卫星遥感监测产品在中国森林生态系统的验证和不确定性分析——基于海量无人机激光雷达数据

准确获取森林结构参数对森林生态系统研究及其保护有着重要意义。卫星遥感数据作为获取大尺度森林结构参数的重要数据源, 已被制作成各种植被监测产品并被应用于森林质量状况变化评估、森林生物量估算以及森林干扰和生物多样性监测等研究。然而, 这些卫星遥感植被监测产品针对中国复杂多样的森林区域缺乏有效验证, 在不同林况和地形条件下的不确定性也不明确。激光雷达具备高精度三维信息采集的优势, 在国内外已被广泛用于森林生态系统监测和卫星遥感产品验证。为此, 该研究利用在中国114个样地收集的153 km²的无人机激光雷达数据, 构建了我国森林结构参数验证数据集, 并以此为基础对3套全球遥感监测产品(全球叶面积指数(GLASS LAI)、全球冠层覆盖度(GLCF TCC)、全球冠层高度(GFCH))进行了像元尺度的验证, 并分析了其在不同坡度、覆盖度和林型条件下的不确定性。研究结果表明: 与无人机激光雷达获取的叶面积指数、覆盖度以及冠层高度相比, GLASS LAI、GLCF TCC、GFCH在中国森林区域均存在一定的不确定性, 且受林况和地形因素影响的程度不一致。对GLASS LAI和GLCF TCC影响的最大因素分别为林型和覆盖度; 而GFCH则更易受地形坡度和覆盖度的影响。     

基于地面遥感信息与气温的夏玉米土壤水分估算方法

土壤水分是土壤-植被-大气连续体的一个重要组分,是决定陆地生态系统水分状况的关键因子,也是作物的水分供应库.为了估算站点尺度不同深度的土壤水分,基于下垫面能量平衡方程和水分亏缺指数,提出了基于地面遥感信息(归一化植被指数和下垫面温度)和气温估算土壤相对湿度方法.利用2014年中国气象局固城生态与农业气象试验站夏玉米水分控制试验资料验证的结果表明: 该方法可以有效估算不同深度的土壤相对湿度,陆地生态系统的潜在干旱程度即实际蒸散与潜在蒸散之比与不同深度土壤湿度呈显著的线性关系.其中,0～10 cm土层的土壤水分估算精度最高,决定系数达0.90;0～20 cm到0～50 cm土层土壤水分估算的平均相对误差均在15%以内,相对均方根误差均在20%以内.研究结果可为作物的干旱监测与灌溉管理提供参考.     

Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat

The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.     

干湿交替格局下黄土高原小麦田土壤呼吸的温湿度模型

全球气候变化的直接后果是气温升高,同时还可能引起强降雨增多和干旱频发,形成干湿交替的格局.土壤呼吸在全球变化过程中发挥着重要作用.以黄土高原沟壑区小麦田土壤为研究对象,采用3个全自动多通量箱以及相应的气象监测系统,对土壤呼吸和环境因子全天候连续测定,利用已有的单因子模型、双因子模型对测定的土壤呼吸与气温和湿度的关系进行了拟合,通过优化,根据实际情况提出E-Q(exponential-quadratic)模型.结果表明:(1)干湿交替格局下,基于气温的单因子模型(指数模型,幂函数模型和线性模型)不适合模拟土壤呼吸;(2)基于土壤湿度的单因子模型中,二次曲线模型最适合模拟干湿交替格局下土壤呼吸的响应情况;(3)基于气温和土壤湿度的双因子模型中,E-Q模型SR=aebT(c+dW+fW2)g,既能反映土壤呼吸随气温的正向指数变化,又能表现土壤湿度对土壤呼吸的双向调节作用,解释了土壤呼吸73.05%的变化情况,比其他双因子模型和单因子模型更能有效描述干湿交替情况下土壤呼吸对气温和土壤湿度协同变化的响应特征.     

