Impact of environmental factors on the dependency of litter biomass in carbon cycling of Hooghly estuary,India

Litterfall of the mangroves and its subsequent decomposition is an important mechanism in terms of productivity and nutrient cycle of that ecosystem. Present study emphasizes on the significance of litter biomass and role of environmental factors impacting this process. Mangrove litter undergoes degradation and decomposition and serves as the main source of carbon in different forms within the system, mangrove forests adjacent to the creeks at Sagar Island of the Hooghly–Matla estuarine ecosystem. This system receives a major load of carbon from adjacent mangrove forest in the form of litterfall throughout the year. Keeping in view the effect of environmental factors on litterfall and dynamics of carbon, machine learning method has been applied for this study. Different forms of carbon and environmental factors like temperature, salinity, pH, dissolved oxygen are estimated following standard procedure. Correlation, redundancy analysis and LASSO (Least Absolute Shrinkage and Selection Operator) regression are done in order to know the impact of environmental variables on carbon pool dynamics and effect of litterfall on the carbon pools in soil and water. The results reflect a strong correlation among the studied environmental factors and carbon pool dynamics. It has been revealed from the LASSO prediction results that each carbon pool is sensitive to a separate set of environmental factors.     

土壤碳库构成研究进展

土壤碳库是陆地生态系统中最大的碳库。土壤碳库的构成影响其累积和分解,并直接影响全球陆地生态系统碳平衡,同时也影响土壤质量变化。弄清土壤碳库的组分及构成,是进一步研究土壤碳库变化机制的关键。综述了土壤碳库的组分和构成,对有机碳库进行不稳定性有机碳库和稳定有机碳库归类,描述各类碳库的性质,并对各类碳库的分析测定方法进行了评述。提出在土壤碳构成中增加黑碳和煤炭(碳)以完善土壤有机碳构成框架。在未来研究中,应加强土壤无机碳及湿地土壤和新开发新复垦的重构土壤碳库构成及变化,各类碳库化学构成,交叉重叠的定量关系,碳库之间的转化及在土壤中的迁移,黑碳对土壤碳库稳定性及土壤质量的影响,煤开采扰动区煤炭(碳)对土壤质量的影响及环境效应等科学问题的研究。     

Traceable components of terrestrial carbon storage capacity in biogeochemical models

Biogeochemical models have been developed to account for more and more processes, making their complex structures difficult to be understood and evaluated. Here, we introduce a framework to decompose a complex land model into traceable components based on mutually independent properties of modeled biogeochemical processes. The framework traces modeled ecosystem carbon storage capacity (X_ss) to (i) a product of net primary productivity (NPP) and ecosystem residence time (τ_E). The latter τ_E can be further traced to (ii) baseline carbon residence times (τ′_E), which are usually preset in a model according to vegetation characteristics and soil types, (iii) environmental scalars (ξ), including temperature and water scalars, and (iv) environmental forcings. We applied the framework to the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model to help understand differences in modeled carbon processes among biomes and as influenced by nitrogen processes. With the climate forcings of 1990, modeled evergreen broadleaf forest had the highest NPP among the nine biomes and moderate residence times, leading to a relatively high carbon storage capacity (31.5 kg cm^?2). Deciduous needle leaf forest had the longest residence time (163.3 years) and low NPP, leading to moderate carbon storage (18.3 kg cm^?2). The longest τ_E in deciduous needle leaf forest was ascribed to its longest τ′_E (43.6 years) and small ξ (0.14 on litter/soil carbon decay rates). Incorporation of nitrogen processes into the CABLE model decreased X_ss in all biomes via reduced NPP (e.g., ?12.1% in shrub land) or decreased τ_E or both. The decreases in τ_E resulted from nitrogen‐induced changes in τ′_E (e.g., ?26.7% in C₃ grassland) through carbon allocation among plant pools and transfers from plant to litter and soil pools. Our framework can be used to facilitate data model comparisons and model intercomparisons via tracking a few traceable components for all terrestrial carbon cycle models. Nevertheless, more research is needed to develop tools to decompose NPP and transient dynamics of the modeled carbon cycle into traceable components for structural analysis of land models.     

