecosystems: A meta-analysis

Vegetation restoration measures have been increasingly employed to alleviate rocky desertification in karst ecosystems. However, the comprehensive effects of these interventions on soil properties and soil organic carbon (SOC) remain poorly understood. Herein, we gathered 644 paired observations from 68 studies and conducted a meta-analysis to quantify the performance of different vegetation restoration measures including moss (MS), grassland (GL), cash crop (CP), shrub (SH), and secondary forest (SF) through soil properties and SOC. Our results demonstrated significant effects of MS, GL, CP, SH, and SF on soil biotic and abiotic factors, each with distinct response characteristics. Particularly, MS significantly enhanced all soil properties (excluding a slight decrease in soil pH by 10.8%). Moreover, MS, GL, CP, SH, and SF could elevate SOC by 32.1%, 17.6%, 24.9%, 59.2%, and 48.7% respectively. Utilizing random forest and linear regression models, we identified primary drivers for SOC in MS, GL, CP, SH, and SF as soil moisture content, arbuscular mycorrhizal fungi, soil microbial phosphorus, total nitrogen, and β-1,4-glucosidase, respectively. This meta-analysis underlined the varied effects of vegetation restoration measures on soil properties and advocates for restoration measures that prioritize plant productivity and reduce soil temperature during the karst rocky desertification restoration process. Additionally, this study underscores the pivotal role of vegetation rehabilitation in environmental conservation and carbon sequestration of ecologically vulnerable regions.

Extreme rainfall erosivity: Research advances and future perspectives

Extreme rainfall erosivity, the capacity of intense rainfall to induce soil erosion, is vital for anticipating future impacts on soil conservation. Despite extensive research, significant differences persist in terms of understanding influencing mechanisms, potential impacts, estimation models and future trends of extreme rainfall erosivity. Quantitatively describing extreme rainfall erosivity remains a key issue in existing research. In this study, we comprehensively reviewed the literature to assess the relationships between extreme rainfall characteristics and rainfall erosivity, between extreme rainfall erosivity and soil erosion, estimation models and trend prediction. The aim was to summarize previous related research and achievements, providing a better understanding of the generation, impacts and future trends of extreme rainfall erosivity. Future research directions should include identifying the thresholds of extreme rainfall events, increasing research attention on tropical cyclones in terms of rainfall erosivity, considering on the impact of extreme rainfall erosivity on soil erosion, and improving rainfall erosivity estimation and simulation prediction methods. This study could contribute to adapting to global climate change and aiding in formulating soil erosion prevention and environmental protection recommendations.

Nitrogen and phosphorus addition mediate soil priming effects via affecting microbial stoichiometric balance in an alpine meadow

Priming effect (PE) plays a crucial role in regulating the decomposition of soil organic matter (SOM). Multiple empirical results have shown that nitrogen (N) and phosphorus (P) addition can significantly alter the direction and intensity of PE, which may significantly affect carbon turnover in grasslands, especially in alpine meadows that are sensitive to N and P enrichment. To evaluate the PE responses to N and/or P addition, we conducted an incubation experiment by adding 13C-labeled glucose and nutrient additions (+N, +P, and +NP) in soils collected from an alpine meadow. The soils were incubated for 30 days and soil/microbial properties and enzyme activities were measured. Partial correlation and linear regression analyses were then performed to investigate their correlations with PE. The results showed that mean PE intensity among all treatments was 0.61 mg C g-1 soil or 1.35 (ratio). Nitrogen addition increased PE intensity, which was attributed to the better match between soil resources and microbial demands and enhanced enzyme activities. However, the PE intensity in P-addition soils was lower than that in control soils. This discrepancy may be related to the P-induced decrease of N availability and stronger microbial C/N imbalance. No significant response of PE intensity to NP addition was detected, and this could be explained by the offset of positive N effects and negative P effects on microbial decomposition. In this experiment, N or P addition altered the PE intensity by mediating the match between soil C:N:P ratio and microbial demands, which supported the stoichiometric decomposition hypothesis. Overall, our study highlights the importance of considering the C, N and P coupling in regulating PE, and underscores the need for further investigation into the effects of soil P on microbial activity and SOM decomposition.

