Microbiology of flooded rice paddies

Flooded rice paddies are one of the major biogenic sources of atmospheric methane. Apart from this contribution to the 'greenhouse' effect, rice paddy soil represents a suitable model system to study fundamental aspects of microbial ecology, such as diversity, structure, and dynamics of microbial communities as well as structure-function relationships between microbial groups. Flooded rice paddy soil can be considered as a system with three compartments (oxic surface soil, anoxic bulk soil, and rhizosphere) characterized by different physio-chemical conditions. After flooding, oxygen is rapidly depleted in the bulk soil. Anaerobic microorganisms, such as fermentative bacteria and methanogenic archaea, predominate within the microbial community, and thus methane is the final product of anaerobic degradation of organic matter. In the surface soil and the rhizosphere well-defined microscale chemical gradients can be measured. The oxygen profile seems to govern gradients of other electron acceptors (e.g., nitrate, iron(III), and sulfate) and reduced compounds (e.g., ammonium, iron(II), and sulfide). These gradients provide information about the activity and spatial distribution of functional groups of microorganisms. This review presents the current knowledge about the highly complex microbiology of flooded rice paddies. In Section 2 we describe the predominant microbial groups and their function with particular regard to bacterial populations utilizing polysaccharides and simple sugars, and to the methanogenic archaea. Section 3 describes the spatial and temporal development of microscale chemical gradients measured in experimentally defined model systems, including gradients of oxygen and dissolved and solid-phase iron(III) and iron(II). In Section 4, the results of measurements of microscale gradients of oxygen, pH, nitrate-nitrite, and methane in natural rice fields and natural rice soil cores taken to the laboratory will be presented. Finally, perspectives of future research are discussed (Section 5).     

内蒙古锡林河流域草原土壤有机碳及氮素的空间异质性分析

生态系统中生物或非生物因子在空间分布上普遍具有空间异质性。本文应用地统计学的基本原理与方法（半方差分析和无偏插值）对研究区域内的草原土壤有机碳（ＳＯＣ）和全氮（ＴＮ）空间异质性进行了分析。研究结果表明：ＳＯＣ和ＴＮ的平均含量分别为１．５５５％和０．１３３３％,平均变异系数分别为１１．２％和１２．４％,二者在空间分布方面均具有明显的空间相关性,其空间相关尺度分别为８．１９ｍ和８．６９ｍ。在此基础上,应用空间局部内插法,绘制了两个因子的空间等值分布图。     

Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators

Sustainable land management and land use planning require reliable information about the spatial distribution of the physical and chemical soil properties affecting both landscape processes and services. Although many studies have been conducted to identify the spatial patterns of soil property distribution on various scales and in various landscapes, only little is known about the relationships underlying the spatial distribution of soil properties in intensively used, finely structured paddy soil landscapes in the southeastern part of China. In order to provide adequate soil information for the modelling of landscape processes, such as soil water movement, nutrient leaching, soil erosion and plant growth, this study investigates to what extent cheap and readily available ancillary information derived from digital elevation models and remote sensing data can be used to support soil mapping and to indicate soil characteristics on the landscape scale. This article focuses on the spatial prediction of the total carbon and nitrogen content and of physical soil properties such as topsoil silt, sand and clay content, topsoil depth and plough pan thickness. Correlation analyses indicate that, on the one side, the distribution of C, N and silt contents is quite closely related to the NDVI of vegetated surfaces and that, on the other side, it corresponds significantly to terrain attributes such as relative elevation, elevation above nearest drainage channel and topographical wetness index. Geostatistical analyses furthermore reflect a moderately structured spatial correlation of these soil variables. The combined use of the above mentioned terrain variables and the NDVI in a multiple linear regression accounted for 29% (silt) to 41% (total C) of the variance of these soil properties. In order to select the best prediction method to accurately map soil property distribution, we compared the performance of different regionalization techniques, such as multi-linear regression, simple kriging, inverse distance to a power, ordinary kriging and regression kriging. Except for the prediction of topsoil clay content, in all cases regression kriging model “C” performed best. Compared to simple kriging, the spatial prediction was improved by up to 14% (total C), 13% (total N) and 10% (silt). Since the autocorrelation lengths of these spatially well correlated soil variables range between three and five times the soil sampling density, we consider regression kriging model “C” to be a suitable method for reducing the soil sampling density. It should help to save time and costs when doing soil mapping on the landscape scale, even in intensively used paddy soil landscapes.     