13CO2 pulse labelling of plants in tandem with stable isotope probing: methodological considerations for examining microbial function in the rhizosphere

Recently developed 13CO2 pulse labelling and stable isotope probing (SIP) methods offer the potential to track 13C-labelled plant photosynthate into phylogenetic groups of microbial taxa in the rhizosphere, permitting an examination of the link between soil microbial diversity and carbon flow in situ. We tested the feasibility of this approach to detect functional differences in microbial communities utilising recently fixed plant photosynthate in moisture perturbed grassland turfs. Specifically, we addressed two questions: (1) How does moisture perturbation (three treatments; continual wetting, drying, and drying followed by rewetting) affect the assimilation of 13C-labelled exudates carbon into the soil microbial community?; (2) Can 13C deposited in soil from pulse-labelled plants be used to identify microbes utilising plant exudates using SIP methodologies? Net CO2 fluxes showed that prior to 13CO2 pulse labelling, all treatments were photosynthetically active, but differences were observed in night time respiration, indicating moisture treatments had impacted on net CO2 efflux. Measurements of pulse-derived 13C incorporated into soil RNA over 2 months showed that there was only evidence of 13C enrichment in the continuously wetted treatments. However, isotopic values represented only a 0.1-0.2 13C at.% increase over natural abundance levels and were found to be insufficient for the application of RNA-SIP. These findings reveal that in this experimental system, the microbial uptake of labelled carbon from plant exudates is low, and further optimisation of methodologies may be required for application of SIP to natural plant-soil systems where 13C tracer dilution is a consideration.     

Recent progress in polymorphism-based population genetic inference

The recent availability of whole-genome sequencing data affords tremendous power for statistical inference. With this, there has been great interest in the development of polymorphism-based approaches for the estimation of population genetic parameters. These approaches seek to estimate, for example, recently fixed or sweeping beneficial mutations, the rate of recurrent positive selection, the distribution of selection coefficients, and the demographic history of the population. Yet despite estimating similar parameters using similar data sets, results between methodologies are far from consistent. We here summarize the current state of the field, compare existing approaches, and attempt to reconcile emerging discrepancies. We also discuss the biases in selection estimators introduced by ignoring the demographic history of the population, discuss the biases in demographic estimators introduced by assuming neutrality, and highlight the important challenge to the field of achieving a true joint estimation procedure to circumvent these confounding effects.     

Lessons from a decade of integrating cancer copy number alterations with gene expression profiles

Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.     

基于微波遥感技术探测森林地表土壤含水率

森林地表土壤含水率是森林生态系统中的重要参数,使用微波遥感技术快速准确地估算区域尺度上的森林地表土壤含水率,对于森林生态系统研究具有重要的现实意义.本文利用TDR-300土壤含水率速测仪测得黑龙江大兴安岭地区塔河林业局盘古林场内120块样地的森林地表土壤含水率作为因变量,利用C波段全极化SAR数据的极化分解参数作为自变量,构造多元线性回归统计模型和BP神经网络模型,定量估测森林地表土壤含水率,通过模型反演获得区域尺度上森林地表土壤含水率的空间分布.结果表明: 多元线性回归统计模型的精度为86.0%,均方差根误差(RMSE)为3.0%;BP神经网络模型的精度为89.4%,RMSE为2.7%.说明利用BP神经网络模型定量估测森林地表土壤含水率优于多元线性回归模型,将全极化SAR数据通过BP神经网络模型进行仿真,最终得到研究区域的森林地表土壤含水率空间分布图.     