Core microbiota drive multi-functionality of the soil microbiome in the Cinnamomum camphora coppice planting

BACKGROUND

Cinnamomum camphora (L.) Presl (C. camphora) is an evergreen broad-leaved tree cultivated in subtropical China. The use of C. camphora as clonal cuttings for coppice management has become popular recently. However, little is known about the relationship between soil core microbiota and ecosystem multi-functionality under tree planting. Particularly, the effects of soil core microbiota on maintaining ecosystem multi-functionality under C. camphora coppice planting remained unclear.

MATERIALS AND METHODS

In this study, we collected soil samples from three points (i.e., the abandoned land, the root zone, and the transition zone) in the C. camphora coppice planting to investigate whether core microbiota influences ecosystem multi-functions.

RESULTS

The result showed a significant difference in soil core microbiota community between the abandoned land (AL), root zone (RZ), and transition zone (TZ), and soil ecosystem multi-functionality of core microbiota in RZ had increased significantly (by 230.8%) compared to the AL. Soil core microbiota played a more significant influence on ecosystem multi-functionality than the non-core microbiota. Moreover, the co-occurrence network demonstrated that the soil ecosystem network consisted of five major ecological clusters. Soil core microbiota within cluster 1 were significantly higher than in cluster 4, and there is also a higher Copiotrophs/Oligotrophs ratio in cluster 1. Our results corroborated that soil core microbiota is crucial for maintaining ecosystem multi-functionality. Especially, the core taxa within the clusters of networks under tree planting, with the same ecological preferences, had a significant contribution to ecosystem multi-functionality.

CONCLUSION

Overall, our results provide further insight into the linkage between core taxa and ecosystem multi-functionality. This enables us to predict how ecosystem functions respond to the environmental changes in areas under the C. camphora coppice planting. Thus, conserving the soil microbiota, especially the core taxa, is essential to maintaining the multiple ecosystem functions under the C. camphora coppice planting.

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

Integrating McGill Wetland Model (MWM) with peat cohort tracking and microbial controls

Peatlands store a large amount of organic carbon and are vulnerable to climate change and human disturbances. However, ecosystem-scale peatland models often do not explicitly simulate the decrease in peat substrate quality, i.e., decomposability or the dynamics of decomposers during peat decomposition, which are key controls in determining peat carbon's response to a changing environment. In this paper, we incorporated the tracking of each year's litter input (a cohort) and controls of microbial processes into the McGill Wetland Model (MWMmic) to address this discrepancy. Three major modifications were made: (1) the simple acrotelm-catotelm decomposition model in MWM was changed into a time-aggregated cohort model, to track the decrease in peat quality with decomposition age; (2) microbial dynamics: growth, respiration and death were incorporated into the model and decomposition rates are regulated by microbial biomass; and (3) vertical and horizontal transport of the dissolved organic carbon (DOC) were added and used to regulate the growth of microbial biomass. MWMmic was evaluated against measurements from the Mer Bleue peatland, a raised ombrotrophic bog located in southern Ontario, Canada. The model was able to replicate microbial and DOC dynamics, while at the same time reproduce the ecosystem-level CO₂ and DOC fluxes. Sensitivity analysis with MWMmic showed increased peatland resilience to perturbations compared to the original MWM, because of the tracking of peat substrate quality. The analysis revealed the most important parameters in the model to be microbial carbon use efficiency (CUE) and turnover rate. Simulated microbial adaptation with those two physiological parameters less sensitive to disturbances leads to a significantly larger peat C loss in response to warming and water table drawdown. Thus, the rarely explored peatland microbial physiological traits merit further research. This work paves the way for further model development to examine important microbial controls on peatland's biogeochemical cycling.