Spatial interpolation of N concentrations and δ15N values in the moss Hypnum cupressiforme collected in the forests of Slovenia

Mosses were collected from 103 locations and analyzed for N concentration and δ¹⁵N values. At each location, the samples were collected from two types of site: under the tree canopies and in the adjacent forest openings. A spatial correlation was detected, and ordinary kriging could be applied only to the N in the mosses that were collected in the openings. In contrast, a spatial correlation was not detected for N in the mosses that were collected under the canopies or for the δ¹⁵N of the mosses at either type of site. For those, the spatial interpolation was calculated as the sum of the regression predictions and the inverse distance weighted interpolation of residuals. The maps for the N at both site types (canopy/open) identified similar areas with increased N concentrations. The sole exception was the mosses collected under the canopy, for which some local emitters of NO_x were also identified.     

长白山低山区森林土壤有机碳及养分空间异质性

以吉林延边汪清林业局金仓林场境内森林土壤为对象,采用多元线性回归方法和地统计学回归克里格方法,研究了土壤有机碳及养分的垂直分布规律,预测了其空间分布,并对预测结果进行插值.结果表明: 0～60 cm深度土壤有机碳密度为(16.14±4.58) kg·m^-2.随土壤深度增加,土壤有机碳含量、有机碳密度以及土壤全N、全P、全K、有效P及速效K含量都呈减小趋势,其中不同土层间土壤有机碳含量、有机碳密度差异显著(P<0.01).0～60 cm土层土壤有机碳含量和碳密度的拟合方程中,地形因子中高程和坡向余弦值是最优的拟合因子,方程的决定系数分别为0.34和0.39(P<0.01).0～20和0～60 cm土层的半方差函数模型分别为高斯模型和指数模型,利用回归克里格插值方法得到土壤有机碳的空间分布图.与普通克里格法相比,回归克里格法的空间预测精度改进了18%～58%.利用回归克里格插值方法预测了土壤全N的空间分布特征.     

Bioclimatic and vegetation mapping of a topographically complex oceanic island applying different interpolation techniques

Different spatial interpolation techniques have been applied to construct objective bioclimatic maps of La Palma, Canary Islands. Interpolation of climatic data on this topographically complex island with strong elevation and climatic gradients represents a challenge. Furthermore, meteorological stations are not evenly distributed over the island, with few stations at high elevations. We carried out spatial interpolations of the compensated thermicity index (Itc) and the annual ombrothermic Index (Io), in order to obtain appropriate bioclimatic maps by using automatic interpolation procedures, and to establish their relation to potential vegetation units for constructing a climatophilous potential natural vegetation map (CPNV). For this purpose, we used five interpolation techniques implemented in a GIS: inverse distance weighting (IDW), ordinary kriging (OK), ordinary cokriging (OCK), multiple linear regression (MLR) and MLR followed by ordinary kriging of the regression residuals. Two topographic variables (elevation and aspect), derived from a high-resolution digital elevation model (DEM), were included in OCK and MLR. The accuracy of the interpolation techniques was examined by the results of the error statistics of test data derived from comparison of the predicted and measured values. Best results for both bioclimatic indices were obtained with the MLR method with interpolation of the residuals showing the highest R ² of the regression between observed and predicted values and lowest values of root mean square errors. MLR with correction of interpolated residuals is an attractive interpolation method for bioclimatic mapping on this oceanic island since it permits one to fully account for easily available geographic information but also takes into account local variation of climatic data.     