Lability of organic carbon in lakes of different trophic status

1. We used first‐order kinetic parameters of biological oxygen demand (BOD), the constant of aerobic decomposition (k) and the asymptotic value of BOD (BOD_ult), to characterise the lability of organic carbon pools in six lakes of different trophic state: L. Naroch, L. Miastro and L. Batorino (Belarus), L. Kinneret (Israel), L. Ladoga (Russia) and L. Mendota (U.S.A.). The relative contributions of labile and refractory organic carbon fractions to the pool of total organic carbon (TOC) in these lakes were quantified. We also determined the amounts of labile organic carbon within the dissolved and particulate TOC pools in the three Belarus lakes. 2. Mean annual chlorophyll concentrations (used as a proxy for lake trophic state) ranged from 2.3 to 50.6 μg L⁻¹, labile organic carbon (OC_L = 0.3BOD_ult) from 0.75 to 2.95 mg C L⁻¹ and k from 0.044 to 0.14 day⁻¹. 3. Our data showed that there were greater concentrations of OC_L but lower k values in more productive lakes. 4. In all cases, the DOC fraction dominated the TOC pool. OC_L was a minor component of the TOC pool averaging about 20%, irrespective of lake trophic state. 5. In all the lakes, most (c. 85%) of the DOC pool was refractory, corresponding with published data based on measurements of bacterial production and DOC depletion. In contrast, a larger fraction (27–55%) of the particulate organic carbon (POC) pool was labile. The relative amount of POC in the TOC pool tended to increase with increasing lake productivity. 6. Long‐term BOD incubations can be valuable in quantifying the rates of breakdown of the combined particulate and dissolved organic carbon pools and in characterising the relative proportions of the labile and recalcitrant fractions of these pools. If verified from a larger number of lakes our results could have important general implications.     

The organic carbon dynamics of a moorland catchment in N. W. England

The carbon cycle was quantified in the catchment of Doe House Gill, which drains high-relief moorland, with thin organic-rich soils (leptosols and podzols) 10–25 cm deep, in northern England. The soil C pool of 8,300 g m^-2 is due mainly to humic acid and older humin. If steady state is assumed, and a single soil C pool, the average ¹⁴C content of the whole soil (93% modern) yields a mean carbon residence time of 800 years, although this varied from 300 to 1,600 years in the four samples studied. Stream water fluxes of dissolved and particulate organic carbon (DOC, POC) were 2.5 and 0.4 g m⁻² a⁻¹ respectively in 2002–2003, lower than values for some other upland streams in the UK. The C pool, flux, and isotope data were used, with the assumption of steady state, to calibrate DyDOC, a model that simulates the soil carbon cycle, including the generation and transport of DOC. According to DyDOC, the litter pool (ca. 100 gC m⁻²) turns over quickly, and most (>90%) of the litter carbon is rapidly mineralised. The soil is calculated to gain only 16 gC m⁻² a⁻¹, and to lose the same amount, about 80% as CO₂ and 20% as DOC. From the DO¹⁴C content of 107.5% modern (due to “bomb carbon”) the model could be calibrated by assuming all DOC to come directly from litter, but DOC is more likely a mixture, derived from more than one soil C pool. The seasonal variability exhibited by stream water DOC concentration (maximum in September, minimum in January) is attributed mainly to variations in rainfall and evapotranspiration, rather than in the metabolic production rate of “potential DOC”. The model predicts that, for a Q ₁₀ of 2, the total soil organic C pool would decrease by about 5% if subjected to warming over 200 years. DyDOC predicts higher DOC fluxes in response to increased litter inputs or warming, and can simulate changes in DOC flux due to variations in sorption to soil solids, that might occur due to acidification and its reversal.     