Differential effects of nitrogen vs. phosphorus limitation on terrestrial carbon storage in two subtropical forests: A Bayesian approach

Nitrogen (N) and phosphorus (P) have been demonstrated to limit terrestrial carbon (C) storage in terrestrial ecosystems. However, the reliable indicator to infer N and P limitation are still lacking, especially in subtropical forests. Here we used a terrestrial ecosystem (TECO) model framework in combination with a Bayesian approach to evaluate effects of nutrient limitation from added N/P processes and data sets on C storage capacities in two subtropical forests (Tiantong and Qianyanzhou [QYZ]). Three of the six simulation experiments were developed with assimilating data (TECO C model with C data [C-C], TECO C-N coupling model with C and N data [CN-CN], and TECO C-N-P model with C, N, and P data [CNP-CNP]), and the other three ones were simulated without assimilating data (C-only, CN-only, and CNP-only). We found that P dominantly constrained C storage capacities in Tiantong (42%) whereas N limitation decreased C storage projections in QYZ (44%). Our analysis indicated that the stoichiometry of wood biomass and soil microbe (e.g., N:P ratio) were more sensitive indicators of N or P limitation than that of other pools. Furthermore, effects of P-induced limitation were mainly on root biomass by additional P data and on both metabolic litter and soil organic carbon (SOC) by added P processes. N-induced effects were mainly from added N data that limited plant non-photosynthetic tissues (e.g., woody biomass and litter). The different effects of N and P modules on C storage projections reflected the diverse nutrient acquisition strategies associated with stand ages and plant species under nutrient stressed environment. These findings suggest that the interaction between plants and microorganisms regulate effects of nutrient availability on ecosystem C storage, and stoichiometric flexibility of N and P in plant and soil C pools could improve the representation of N and P limitation in terrestrial ecosystem models.

Evaluation of the Terrestrial Ecosystem Model Biome-BGCMuSo for Modelling Soil Organic Carbon under Different Land Uses

Soil organic carbon (SOC) is a mandatory pool in national inventory reports on greenhouse gas (GHG) emissions and removals to the UNFCCC. Hence, its accurate assessment is important. Modelling SOC changes for national GHG reports is encouraged, but the uncertainty related to this pool still presents a significant challenge; thus, verifying modelling results with field observations is essential. We used the process-based model Biome-BGCMuSo and assessed its suitability for use in Croatia’s GHG reporting. We modelled SOC stocks in the top 30 cm of the mineral soil layer (SOC30) for four different land-use (LU) categories (Deciduous/Coniferous Forest, Grassland and Annual Cropland) distributed in three biogeographical regions (Alpine, Continental and Mediterranean) and compared them with results of a national soil survey. A total of 573 plot level simulations were undertaken and results were evaluated at three stratification levels (LU, LU × biogeographical region, and plot). The model reproduced the overall country mean of SOC30 with no overall bias, and showed good performance at the LU level with no significant (p < 0.05) difference for all LUs except Deciduous Forest (11% overestimation). At finer stratifications, the model performance considerably worsened. Further model calibration, improvement and testing, as well as repeated soil survey are needed in order to assess the changes in SOC30 and to evaluate the potential of the Biome-BGCMuSo model for use in GHG reporting.