Ensemble Learning for Spatial Interpolation of Soil Potassium Content Based on Environmental Information

One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful.     

Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data

Freshwater ecosystems are declining faster than their terrestrial and marine counterparts because of physical pressures on habitats. European legislation requires member states to achieve ecological targets through the effective management of freshwater habitats. Maps of habitats across river networks would help diagnose environmental problems and plan for the delivery of improvement work. Existing habitat mapping methods are generally time consuming, require experts and are expensive to implement. Surveys based on sampling are cheaper but provide patchy representations of habitat distribution. In this study, we present a method for mapping habitat indices across networks using semi-quantitative data and a geostatistical technique called regression kriging. The method consists of the derivation of habitat indices using multivariate statistical techniques that are regressed on map-based covariates such as altitude, slope and geology. Regression kriging combines the Generalised Least Squares (GLS) regression technique with a spatial analysis of model residuals. Predictions from the GLS model are ‘corrected’ using weighted averages of model residuals following an analysis of spatial correlation. The method was applied to channel substrate data from the River Habitat Survey in Great Britain. A Channel Substrate Index (CSI) was derived using Correspondence Analysis and predicted using regression kriging. The model explained 74% of the main sample variability and 64% in a test sample. The model was applied to the English and Welsh river network and a map of CSI was produced. The proposed approach demonstrates how existing national monitoring data and geostatistical techniques can be used to produce continuous maps of habitat indices at the national scale.     

A geostatistical approach to the field-scale pattern of heterotrophic soil CO2 emission using covariates

Soil heterotrophic respiration fluxes at field scale may exhibit a substantial spatial variability. The aim of this study was (1) to elucidate the role of soil temperature and different carbon fractions on heterotrophic soil respiration and (2) to test by which of three different statistical approaches (multiple regression, external drift kriging and simulated annealing) such influences may be best represented. Chamber-based measurements of respiration fluxes were carried out within a 180 × 40 m bare soil plot. Soil temperature was measured simultaneously to the flux measurements. Further, we recorded total soil organic carbon content, apparent electrical conductivity as well as mid-infrared spectroscopy-based carbon fractions as co-variates in addition to basic soil properties like stone content and texture. A stepwise multiple linear regression procedure was used to spatially predict bare soil respiration from the co-variates. The results showed that the particulate organic matter (POM) fraction and terrain elevation were able to explain the spatial pattern of heterotrophic soil respiration (R ² = 0.45). In a second step we applied external drift kriging to determine the improvement of using co-variates in an estimation procedure in comparison to ordinary kriging. The maximum relative improvement using the co-variates in terms of the root mean square error was 16%. In a third step we applied simulated annealing to perform stochastic simulations conditioned with external drift kriging to generate more realistic spatial patterns of heterotrophic respiration at plot scale. The conditional stochastic simulations revealed a significantly improved reproduction of the probability density function, the G-statistics value increased from 0.36 to 0.92. Further, the error in the reproduction of the semivariogram of the original point data decreased by more than one order of magnitude. All this confirmed that the mapping of soil respiration patterns may be significantly improved when considering terrain elevation and spatial heterogeneity of POM in combination with a conditional stochastic simulation.     