土壤水分遥感反演研究进展

土壤水分精确反演对于理解和解决农业生产、生态规划以及水资源管理中的科学与实际问题至关重要。目前,大量的反演算法被广泛用于土壤水分估算,全球土壤水分遥感反演产品不断发布,反演算法与产品数据集的应用前景亟待系统梳理。基于不同谱段遥感探测技术中的土壤水分反演方法存在各自的特点、优势和局限性。除反演方法研究外,土壤水分遥感反演研究热点可被归纳为遥感土壤水分产品评估、在相关领域的应用、数据同化3个方面。大量研究表明土壤水分遥感反演产品在生态、水文、干旱等研究中表现出巨大的潜力,且在部分研究中已经得到应用。但目前土壤水分的遥感观测与应用需求仍存在一定的差距,因此最后对土壤水分遥感反演在探测的精度和准确度两个方面及其解决方案进行了总结与展望。     

Rank-based statistical methodologies for quantitative trait locus mapping

This article addresses the identification of genetic loci (QTL and elsewhere) that influence nonnormal quantitative traits with focus on experimental crosses. QTL mapping is typically based on the assumption that the traits follow normal distributions, which may not be true in practice. Model-free tests have been proposed. However, nonparametric estimation of genetic effects has not been studied. We propose an estimation procedure based on the linear rank test statistics. The properties of the new procedure are compared with those of traditional likelihood-based interval mapping and regression interval mapping via simulations and a real data example. The results indicate that the nonparametric method is a competitive alternative to the existing parametric methodologies.     

采用遥感手段估算海洋初级生产力研究进展

海洋初级生产力的精确估算对渔业资源评估与管理、海洋生态系统和全球变化等研究具有重要意义.传统的现场测量与估算方法必须依赖于随船采样数据.卫星遥感具有能够获取实时的、大尺度的、动态的海洋环境参数的优点,因此卫星遥感日益成为大尺度海洋初级生产力估算的重要手段.本文从海洋水色传感器的发展历程出发,着重归纳了以叶绿素、浮游植物碳和浮游植物吸收系数为参量的海洋初级生产力的遥感估算方法,并就这3类模型的适应性和复杂程度进行了讨论.在此基础上,进一步分析评价了全球海洋初级生产力遥感估算的研究现状.鉴于当前海洋初级生产力遥感估算研究中存在的问题,今后的研究需要在4个方面进一步加强:1)对全球海洋初级生产力估算进行分区域研究;2)加深对浮游植物吸收系数的研究;3)提高海洋遥感技术水平;4)加强实地测量技术的研究.     

The sensitivity of soil respiration to soil temperature,moisture, and carbon supply at the global scale

Soil respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, moisture, carbon supply, and other site characteristics are known to regulate soil respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature‐driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global‐scale models tend to smooth its spatial heterogeneity by aggregating factors that increase moisture variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr^?1, but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain.     

Statistical Application and Challenges in Global Gel-Free Proteomic Analysis by Mass Spectrometry

Global gel-free proteomic analysis by mass spectrometry has been widely used as an important tool for exploring complex biological systems at the whole genome level. Simultaneous analysis of a large number of protein species is a complicated and challenging task. The challenges exist throughout all stages of a global gel-free proteomic analysis: experimental design, peptide/protein identification, data preprocessing and normalization, and inferential analysis. In addition to various efforts to improve the analytical technologies, statistical methodologies have been applied in all stages of proteomic analyses to help extract relevant information efficiently from large proteomic datasets. In this review, we summarize current applications of statistics in several stages of global gel-free proteomic analysis by mass spectrometry. We discuss the challenges associated with the applications of various statistical tools. Whenever possible, we also propose potential solutions on how to improve the data collection and interpretation for mass-spectrometry-based global proteomic analysis using more sophisticated and/or novel statistical approaches.     

Modeling soil water regime and corn yields considering climatic uncertainty

Real time estimation of soil moisture and crop yield plays an important role for best irrigation management practices especially in arid and semiarid regions. A simulation model able of real time estimating and forecasting soil water storage and corn yield response to soil moisture was developed by combining two existing models. Soil water storage was estimated through the soil water balance equation considering the uncertainty of evapotranspiration and combing with Kalman filter technique. Crop dry matter and grain yield were simulated by using a functional relationship between yield and soil moisture. Some improvements have been made in the response function by considering different impacts of moisture stress on crop growth and yield for the different growing stages. Four years data sets collected in an experimental station in the North China Plain were used to calibrate and test the model. Results indicate that soil moisture storage in the soil profile estimated and predicted by the model agrees well with the measured data, and the relative error of yield prediction is around 10%, which means that the combined model and the methodology applied are capable of predicting crop yield and soil water storage dynamics.     