Compartmental modelling of photorespiration and carbon metabolism of water stressed leaves

Abstract Carbon fluxes in photosynthesis and photorespiration of water stressed leaves have been analysed in a steady state model based on the ribulose diphosphate carboxylase (RuDP carboxylase) and RuDP oxygenase enzyme activities and the CO₂ and O₂ concentrations in the leaf. Agreement between predicted and observed photorespiration (Lawlor & Fock, 1975) and C flux in the glycollate pathway is good over much of the range of water stress, but not at severe stress. An alternative source of respiratory CO₂ is suggested to explain the discrepancy. The model suggests that resistance to CO₂ fixation is mainly in the carboxylation reactions, not in CO₂ transport. Using the steady state model, the kinetics of ¹⁴C incorporation into photosynthetic and photorespiratory intermediates are simulated. The predicted rate of ¹⁴C incorporation is faster than observed and delay terms in the model are used to simulate the slow rates of mixing and metabolic reactions. Inactive pools of glycine and serine are suggested to explain the observed specific activities of glycine and serine. Three models of carbon flux between the glycollate pathway, the photosynthetic carbon reduction cycle and sucrose synthesis are considered. The most satisfactory simulation is for glycollate pathway carbon feeding into the PCR cycle pool of 3-phosphoglyceric acid which provides the carbon for sucrose synthesis. Simulation of the specific activity of CO₂ released in photorespiration suggests that a source of unlabelled carbon may contribute to photorespiration.     

Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints

Assumptions of steady‐state conditions in biogeochemical modelling are often invoked because knowledge on the development status of the modelling domain is generally unavailable. Here, we investigate the role of vegetation pool sizes on nonequilibrium conditions through model‐data integration approaches for a set of sites using eddy covariance CO₂ flux data. The study is based on the Carnegie–Ames–Stanford Approach (CASA) model, modified (CASA_G) in order to evaluate the sensitivity of simulated net ecosystem production (NEP) fluxes to vegetation pool sizes. The experimental design is based on the inverse model optimization of different parameter vectors performed at the measurement site level. Each parameter vector prescribes different simulation dynamics that embody different model structural assumptions concerning (non)steady‐state conditions in vegetation and soil carbon pools. We further explore the potential of assimilating biometric constraints through the cost function for sites where in situ information on aboveground biomass or wood pools is available. The integration of biometric data yields marked improvements in the simulation of vegetation C pools compared to single constraints with eddy flux data. Overall, it is necessary to relax both vegetation and soil carbon pools for consistency with the observed data streams. Multiple constraints approaches also leads to variable model performance among the different experimental setups and model structures. We identify and assess the limitations of various model structures and the role of multiple constraints approaches for tackling issues of equifinality. These studies emphasize the need for establishing consistent data sets of fluxes and biometric data for successful model‐data fusion.     

Implications of increased carbon dioxide levels for carbon input and turnover in soils

The complexity of the plant-soil system in its interaction with the changing climate is discussed. It is shown that processes at the level of organic matter inputs into the soil and the fluxes and pools involved in the global cycle are not known in sufficient detail to allow an estimation of the future quantitative shifts. Even the direction in which the level of stored carbon in the soil organic matter pool will develop is not clear. The importance of the nitrogen cycle, which is intimately coupled to the carbon cycle through the turnover of soil organic matter is underlined. In its turn, the mineralisation of soil organic matter takes place at a rate which is highly dependent on the nature of inputs and the availability of mineral nutrients.Aspects of shifts in temperature, changes in cultivation practices (reduced tillage) and unintended spreading of inputs in chemical N-fertilizers are of great importance at a regional and global scale.The complexity of the interactions in the process of mineralisation do require further studies to clarify the point whether a substantial and durable additional storage of carbon in soil organic matter is likely, or that shifts in temperature will cause an overriding acceleration of the mineralisation, and trigger a corresponding net release of carbon.     