Pervasive Local-Scale Tree-Soil Habitat Association in a Tropical Forest Community

We examined tree-soil habitat associations in lowland forest communities at Paracou, French Guiana. We analyzed a large dataset assembling six permanent plots totaling 37.5 ha, in which extensive LIDAR-derived topographical data and soil chemical and physical data have been integrated with precise botanical determinations. Map of relative elevation from the nearest stream summarized both soil fertility and hydromorphic characteristics, with seasonally inundated bottomlands having higher soil phosphate content and base saturation, and plateaus having higher soil carbon, nitrogen and aluminum contents. We employed a statistical test of correlations between tree species density and environmental maps, by generating Monte Carlo simulations of random raster images that preserve autocorrelation of the original maps. Nearly three fourths of the 94 taxa with at least one stem per ha showed a significant correlation between tree density and relative elevation, revealing contrasted species-habitat associations in term of abundance, with seasonally inundated bottomlands (24.5% of species) and well-drained plateaus (48.9% of species). We also observed species preferences for environments with or without steep slopes (13.8% and 10.6%, respectively). We observed that closely-related species were frequently associated with different soil habitats in this region (70% of the 14 genera with congeneric species that have a significant association test) suggesting species-habitat associations have arisen multiple times in this tree community. We also tested if species with similar habitat preferences shared functional strategies. We found that seasonally inundated forest specialists tended to have smaller stature (maximum diameter) than species found on plateaus. Our results underline the importance of tree-soil habitat associations in structuring diverse communities at fine spatial scales and suggest that additional studies are needed to disentangle community assembly mechanisms related to dispersal limitation, biotic interactions and environmental filtering from species-habitat associations. Moreover, they provide a framework to generalize across tropical forest sites.     

In Situ Spatial Patterns of Soil Bacterial Populations,Mapped at Multiple Scales,in an Arable Soil

Very little is known about the spatial organization of soil microbes across scales that are relevant both to microbial function and to field-based processes. The spatial distributions of microbes and microbially mediated activity have a high intrinsic variability. This can present problems when trying to quantify the effects of disturbance, management practices, or climate change on soil microbial systems and attendant function. A spatial sampling regime was implemented in an arable field. Cores of undisturbed soil were sampled from a 3 × 3 × 0.9 m volume of soil (topsoil and subsoil) and a biological thin section, in which the in situ distribution of bacteria could be quantified, prepared from each core. Geostatistical analysis was used to quantify the nature of spatial structure from micrometers to meters and spatial point pattern analysis to test for deviations from complete spatial randomness of mapped bacteria. Spatial structure in the topsoil was only found at the microscale (micrometers), whereas evidence for nested scales of spatial structure was found in the subsoil (at the microscale, and at the centimeter to meter scale). Geostatistical ranges of spatial structure at the micro scale were greater in the topsoil and tended to decrease with depth in the subsoil. Evidence for spatial aggregation in bacteria was stronger in the topsoil and also decreased with depth in the subsoil, though extremely high degrees of aggregation were found at very short distances in the deep subsoil. The data suggest that factors that regulate the distribution of bacteria in the subsoil operate at two scales, in contrast to one scale in the topsoil, and that bacterial patches are larger and more prevalent in the topsoil.     

Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping

Information on the spatial distribution and composition of biological communities is essential in designing effective strategies for biodiversity conservation and management. Reliable maps of species richness across the landscape can be useful tools for these purposes. Acquiring such information through traditional survey techniques is costly and logistically difficult. The kriging interpolation method has been widely used as an alternative to predict spatial distributions of species richness, as long as the data are spatially dependent. However, even when this requirement is met, researchers often have few sampled sites in relation to the area to be mapped. Remote sensing provides an inexpensive means to derive complete spatial coverage for large areas and can be extremely useful for estimating biodiversity. The aim of this study was to combine remotely sensed data with kriging estimates (hybrid procedures) to evaluate the possibility of improving the accuracy of tree species richness maps. We did this through the comparison of the predictive performance of three hybrid geostatistical procedures, based on tree species density recorded in 141 sampling quadrats: co-kriging (COK), kriging with external drift (KED), and regression kriging (RK). Reflectance values of spectral bands, computed NDVI and texture measurements of Landsat 7 TM imagery were used as ancillary variables in all methods. The R² values of the models increased from 0.35 for ordinary kriging to 0.41 for COK, and from 0.39 for simple regression estimates to 0.52 and 0.53 when using simple KED and RK, respectively. The R² values of the models also increased from 0.60 for multiple regression estimates to 0.62 and 0.66 when using multiple KED and RK, respectively. Overall, our results demonstrate that these procedures are capable of greatly improving estimation accuracy, with multivariate RK being clearly superior, because it produces the most accurate predictions, and because of its flexibility in modeling multivariate relationships between tree richness and remotely sensed data. We conclude that this is a valuable tool for guiding future efforts aimed at conservation and management of highly diverse tropical forests.     