Biological mechanisms may contribute to soil carbon saturation patterns

Increasing soil organic carbon (SOC) storage is a key strategy to mitigate rising atmospheric CO₂ , yet SOC pools often appear to saturate, or increase at a declining rate, as carbon (C) inputs increase. Soil C saturation is commonly hypothesized to result from the finite amount of reactive mineral surface area available for retaining SOC, and is accordingly represented in SOC models as a physicochemically determined SOC upper limit. However, mineral-associated SOC is largely microbially generated. In this perspective, we present the hypothesis that apparent SOC saturation patterns could emerge as a result of ecological constraints on microbial biomass-for example, via competition or predation-leading to reduced C flow through microbes and a reduced rate of mineral-associated SOC formation as soil C inputs increase. Microbially explicit SOC models offer an opportunity to explore this hypothesis, yet most of these models predict linear microbial biomass increases with C inputs and insensitivity of SOC to input rates. Synthesis of 54 C addition studies revealed constraints on microbial biomass as C inputs increase. Different hypotheses limiting microbial density were embedded in a three-pool SOC model without explicit limits on mineral surface area. As inputs increased, the model demonstrated either no change, linear, or apparently saturating increases in mineral-associated and particulate SOC pools. Taken together, our results suggest that microbial constraints are common and could lead to reduced mineral-associated SOC formation as input rates increase. We conclude that SOC responses to altered C inputs-or any environmental change-are influenced by the ecological factors that limit microbial populations, allowing for a wider range of potential SOC responses to stimuli. Understanding how biotic versus abiotic factors contribute to these patterns will better enable us to predict and manage soil C dynamics.

Short-term phosphorus addition augments the effects of nitrogen addition on soil respiration in a typical steppe

Soil respiration is one of the largest carbon (C) sources in terrestrial ecosystems and is sensitive to soil nutrient variation. Although nitrogen (N) availability affects soil respiration, other nutrients, such as phosphorous (P), which play pivotal roles in plant growth and microbial activity, may also affect soil respiration. In addition, N and P have been widely reported to interactively affect plant growth; however, their interactive effects on soil respiration have rarely been studied. Therefore, we conducted a short-term, two-factor experiment (from 2013 to 2015) to determine whether N and P addition can interactively affect soil respiration in a northern Chinese steppe. Nitrogen addition elevated soil respiration by 9.5%, whereas P addition did not affect soil respiration in the studied steppe across all treatments. However, neither N nor P addition significantly affected soil respiration alone in the experiment. Furthermore, N and P interactively affected soil respiration. Nitrogen addition did not affect soil respiration in the ambient P plots, but significantly elevated soil respiration (by 17.7%) in P addition plots across the three growing seasons. The effects of N addition on soil respiration were primarily correlated with the responses of vegetation cover and litter biomass to N addition in the experiment. Our results demonstrate that P addition augments the effects of N addition on soil respiration. Soil nutrient contents should be incorporated into predictive models for terrestrial C cycle response to N addition.

Nutrient Balance as a Tool for Maintaining Yield and Mitigating Environmental Impacts of Acacia Plantation in Drained Tropical Peatland—Description of Plantation Simulator

Responsible management of Acacia plantations requires an improved understanding of trade-offs between maintaining stand production whilst reducing environmental impacts. Intensive drainage and the resulting low water tables (WT) increase carbon emissions, peat subsidence, fire risk and nutrient export to water courses, whilst increasing nutrient availability for plant uptake from peat mineralization. In the plantations, hydrology, stand growth, carbon and nutrient balance, and peat subsidence are connected forming a complex dynamic system, which can be thoroughly understood by dynamic process models. We developed the Plantation Simulator to describe the effect of drainage, silviculture, fertilization, and weed control on the above-mentioned processes and to find production schemes that are environmentally and economically viable. The model successfully predicted measured peat subsidence, which was used as a proxy for stand total mass balance. Computed nutrient balances indicated that the main growth-limiting factor was phosphorus (P) supply, and the P balance was affected by site index, mortality rate and WT. In a scenario assessment, where WT was raised from −0.80 m to −0.40 m the subsidence rate decreased from 4.4 to 3.3 cm yr−1, and carbon loss from 17 to 9 Mg ha−1 yr−1. P balance shifted from marginally positive to negative suggesting that additional P fertilization is needed to maintain stand productivity as a trade-off for reducing C emissions.