土壤和沉积物中黑碳的环境行为及效应研究进展

土壤和沉积物是全球黑碳排放的主要归宿,土壤和沉积物中黑碳具有复杂的环境行为和环境效应。分析了黑碳的概念,指出应以环境意义为出发点去理解黑碳概念的丰富内涵;描述了黑碳形成过程及其对黑碳理化性质的影响,以及基于此的黑碳分类;总结了黑碳来源辨析的若干种常用方法;讨论了黑碳在土壤/沉积物与其他环境介质之间的迁移循环过程,以及在土壤和沉积物内部的迁移行为;探讨了土壤和沉积物中黑碳的降解行为与稳定性,及其与地-气碳氮温室气体通量、土壤稳定碳库的关系,以及在土壤碳循环模型中的作用;综述了土壤和沉积物中黑碳对有机物、重金属和营养盐的吸附行为及主要机制;提出了今后研究的主要方向,以供相关研究者参考。     

湿润亚热带峰丛洼地岩溶土壤系统中碳分布及其转移

以桂林丫吉村岩溶试验场为例,研究了湿润亚热带峰丛洼地表层岩溶带生物量碳库、凋落物碳库、土壤有机碳库（ＳＯＭ）及其活泼性、有机碳分解速率、土壤中ＣＯ２浓度和土壤呼吸ＣＯ２排放,表明岩溶系统中丰富的碳库提供了系统中ＣＯ２的来源,并用δ＾１３Ｃ证实春夏岩溶活跃季节中岩溶输出Ｃ约６０％来自土壤ＣＯ２。由此认为,驱动岩溶作用的ＣＯ２并非直接来自大气ＣＯ２,而是大气－植物－土壤－水碳素转移的结果,因而揭示了土     

Global and regional importance of the tropical peatland carbon pool

Accurate inventory of tropical peatland is important in order to (a) determine the magnitude of the carbon pool; (b) estimate the scale of transfers of peat‐derived greenhouse gases to the atmosphere resulting from land use change; and (c) support carbon emissions reduction policies. We review available information on tropical peatland area and thickness and calculate peat volume and carbon content in order to determine their best estimates and ranges of variation. Our best estimate of tropical peatland area is 441 025 km² (～11% of global peatland area) of which 247 778 km² (56%) is in Southeast Asia. We estimate the volume of tropical peat to be 1758 Gm³ (～18–25% of global peat volume) with 1359 Gm³ in Southeast Asia (77% of all tropical peat). This new assessment reveals a larger tropical peatland carbon pool than previous estimates, with a best estimate of 88.6 Gt (range 81.7–91.9 Gt) equal to 15–19% of the global peat carbon pool. Of this, 68.5 Gt (77%) is in Southeast Asia, equal to 11–14% of global peat carbon. A single country, Indonesia, has the largest share of tropical peat carbon (57.4 Gt, 65%), followed by Malaysia (9.1 Gt, 10%). These data are used to provide revised estimates for Indonesian and Malaysian forest soil carbon pools of 77 and 15 Gt, respectively, and total forest carbon pools (biomass plus soil) of 97 and 19 Gt. Peat carbon contributes 60% to the total forest soil carbon pool in Malaysia and 74% in Indonesia. These results emphasize the prominent global and regional roles played by the tropical peat carbon pool and the importance of including this pool in national and regional assessments of terrestrial carbon stocks and the prediction of peat‐derived greenhouse gas emissions.     

Modelling organic carbon turnover in cleared temperate forest soils converted to maize cropping by using 13C natural abundance measurements

In southwest France, thick humic acid loamy soils have developed from Quaternary silty alluvial deposits. On these soils, most forest lands have been converted to continuous intensive maize cropping and the loss of C upon conversion to intensive agriculture has been shown to be significant. The objective of this study was to determine if a study of natural ¹³C abundance in soil organic C makes possible an improved modelling of organic carbon turnover in the cultivated horizons of soils in this landscape in southwest France. A chronosequence study is realized by comparing C pools and C-13 natural abundance of three forest sites and 14 adjacent agricultural sites, whose ages of cultivation ranged from 3 to 32 yr. ¹³C ratio is found to increase with time of cultivation. The fraction of C coming from the maize crop increases during the first decades of cultivation, and reaches a plateau thereafter. This equilibrium level is reached after a few decades of cultivation. The decrease of the initial C pool is fitted by a simple model assuming that about half of this pool is mineralized during the first yr of cultivation whereas the other half decreases at a slower rate. Therefore, a general bi-compartmental model is proposed for describing the soil organic carbon dynamics in these soils after forest clearing and intensive maize cropping.     