Geostatistical analysis of soil properties in a secondary tropical dry forest,St. Lucia,West Indies

Spatial variability of soil properties directly influences forest growth. However, spatial variation in soil properties has not been studied within tropical dry forests. As such, it is unclear whether soil properties, like moisture and N availability, display spatial variation at scales similar to that of other ecosystems. To gain insight into this variation, we established a 56 × 56 m sampling grid in tropical dry forest on the Caribbean island of St. Lucia. Samples collected at 4-m intervals were analyzed for forest floor mass, soil texture, pH, organic C, net N mineralization, net nitrification and available P. Geostatistical procedures were used to determine spatial autocorrelation of the aforementioned properties and processes. Semivariogram parameters were used in a block kriging procedure to produce spatial maps of soil properties. At the scale of our study, most soil properties exhibited spatial autocorrelation at distances of 24 m or less. Varying degrees of similarity were found between patterns of forest floor mass, organic C, net N mineralization, net nitrification and available P. No similarity was found between soil texture or pH and other properties. Fine-scale spatial patterns of net N mineralization and net nitrification are likely driven by overstory litter inputs, rather than variation in soil texture and water availability.     

Spatial distribution of the soil organic carbon pool in a Holm oak dehesa in Spain

Aims

Dehesas are agroforestry systems characterized by scattered trees among pastures, crops and/or fallows. A study at a Spanish dehesa has been carried out to estimate the spatial distribution of the soil organic carbon stock and to assess the influence of the tree cover.

Methods

The soil organic carbon stock was estimated from the five uppermost cm of the mineral soil with high spatial resolution at two plots with different grazing intensities. The Universal Kriging technique was used to assess the spatial distribution of the soil organic carbon stocks, using tree coverage within a buffering area as an auxiliary variable.

Results

A significant positive correlation between tree presence and soil organic carbon stocks up to distances of around 8 m from the trees was found. The tree crown cover within a buffer up to a distance similar to the crown radius around the point absorbed 30 % of the variance in the model for both grazing intensities, but residual variance showed stronger spatial autocorrelation under regular grazing conditions.

Conclusions

Tree cover increases soil organic carbon stocks, and can be satisfactorily estimated by means of crown parameters. However, other factors are involved in the spatial pattern of the soil organic carbon distribution. Livestock plays an interactive role together with tree presence in soil organic carbon distribution.     

基于沿海蝗区飞蝗卵块分布格局的土壤空间异质性

以沿海蝗区南大港水库为研究区域,通过连续2a野外450m,50m规则栅格取样,利用地统计学方法,在GIS平台下,分析东亚飞蝗卵块分布格局与土壤空间异质性的研究,结果表明：（1）飞蝗卵块呈斑块、聚集分布,并具有明显的空间异质性,其空间自相关范围为390m,且主要分布在南大港水库的中部和东部,少数分布在南部,而在西部和北部地块几乎没有卵的分布。（2）研究区域内土壤含盐量和5cm含水量由空间自相关引起的空间异质性分别占总空间异质性的76.15%、78.04%,即表现出较强的空间相关性,空间自相关范围分别为594m,621m,土壤有机质和pH值具有中等强度的空间相关性,由空间自相关引起的空间变异分别为61.85%和57.19%,空间变异尺度分别为1014m,1368m。（3）研究区域内卵块主要集中在土壤含盐量较低（〈1.9%）、含水量适中（10.1%~29.9%）的中部和东部地块,而在土壤含水量较高（〉30%）的西部和盐分过重（〉3%）的北部几乎没有卵块的分布。即在一定程度上,蝗区土壤理化特性的空间异质决定了飞蝗卵块的空间分布特点。（4）卵块和土壤理化特性的空间格局图不仅为野外抽样调查、蝗灾预测预报及其防治提供科学依据,而且对蝗区环境改造具有重要指导作用。     

Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function

Understanding spatial variation of soil organic carbon (SOC) in three-dimensional direction is helpful for land use management. Due to the effect of profile depths and soil texture on vertical distribution of SOC, the stationary assumption for SOC cannot be met in the vertical direction. Therefore the three-dimensional (3D) ordinary kriging technique cannot be directly used to map the distribution of SOC at a regional scale. The objectives of this study were to map the 3D distribution of SOC at a regional scale by combining kriging method with the profile depth function of SOC (KPDF), and to explore the effects of soil texture and land use type on vertical distribution of SOC in a fluvial plain. A total of 605 samples were collected from 121 soil profiles (0.0 to 1.0 m, 0.20 m increment) in Quzhou County, China and SOC contents were determined for each soil sample. The KPDF method was used to obtain the 3D map of SOC at the county scale. The results showed that the exponential equation well described the vertical distribution of mean values of the SOC contents. The coefficients of determination, root mean squared error and mean prediction error between the measured and the predicted SOC contents were 0.52, 1.82 and -0.24 g kg^-1 respectively, suggesting that the KPDF method could be used to produce a 3D map of SOC content. The surface SOC contents were high in the mid-west and south regions, and low values lay in the southeast corner. The SOC contents showed significant positive correlations between the five different depths and the correlations of SOC contents were larger in adjacent layers than in non-adjacent layers. Soil texture and land use type had significant effects on the spatial distribution of SOC. The influence of land use type was more important than that of soil texture in the surface soil, and soil texture played a more important role in influencing the SOC levels for 0.2-0.4 m layer.     

Environmental correlates for tropical tree diversity and distribution patterns in Borneo

Aim Identify environmental correlates for tropical tree diversity and composition. Location Borneo, Southeast Asia. Methods A GIS‐environmental database with 5 arc minute (c. 10 × 10 km) resolution was combined with tree inventory data. Tree diversity, phylogenetic diversity (PD) and the two main compositional gradients were determined for 46 tree inventories. Akaike's information criterion and a data jackknifing procedure were used to select 50 explanatory models for diversity and composition gradients. The average of these models was used as our final diversity and compositional model. We applied Moran's I to detect spatial autocorrelation of residuals. Results Tree diversity, PD and the two main compositional gradients in Borneo were all significantly correlated with the environment. Tree diversity correlated negatively with elevation, soil depth, soil coarseness (texture) and organic carbon content, whereas it correlated positively with soil C:N ratio, soil pH, moisture storage capacity and annual rainfall. Tree PD was correlated positively with elevation and temperature seasonality and was largely determined by gymnosperms. However, angiosperm PD also correlated positive with elevation. Compositional patterns were strongly correlated with elevation but soil texture, cation‐exchange‐capacity, C:N ratio, C and N content and drainage were also important next to rainfall seasonality and El Niño Southern Oscillation drought impact. Main conclusions Although elevation is the most important correlate for diversity and compositional gradients in Borneo, significant additional variability is explained by soil characteristics (texture, carbon content, pH, depth, drainage and nutrient status) and climate (annual rainfall, rainfall seasonality and droughts). The identified environmental correlates for diversity and composition gradients correspond to those found in other tropical regions of the world. Differences between the regions are mainly formed by differences in the relative importance of the environmental variables in explaining diversity and compositional gradients.     