New forest biomass carbon stock estimates in Northeast Asia based on multisource data

Forests play an important role in both regional and global C cycles. However, the spatial patterns of biomass C density and underlying factors in Northeast Asia remain unclear. Here, we characterized spatial patterns and important drivers of biomass C density for Northeast Asia, based on multisource data from in situ forest inventories, as well as remote sensing, bioclimatic, topographic, and human footprint data. We derived, for the first time, high-resolution (1 km × 1 km) maps of the current and future forest biomass C density for this region. Based on these maps, we estimated that current biomass C stock in northeastern China, the Democratic People's Republic of Korea, and Republic of Korea to be 2.53, 0.40, and 0.35 Pg C, respectively. Biomass C stock in Northeast Asia has increased by 20%-46% over the past 20 years, of which 40%-76% was contributed by planted forests. We estimated the biomass C stock in 2080 to be 6.13 and 6.50 Pg C under RCP4.5 and RCP8.5 scenarios, respectively, which exceeded the present region-wide C stock value by 2.85-3.22 Pg C, and were 8%-14% higher than the baseline C stock value (5.70 Pg C). The spatial patterns of biomass C densities were found to vary greatly across the Northeast Asia, and largely decided by mean diameter at breast height, dominant height, elevation, and human footprint. Our results suggest that reforestation and forest conservation in Northeast Asia have effectively expanded the size of the carbon sink in the region, and sustainable forest management practices such as precision forestry and close forest monitoring for fire and insect outbreaks would be important to maintain and improve this critical carbon sink for Northeast Asia.

The global soil community and its influence on biogeochemistry

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

Warming but Not Nitrogen Addition Alters the Linear Relationship Between Microbial Respiration and Biomass

Soil contains a large amount of organic matter, which constitutes the largest terrestrial carbon pool. Heterotrophic or microbial respiration (R_h) that results from microbial decomposition of soil organic carbon (SOC) constitutes a substantial proportion of soil C efflux. Whether soil microbial biomass is of primary importance in controlling R_h remains under debate, and the question of whether the microbial biomass-decomposition relationship changes with warming and nitrogen (N) deposition has rarely been assessed. We conducted an incubation experiment to test the relationship between R_h and the size of soil microbial communities in two layers of soil collected from a natural subtropical forest and to examine whether the relationship was affected by changes in temperature and by added N in different forms. The results showed that regardless of the added N species, the N load did not significantly affect R_h or the size of the soil microbial communities. These results could be due to a long-term N-rich soil condition that acclimates soil microbial communities to resist N inputs into the studied forest; however, warming may significantly stimulate SOC decomposition, reducing soil microbial biomass under high temperatures. A significant linear soil microbial biomass-decomposition relationship was observed in our study, with the coefficients of determination ranging from 54 to 70%. Temperature rather than N additions significantly modified the linear relationship between soil microbial biomass and respiration. These results suggest that warming could impose a more substantial impact than N addition on the relationship between soil microbial biomass and SOC decomposition.

Global variation of soil microbial carbon-use efficiency in relation to growth temperature and substrate supply

Soil microbial carbon-use efficiency (CUE), which is defined as the ratio of growth over C uptake, is commonly assumed as a constant or estimated by a temperature-dependent function in current microbial-explicit soil carbon (C) models. The temperature-dependent function (i.e., CUE = CUE₀ + m × (T − 20)) simulates the dynamic CUE based on the specific CUE at a given reference temperature (i.e., CUE₀) and a temperature response coefficient (i.e., m). Here, based on 780 observations from 98 sites, we showed a divergent spatial distribution of the soil microbial CUE (0.5 ± 0.25; mean ± SD) at the global scale. Then, the key parameters CUE₀ and m in the above equation were estimated as 0.475 and −0.016, respectively, based on the observations with the Markov chain Monte Carlo technique. We also found a strong dependence of microbial CUE on the type of C substrate. The multiple regression analysis showed that glucose influences the variation of measured CUE associated with the environmental factors. Overall, this study confirms the global divergence of soil microbial CUE and calls for the incorporation of C substrate beside temperature in estimating the microbial CUE in different biomes.

Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation

It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback.