Predictability of the terrestrial carbon cycle

Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool‐dominant transfers, and the first‐order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.     

森林碳库特征及驱动因子分析研究进展

森林碳库作为全球碳库的重要组成部分,在区域以及全球碳循环中发挥重要作用。森林生态系统有机碳库主要由3部分组成:活植物碳库、土壤有机质碳库和死植物体碳库。各碳库时空差异很大,使研究森林碳储存机制存在很大的不确定性。在全球或者区域尺度上,森林生物量(与森林碳储量密切相关)呈现出清晰的分布格局,但对于这些格局的驱动因子与地上生物量的相关关系和尺度外推程度尚有很大的争议。分别讨论了气候、土壤、地形和生物因子对地上生物量的影响;而后,从各生态因子与地上生物量之间的直接或者间接关系入手,分析了各因子与生物量之间的交互作用,认为目前运用相同的处理方式来研究环境因子和生物因子对森林生物量的影响是不合适的。文章最后介绍两种新的研究方法:层次模型和生境分类,以期用来重新评估生态因子对森林生物量的影响,有助于更准确的了解森林碳储存机制。     

Uncertainty analysis of forest carbon sink forecast with varying measurement errors: a data assimilation approach

Aims Accurate forecast of ecosystem states is critical for improving natural resource management and climate change mitigation. Assimilating observed data into models is an effective way to reduce uncertainties in ecological forecasting. However, influences of measurement errors on parameter estimation and forecasted state changes have not been carefully examined. This study analyzed the parameter identifiability of a process-based ecosystem carbon cycle model, the sensitivity of parameter estimates and model forecasts to the magnitudes of measurement errors and the information contributions of the assimilated data to model forecasts with a data assimilation approach.Methods We applied a Markov Chain Monte Carlo method to assimilate eight biometric data sets into the Terrestrial ECOsystem model. The data were the observations of foliage biomass, wood biomass, fine root biomass, microbial biomass, litter fall, litter, soil carbon and soil respiration, collected at the Duke Forest free-air CO₂ enrichment facilities from 1996 to 2005. Three levels of measurement errors were assigned to these data sets by halving and doubling their original standard deviations.Important findings Results showed that only less than half of the 30 parameters could be constrained, though the observations were extensive and the model was relatively simple. Higher measurement errors led to higher uncertainties in parameters estimates and forecasted carbon (C) pool sizes. The long-term predictions of the slow turnover pools were affected less by the measurement errors than those of fast turnover pools. Assimilated data contributed less information for the pools with long residence times in long-term forecasts. These results indicate the residence times of C pools played a key role in regulating propagation of errors from measurements to model forecasts in a data assimilation system. Improving the estimation of parameters of slow turnover C pools is the key to better forecast long-term ecosystem C dynamics.     

Detection of a variable intracellular acid‐labile carbon pool in Thalassiosira weissflogii (Heterokontophyta) and Emiliania huxleyi (Haptophyta) in response to changes in the seawater carbon system

Accumulation of an intracellular pool of carbon (C_i pool) is one strategy by which marine algae overcome the low abundance of dissolved CO₂ (CO_2(aq)) in modern seawater. To identify the environmental conditions under which algae accumulate an acid‐labile C_i pool, we applied a ¹⁴C pulse‐chase method, used originally in dinoflagellates, to two new classes of algae, coccolithophorids and diatoms. This method measures the carbon accumulation inside the cells without altering the medium carbon chemistry or culture cell density. We found that the diatom Thalassiosira weissflogii [(Grunow) G. Fryxell & Hasle] and a calcifying strain of the coccolithophorid Emiliania huxleyi [(Lohmann) W. W. Hay & H. P. Mohler] develop significant acid‐labile C_i pools. Ci pools are measureable in cells cultured in media with 2–30 µmol l^?1 CO_2(aq), corresponding to a medium pH of 8.6–7.9. The absolute C_i pool was greater for the larger celled diatoms. For both algal classes, the C_i pool became a negligible contributor to photosynthesis once CO_2(aq) exceeded 30 µmol l^?1. Combining the ¹⁴C pulse‐chase method and ¹⁴C disequilibrium method enabled us to assess whether E. huxleyi and T. weissflogii exhibited thresholds for foregoing accumulation of DIC or reduced the reliance on bicarbonate uptake with increasing CO_2(aq). We showed that the C_i pool decreases with higher CO₂:HCO₃^? uptake rates.     