温室甜瓜营养生长期日蒸腾量估算模型

建立了基于温室环境参数、甜瓜生长发育参数和土壤水分参数的温室甜瓜日蒸腾量估算模型,以研究温室条件下甜瓜蒸腾量的估算方法.根据温室内特定环境对Penman-Monteith方程中空气动力项进行修正,推导出了适于计算温室条件下参考作物蒸腾量的温室环境因子子模型;以甜瓜叶面积指数为自变量构建了作物因子子模型,模型形式为线性函数;以土壤相对有效含水量为自变量构建了土壤水分因子子模型,模型形式为对数函数.采用分期播种法,根据周年不同播期实测数据对模型参数进行估计和分析.采用土壤相对含水量分别为80%、70%、60%的实测蒸腾数据,对模型在充分灌溉和节水灌溉条件下的预测精度进行了检验,模拟值的平均相对误差分别为11.5%、16.2%、16.9%.所建蒸腾模型是对Penman-Monteith公式在温室环境和节水灌溉条件下的有益探索,具有重要推广应用价值.     

西南地区土壤湿度与气候之间的互馈效应

土壤湿度控制着陆气之间的水热交换,与气候具有互馈效应。为了揭示中国西南地区土壤湿度与气候之间的关系,基于39年GLDAS数据,采用线性倾向估计、偏相关等方法探究了该区域土壤湿度与降水、气温的时空规律及相关性,分析了土壤湿度记忆性(Soil Moisture Memory,SMM)的空间分布及季节特征。主要结论如下:(1) 1979—2017年,西南地区仅表层(0—10cm)年平均土壤湿度呈显著减少趋势(P 0.001),气候倾向率为0.7 kg/m2/10 a,年降水量呈不显著增加趋势,而年平均气温呈显著的增加趋势(P 0.001)。(2)多年平均状态下,表层与更深层(10—40、40—100、100—200 cm)的土壤湿度呈相反的空间格局,中层(10—40、40—100 cm)土壤湿度最高。(3)基于像元的偏相关系数表明研究区土壤湿度总体上与降水关系更密切,二者呈正相关,但在0—10 cm,部分地区相关性不显著。(4)西南地区SMM总体上以60—90 d为主,且SMM均值以夏季最长,其次为冬季和春季,秋季最短;同时,对比各深度的SMM,发现0—10 cm的土壤湿度对整个西南地区长期的气候预测具有更好的代表性。研究结果可为研究西南地区陆-气相互作用以及气候预测与模式评估等提供参考依据。     

宇宙射线快中子法在华北平原典型农田土壤水分测量中的应用

宇宙射线快中子法是一种新兴的能够实现区域土壤水分观测的方法.以中国科学院山东禹城综合试验站的宇宙射线连续观测数据和无线网络土壤水分观测数据为基础,研究了该方法在华北平原典型农田生态系统的适用性,重点探讨了采用标定法获得的土壤完全干燥条件下中子数(N₀)的稳定性和降雨灌溉对土壤水分估算的影响.结果表明: N₀的变化受下垫面变化影响,变化周期与试验区冬小麦和夏玉米的种植和收割期较一致.宇宙射线法估算的土壤水分对降雨和灌溉有明显响应,但在灌溉或降雨发生时会高估土壤水分.在华北农田生态系统条件下,宇宙射线法能有效地估算区域土壤水分,估算结果与浅层土壤水分观测值(10 cm深度)最为接近.     