Towards an integrated global framework to assess the impacts of land use and management change on soil carbon: current capability and future vision

Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodologies commonly underpin project‐scale carbon accounting for changes in land use and management and are used in frameworks for Life Cycle Assessment and carbon footprinting of food and energy crops. These methodologies were intended for use at large spatial scales. This can introduce error in predictions at finer spatial scales. There is an urgent need for development and implementation of higher tier methodologies that can be applied at fine spatial scales (e.g. farm/project/plantation) for food and bioenergy crop greenhouse gas (GHG) accounting to facilitate decision making in the land‐based sectors. Higher tier methods have been defined by IPCC and must be well evaluated and operate across a range of domains (e.g. climate region, soil type, crop type, topography), and must account for land use transitions and management changes being implemented. Furthermore, the data required to calibrate and drive the models used at higher tiers need to be available and applicable at fine spatial resolution, covering the meteorological, soil, cropping system and management domains, with quantified uncertainties. Testing the reliability of the models will require data either from sites with repeated measurements or from chronosequences. We review current global capability for estimating changes in soil carbon at fine spatial scales and present a vision for a framework capable of quantifying land use change and management impacts on soil carbon, which could be used for addressing issues such as bioenergy and biofuel sustainability, food security, forest protection, and direct/indirect impacts of land use change. The aim of this framework is to provide a globally accepted standard of carbon measurement and modelling appropriate for GHG accounting that could be applied at project to national scales (allowing outputs to be scaled up to a country level), to address the impacts of land use and land management change on soil carbon.     

森林不同土壤层全氮空间变异特征

应用经典统计学和地统计学方法,分析了八达岭地区土壤全氮(TN)在不同层次(A,B,C)的空间变异特征。同时结合地理信息系统(GIS),分析了该地区植被类型和土壤TN之间的关系。应用分类回归树模型(classification and regression trees,CART)分析了土壤TN和海拔与植被分布格局的关系。得到以下结论:(1)TN在A、B、C层平均值分别为2.94、1.30,0.63 g/kg,变异系数(CV)分别为33%、33%、45%,都表现为中等变异。(2)TN在不同土层的变异函数理论模型符合球状模型,TN在A层为弱空间相关,在B、C层为中等空间相关。(3)泛可里格插值表明,TN在不同层次都表现出了明显的空间分布趋势。不同植被类型所对应土壤全氮的空间分布则各不相同。(4)CART研究结果表明,该区植被类型分布格局可大致划分为四大部分。可初步确定海拔725m,TN含量4.23 g/kg和5.69 g/kg为影响该区植被分布格局的重要参考值。     

Roots,nutrients and their relationship to spatial patterns

Ecosystem sustainability and resilience after a disturbance may be regulated by processes occurring at smaller spatial scales. The matrix of different spatial environments are created by (1) individual plants that accumulate higher concentrations of specific nutrients, trace elements or defensive plant secondary chemicals and thereby modify the chemistry of their ecological space and/or rates of processes, (2) the presence of structures (e.g., coarse woody debris) that may buffer some micro-environments from disturbances by functioning as a hospitable environment or as a reservoir for mycorrhizal fungi to sustain them into the next phase of stand development, and (3) chemical changes in soils during soil development which may result in distinct soil chemical environments. The response of the plants or change in the sustainability of carbon and nutrient cycles may be expressed more strongly at this smaller ecological space of an individual plant and furthermore must be frequently examined separately by the above- and belowground space of that individual.This paper will present three case studies from temperate and tropical forest ecosystems which suggest the importance of studying plant growth and nutrient and trace element cycling by stratifying sampling to encompass the mosaic patterns of existing spatial variability within the ecosystem. The examples show how individual plant species are able to create ecologically distinct spatial environments because of their distribution patterns within the landscape, how nutrient transfers in roots respond to the chemical variations in the soil, and how roots and mycorrhizal fungi are able to maintain themselves in the mosaic of coarse woody debris remaining on a site after the elimination of aboveground tree biomass.