Spatial distributions of biomass and carbon density in natural grasslands of Hebei,China

分析不同草地类型生物量与碳密度空间分布特征及其影响因素, 揭示草地植物碳库的变化规律, 对于了解我国草地生态系统碳汇具有重要意义。2011-2013年以河北省天然草地为研究对象, 调查了不同草地类型的地上活体生物量、凋落物生物量和根系生物量以及各组分的碳密度。结果表明: 温性草原、温性草甸、温性山地草甸、低地盐化草甸、暖性草丛和暖性灌草丛6种草地类型的总生物量差异显著, 其中低地盐化草甸总生物量最高, 为2 770.2 g·m ^-2, 而温性草原最低, 为747.6 g·m ^-2, 前者约为后者的3.7倍; 地上活体生物量最大的是低地盐化草甸, 其次是暖性灌草丛和温性山地草甸, 最小的是温性草原, 分别为285.0、235.1、203.1和110.6 g·m ^-2; 凋落物生物量也是低地盐化草甸最大, 其次是温性山地草甸和温性草甸, 分别为584.0、187.9和91.0 g·m ^-2。6种草地类型的根系生物量均大于地上生物量, 是地上生物量的1.9-4.3倍, 不同草地类型根冠比的平均值为3.1; 低地盐化草甸的根系生物量最高, 为1901.3 g·m ^-2, 温性草原的根系生物量最低, 只有低地盐化草甸的1/3。在各类草地生物量碳密度方面, 低地盐化草甸的地上活体碳密度、凋落物碳密度与根系碳密度均为最大, 分别为132.7、81.2和705.9 g C·m ^-2。草地地上生物量、凋落物生物量和根系生物量以及总生物量均随海拔的升高先减少而后增加(p < 0.05); 草地生态系统总生物量和根系生物量随大于10 ℃积温的增加先降低而后升高(p < 0.01)。该研究中暖性灌草丛多分布在石质山区, 土层很薄, 植物地上生物量和根系生物量都比土层较厚的草甸草原低。可见, 在较大区域比较不同草地类型生物量时, 应综合考虑气候、土壤、地理等因素。     

河北省天然草地生物量和碳密度空间分布格局

分析不同草地类型生物量与碳密度空间分布特征及其影响因素, 揭示草地植物碳库的变化规律, 对于了解我国草地生态系统碳汇具有重要意义。2011-2013年以河北省天然草地为研究对象, 调查了不同草地类型的地上活体生物量、凋落物生物量和根系生物量以及各组分的碳密度。结果表明: 温性草原、温性草甸、温性山地草甸、低地盐化草甸、暖性草丛和暖性灌草丛6种草地类型的总生物量差异显著, 其中低地盐化草甸总生物量最高, 为2 770.2 g·m ^-2, 而温性草原最低, 为747.6 g·m ^-2, 前者约为后者的3.7倍; 地上活体生物量最大的是低地盐化草甸, 其次是暖性灌草丛和温性山地草甸, 最小的是温性草原, 分别为285.0、235.1、203.1和110.6 g·m ^-2; 凋落物生物量也是低地盐化草甸最大, 其次是温性山地草甸和温性草甸, 分别为584.0、187.9和91.0 g·m ^-2。6种草地类型的根系生物量均大于地上生物量, 是地上生物量的1.9-4.3倍, 不同草地类型根冠比的平均值为3.1; 低地盐化草甸的根系生物量最高, 为1901.3 g·m ^-2, 温性草原的根系生物量最低, 只有低地盐化草甸的1/3。在各类草地生物量碳密度方面, 低地盐化草甸的地上活体碳密度、凋落物碳密度与根系碳密度均为最大, 分别为132.7、81.2和705.9 g C·m ^-2。草地地上生物量、凋落物生物量和根系生物量以及总生物量均随海拔的升高先减少而后增加(p < 0.05); 草地生态系统总生物量和根系生物量随大于10 ℃积温的增加先降低而后升高(p < 0.01)。该研究中暖性灌草丛多分布在石质山区, 土层很薄, 植物地上生物量和根系生物量都比土层较厚的草甸草原低。可见, 在较大区域比较不同草地类型生物量时, 应综合考虑气候、土壤、地理等因素。     