Reduced carbon use efficiency and increased microbial turnover with soil warming

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 <1.0% on average (1st and 3rd quartiles: 3.0% loss and 10.5% gain) in the surface mineral horizon. Our results demonstrate that estimates of temperature sensitivity of microbial CUE and rB can be obtained and evaluated rigorously by integrating multidecadal datasets. This approach can potentially be applied in broader spatiotemporal scales to improve long-term projections of soil C feedbacks to climate warming.

Carbon pools in China's terrestrial ecosystems: New estimates based on an intensive field survey

China's terrestrial ecosystems have functioned as important carbon sinks. However, previous estimates of carbon budgets have included large uncertainties owing to the limitations of sample size, multiple data sources, and inconsistent methodologies. In this study, we conducted an intensive field campaign involving 14,371 field plots to investigate all sectors of carbon stocks in China's forests, shrublands, grasslands, and croplands to better estimate the regional and national carbon pools and to explore the biogeographical patterns and potential drivers of these pools. The total carbon pool in these four ecosystems was 79.24 ± 2.42 Pg C, of which 82.9% was stored in soil (to a depth of 1 m), 16.5% in biomass, and 0.60% in litter. Forests, shrublands, grasslands, and croplands contained 30.83 ± 1.57 Pg C, 6.69 ± 0.32 Pg C, 25.40 ± 1.49 Pg C, and 16.32 ± 0.41 Pg C, respectively. When all terrestrial ecosystems are taken into account, the country's total carbon pool is 89.27 ± 1.05 Pg C. The carbon density of the forests, shrublands, and grasslands exhibited a strong correlation with climate: it decreased with increasing temperature but increased with increasing precipitation. Our analysis also suggests a significant sequestration potential of 1.9-3.4 Pg C in forest biomass in the next 10-20 years assuming no removals, mainly because of forest growth. Our results update the estimates of carbon pools in China's terrestrial ecosystems based on direct field measurements, and these estimates are essential to the validation and parameterization of carbon models in China and globally.

Multiple Factors Drive Variation of Forest Root Biomass in Southwestern China

The roots linking the above-ground organs and soil are key components for estimating net primary productivity and carbon sequestration of forests. The patterns and drivers of root biomass in forest have not been examined well at the regional scale, especially for the widely distributed forest ecosystems in southwestern China. We attempted to determine the spatial patterns of root biomass (RB, Mg/ha), annual increment root biomass (AIRB, Mg/ha/year), ratio of root and above-ground (RRA), and the relative contributions of abiotic and biotic factors that drive the variation of root biomass. Forest biomass and multiple factors (climate, soil, forest types, and stand characteristics) of 318 plots in this region (790,000 km2) were analyzed in this research. The AB (the mean values for forest aboveground biomass per ha, Mg/ha), RB, AIRB, and RRA were 126 Mg/ha, 28 Mg/ha, 0.69 Mg/ha and 0.22, respectively. AB, RB, AIRB, and RRA varied across all the plots and forest types. Both RB and AIRB showed significant spatial patterns of distribution, while RRA did not show any spatial patterns of distribution. Up to 28.4% of variation in total of RB, AIRB, and RRA can be attributed to the climate, soil, and stand characteristics. The explained or contribution rates of climate, soil, and stand characteristics for variation of whole forest root biomass were 6.7%, 16.9%, and 10.9%, respectively. Path analysis in structural equation model (SEM) indicated the direct influence of stand age on RB. AIRB was greater than that of the other factors. Climate, soil and stand characteristics in different forest types could explain 9.7%–96.1%, 15.4%–96.4%, and 36.7%–99.4% of variations in RB, AIRB, and RRA, respectively, which suggests that the multiple factors may be important in explaining the variations in forest root biomass. The results of the analysis of root biomass per ha, annual increment of root biomass per ha, and ratio of root and above-ground in the seven forest types categorized by climate, soil, and stand characteristics may be used for accurately determining C sequestration by the forest root and estimating forest biomass in this region.

Model structures amplify uncertainty in predicted soil carbon responses to climate change

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

A substantial portion of model uncertainty arises from model parameters and structures. Here, the authors show that alternative model structures with data-driven parameters project greater uncertainty in soil carbon responses to climate change than the conventional soil carbon model.