Indirect Estimates of Total Fertility Rate Using Child Woman/Ratio: A Comparison with the Bogue-Palmore Method

Indirect estimation methodologies of the total fertility rate (TFR) have a long history within demography and have provided important techniques applied demographers can use when data is sparse or lacking. However new methodologies for approximating the total fertility rate have not been proposed in nearly 30 years. This study presents a novel method for indirectly approximating the total fertility rate using an algebraic rearrangement of the general fertility rate (GFR) through the known relationship between GFR and TFR. It then compares the proposed method to the well-known Bogue-Palmore method. These methods are compared in 196 countries and include overall errors as well as characteristics of the countries that contribute to fertility behavior. Additionally, these methods were compared geographically to find any geographical patterns. We find this novel method is not only simpler than the Bogue-Palmore method, requiring fewer data inputs, but also has reduced algebraic and absolute errors when compared with the Bogue-Palmore method and specifically outperforms the Bogue-Palmore method in developing countries. We find that our novel method may be useful estimation procedure for demographers.     

Eco-hydrological controls on summertime convective rainfall triggers

Triggers of summertime convective rainfall depend on numerous interactions and feedbacks, often compounded by spatial variability in soil moisture and its impacts on vegetation function, vegetation composition, terrain, and all the complex turbulent entrainment processes near the capping inversion. To progress even within the most restricted and idealized framework, many of the governing processes must be simplified and parameterized. In this work, a zeroth‐order representation of the dynamical processes that control convective rainfall triggers – namely land surface fluxes of heat and moisture – is proposed and used to develop a semianalytical model to explore how differential sensitivities of various ecosystems to soil moisture states modify convective rainfall triggers. The model is then applied to 4 years (2001–2004) of half‐hourly precipitation, soil moisture, environmental, and eddy‐covariance surface heat flux data collected at a mixed hardwood forest (HW), a maturing planted loblolly pine forest (PP), and an abandoned old field (OF) experiencing the same climatic and edaphic conditions. We found that the sensitivity of PP to soil moisture deficit enhances the trigger of convective rainfall relative to HW and OF, with enhancements of about 25% and 30% for dry moisture states, and 5% and 15% for moist soil moisture states, respectively. We discuss the broader implications of these findings on potential modulations of convective rainfall triggers induced by projected large‐scale changes in timberland composition within the Southeastern United States.     

Sampling, defining, characterising and modeling the rhizosphere—the soil science tool box

We review methods and models that help to assess how root activity changes soil properties and affects the fluxes of matter in the soil. Subsections discuss (1) experimental systems including plant treatments in artificial media, studying the interaction of model root and microbial exudates with soil constituents, and microcosms to distinguish between soil compartments differing in root influence, (2) the sampling and characterization of rhizosphere soil and solution, focusing on the separation of soil at different distances from roots and the spatially resolved sampling of soil solution, (3) cutting-edge methodologies to study chemical effects in soil, including the estimation of bioavailable element or ion contents (biosensors, diffusive gradients in thin-films), studying the ultrastructure of soil components, localizing elements and determining their chemical form (microscopy, diffractometry, spectroscopy), tracing the compartmentalization of substances in soils (isotope probing, autoradiography), and imaging gradients in-situ with micro electrodes or gels or filter papers containing dye indicators, (4) spectroscopic and geophysical methods to study the plants influence on the distribution of water in soils, and (5) the modeling of rhizosphere processes. Macroscopic models with a rudimentary depiction of rhizosphere processes are used to predict water or nutrient requirements by crops and forests, to estimate biogeochemical element cycles, to calculate soil water transport on a profile scale, or to simulate the development of root systems. Microscopic or explanatory models are based on mechanistic or empirical relations that describe processes on a single root or root system scale and/or chemical reactions in soil solution. We conclude that in general we have the tools at hand to assess individual processes on the microscale under rather artificial conditions. Microscopic, spectroscopic and tracer methods to look at processes in small “aliquots” of naturally structured soil seem to step out of their infancy and have become promising tools to better understand the complex interactions between plant roots, soil and microorganisms. On the field scale, while there are promising first results on using non-invasive geophysical methods to assess the plant’s influence on soil moisture, there are no such tools in the pipeline to assess the spatial heterogeneity of chemical properties and processes in the field. Here, macroscopic models have to be used, or model results on the microscopic level have to be scaled up to the whole plant or plot scale. Upscaling is recognized as a major challenge.