Investigating alginate production and carbon utilization in Pseudomonas fluorescens SBW25 using mass spectrometry-based metabolic profiling

Metabolic profiling of Pseudomonas fluorescens SBW25 and various mutants derived thereof was performed to explore how the bacterium adapt to changes in carbon source and upon induction of alginate synthesis. The experiments were performed at steady-state conditions in nitrogen-limited chemostats using either fructose or glycerol as carbon source. Carbon source consumption was up-regulated in the alginate producing mutant with inactivated anti-sigma factor MucA. The mucA- mutants (also non-alginate producing mucA- control strains) had a higher dry weight yield on carbon source implying a change in carbon and energy metabolism due to the inactivation of the anti-sigma factor MucA. Both LC–MS/MS and GC–MS methods were used for quantitative metabolic profiling, and major reorganization of primary metabolite pools in both an alginate producing and a carbon source dependent manner was observed. Generally, larger changes were observed among the phosphorylated glycolytic metabolites, the pentose phosphate pathway metabolites and the nucleotide pool than among amino acids and citric acid cycle compounds. The most significant observation at the metabolite level was the significantly reduced energy charge of the mucA- mutants (both alginate producing and non-producing control strains) compared to the wild type strain. This reduction was caused more by a strong increase in the AMP pool than changes in the ATP and ADP pools. The alginate-producing mucA- mutant had a slightly increased GTP pool, while the GDP and GMP pools were strongly increased compared to non-producing mucA- strains and to the wild type. Thus, whilst changes in the adenosine phosphate nucleotide pool are attributed to the mucA inactivation, adjustments in the guanosine phosphate nucleotide pool are consequences of the GTP-dependent alginate production induced by the mucA inactivation. This metabolic profiling study provides new insight into carbon and energy metabolism of the alginate producer P. fluorescens.     

Separating soil CO2 efflux into C-pool-specific decay rates via inverse analysis of soil incubation data

Soil organic matter (SOM) is heterogeneous in structure and has been considered to consist of various pools with different intrinsic turnover rates. Although those pools have been conceptually expressed in models and analyzed according to soil physical and chemical properties, separation of SOM into component pools is still challenging. In this study, we conducted inverse analyses with data from a long-term (385 days) incubation experiment with two types of soil (from plant interspace and from underneath plants) to deconvolute soil carbon (C) efflux into different source pools. We analyzed the two datasets with one-, two- and three-pool models and used probability density functions as a criterion to judge the best model to fit the datasets. Our results indicated that soil C release trajectories over the 385 days of the incubation study were best modeled with a two-pool C model. For both soil types, released C within the first 10 days of the incubation study originated from the labile pool. Decomposition of C in the recalcitrant pool was modeled to contribute to the total CO₂ efflux by 9–11 % at the beginning of the incubation. At the end of the experiment, 75–85 % of the initial soil organic carbon (SOC) was modeled to be released over the incubation period. Our modeling analysis also indicated that the labile C-pool in the soil underneath plants was larger than that in soil from interspace. This deconvolution analysis was based on information contained in incubation data to separate carbon pools and can facilitate integration of results from incubation experiments into ecosystem models with improved parameterization.