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

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20^th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

Understanding the spatial distribution of factors controlling topsoil organic carbon content in European soils

Soil Organic Carbon (SOC) constitutes the largest terrestrial carbon pool. The understanding of its dynamics and the environmental factors that influence its behaviour as sink or source of atmospheric CO₂ is crucial to quantify the carbon budget at the global scale. At the European scale, most of the existing studies to account for SOC stocks are centred in the fitting of predictive model to ascertain the distribution of SOC. However, the development of methodologies for monitoring and identifying the environmental factors that control SOC storage in Europe remains a key research challenge. Here we present a modelling procedure for mapping and monitoring SOC contents that uses Visible-Near Infrared (VNIR) spectroscopic measurements and a series of environmental covariates to ascertain the key environmental processes that have a major contribution into SOC sequestration processes. Our results show that it follows a geographically non-stationary process in which the influencing environmental factors have different weights depending on the spatial location. This implies that SOC stock modelling should not rely on a single model but on a combination of different statistical models depending on the environmental characteristics of each area. A cluster classification of European soils in relation to those factors resulted in the determination of four groups for which specific models have been obtained. Differences in climate, soil pH, content of coarse fragments or land cover type are the main factors explaining the differences in SOC in topsoil from Europe. We found that climatic conditions are the main driver of SOC storage at the continental scale, but we also found that parameters like land cover type influence SOC content found at the local scales in certain areas. Our methodology developed at continental scale could be used in future research aimed to improve the predictive performance of SOC assessments at European scale.

Microbial community-level regulation explains soil carbon responses to long-term litter manipulations

Climatic, atmospheric, and land-use changes all have the potential to alter soil microbial activity, mediated by changes in plant inputs. Many microbial models of soil organic carbon (SOC) decomposition have been proposed recently to advance prediction of climate and carbon (C) feedbacks. Most of these models, however, exhibit unrealistic oscillatory behavior and SOC insensitivity to long-term changes in C inputs. Here we diagnose the source of these problems in four archetypal models and propose a density-dependent formulation of microbial turnover, motivated by community-level interactions, that limits population sizes and reduces oscillations. We compare model predictions to 24 long-term C-input field manipulations and identify key benchmarks. The proposed formulation reproduces soil C responses to long-term C-input changes and implies greater SOC storage associated with CO₂-fertilization-driven increases in C inputs over the coming century compared to recent microbial models. This study provides a simple modification to improve microbial models for inclusion in Earth System Models.

Phylogenetic conservation of substrate use specialization in leaf litter bacteria

Environmental change will influence the ecosystem processes regulated by microbial communities, including leaf litter decomposition. To assess how microbial communities and their functioning might respond to increases in temperature, we quantified the distribution of traits related to carbon substrate utilization and temperature sensitivity in leaf litter bacteria isolated from a natural grassland ecosystem in Southern California. The isolates varied substantially in their carbon substrate use, as well as their response to temperature change. To better predict the functioning and responses in natural communities, we also examined if the functional and response traits were phylogenetically patterned or correlated with one another. We found that the distribution of functional traits displayed a phylogenetic pattern, but the sensitivity of the traits to changes in temperature did not. We also did not detect any correlations between carbon substrate use and sensitivity to changes in temperature. Together, these results suggest that information about microbial composition may provide insights to predicting ecosystem function under one temperature, but that these relationships may not hold under new temperature conditions.

Low-carbon agriculture in South America to mitigate global climate change and advance food security

The worldwide historical carbon (C) losses due to Land Use and Land-Use Change between 1870 and 2014 are estimated at 148 Pg C (1 Pg=1billionton). South America is chosen for this study because its soils contain 10.3% (160 Pg C to 1-m depth) of the soil organic carbon stock of the world soils, it is home to 5.7% (0.419 billion people) of the world population, and accounts for 8.6% of the world food (491milliontons) and 21.0% of meat production (355milliontons of cattle and buffalo). The annual C emissions from fossil fuel combustion and cement production in South America represent only 2.5% (0.25 Pg C) of the total global emissions (9.8 Pg C). However, South America contributes 31.3% (0.34 Pg C) of global annual greenhouse gas emissions (1.1 Pg C) through Land Use and Land Use Change. The potential of South America as a terrestrial C sink for mitigating climate change with adoption of Low-Carbon Agriculture (LCA) strategies based on scenario analysis method is 8.24 Pg C between 2016 and 2050. The annual C offset for 2016 to 2020, 2021 to 2035, and 2036 to 2050 is estimated at 0.08, 0.25, and 0.28 Pg C, respectively, equivalent to offsetting 7.5, 22.2 and 25.2% of the global annual greenhouse gas emissions by Land Use and Land Use Change for each period. Emission offset for LCA activities is estimated at 31.0% by restoration of degraded pasturelands, 25.6% by integrated crop-livestock-forestry-systems, 24.3% by no-till cropping systems, 12.8% by planted commercial forest and forestation, 4.2% by biological N fixation and 2.0% by recycling the industrial organic wastes. The ecosystem carbon payback time for historical C losses from South America through LCA strategies may be 56 to 188years, and the adoption of LCA can also increase food and meat production by 615Mton or 17.6Mtonyear^-1 and 56Mton or 1.6Mtonyear^-1, respectively, between 2016 and 2050.

Microbial regulation of the soil carbon cycle: evidence from gene-enzyme relationships

A lack of empirical evidence for the microbial regulation of ecosystem processes, including carbon (C) degradation, hinders our ability to develop a framework to directly incorporate the genetic composition of microbial communities in the enzyme-driven Earth system models. Herein we evaluated the linkage between microbial functional genes and extracellular enzyme activity in soil samples collected across three geographical regions of Australia. We found a strong relationship between different functional genes and their corresponding enzyme activities. This relationship was maintained after considering microbial community structure, total C and soil pH using structural equation modelling. Results showed that the variations in the activity of enzymes involved in C degradation were predicted by the functional gene abundance of the soil microbial community (R²>0.90 in all cases). Our findings provide a strong framework for improved predictions on soil C dynamics that could be achieved by adopting a gene-centric approach incorporating the abundance of functional genes into process models.

Multivariate regulation of soil CO2 and N2 O pulse emissions from agricultural soils

Climate and land-use models project increasing occurrence of high temperature and water deficit in both agricultural production systems and terrestrial ecosystems. Episodic soil wetting and subsequent drying may increase the occurrence and magnitude of pulsed biogeochemical activity, affecting carbon (C) and nitrogen (N) cycles and influencing greenhouse gas (GHG) emissions. In this study, we provide the first data to explore the responses of carbon dioxide (CO2 ) and nitrous oxide (N2 O) fluxes to (i) temperature, (ii) soil water content as percent water holding capacity (%WHC), (iii) substrate availability throughout, and (iv) multiple soil drying and rewetting (DW) events. Each of these factors and their interactions exerted effects on GHG emissions over a range of four (CO2 ) and six (N2 O) orders of magnitude. Maximal CO2 and N2 O fluxes were observed in environments combining intermediate %WHC, elevated temperature, and sufficient substrate availability. Amendments of C and N and their interactions significantly affected CO2 and N2 O fluxes and altered their temperature sensitivities (Q10 ) over successive DW cycles. C amendments significantly enhanced CO2 flux, reduced N2 O flux, and decreased the Q10 of both. N amendments had no effect on CO2 flux and increased N2 O flux, while significantly depressing the Q10 for CO2 , and having no effect on the Q10 for N2 O. The dynamics across DW cycles could be attributed to changes in soil microbial communities as the different responses to wetting events in specific group of microorganisms, to the altered substrate availabilities, or to both. The complex interactions among parameters influencing trace gas fluxes should be incorporated into next generation earth system models to improve estimation of GHG emissions.