植物生态学报, 2020, 44(4): 360-372 doi: 10.17521/cjpe.2019.0208

综述

碳同位素示踪技术及其在陆地生态系统碳循环研究中的应用与展望

葛体达,1,*, 王东东1,2, 祝贞科1, 魏亮1,2, 魏晓梦1,2, 吴金水1,2

1中国科学院亚热带农业生态研究所亚热带农业生态过程重点实验室, 长沙 410125

2中国科学院大学, 北京 100049

Tracing technology of carbon isotope and its applications to studies of carbon cycling in terrestrial ecosystem

GE Ti-Da,1,*, WANG Dong-Dong1,2, ZHU Zhen-Ke1, WEI Liang1,2, WEI Xiao-Meng1,2, WU Jin-Shui1,2

1CAS Key Laboratory of Subtropical Agriculture Ecology, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China

2University of Chinese Academy of Sciences, Beijing 100049, China

通讯作者: * E-mail:gtd@isa.ac.cn

编委: 温学发

责任编辑: 赵航

收稿日期: 2019-08-6   接受日期: 2019-10-14   网络出版日期: 2020-04-20

基金资助: 国家自然科学基金(41430860)
国家自然科学基金(41761134095)
国家自然科学基金(41811540031)
湖南省自然科学基金优秀青年项目(2019JJ30028)
湖南省自然科学基金创新研究群体项目(2019JJ10003)
湖南省国际科技创新合作基地项目(2018WK4012)

Corresponding authors: * E-mail:gtd@isa.ac.cn

Received: 2019-08-6   Accepted: 2019-10-14   Online: 2020-04-20

Fund supported: National Natural Science Foundation of China(41430860)
National Natural Science Foundation of China(41761134095)
National Natural Science Foundation of China(41811540031)
Natural Science Foundation of Hunan Province(2019JJ30028)
Foundation Science Natural of the Groups Research Innovative of Hunan Province(2019JJ10003)
Hunan Province Base for Scientific and Technological Innovation Cooperation(2018WK4012)

摘要

碳同位素示踪技术具有高度的专一性和灵敏度, 经过几十年的发展, 形成了一系列成熟的标记方法, 在陆地生态系统碳循环过程的研究中已得到广泛应用。目前, 自然丰度法、与13C贫化示踪技术结合的自由空气中气体浓度增加(FACE)实验、脉冲与连续标记法以及碳同位素高丰度底物富集标记法是研究陆地生态系统碳循环过程常用的碳同位素示踪方法; 通过将长期定位实验和室内模拟实验结合, 量化光合碳在植物-土壤系统的传输与分配特征, 明确植物光合碳对土壤有机质的来源、稳定化过程的影响及其微生物驱动机制; 阐明土壤碳动态变化(迁移与转化)和新碳与老碳对土壤碳库储量的相对贡献, 评估有机碳输入、转化与稳定的生物与非生物微观界面过程机制。然而, 生态系统碳循环受气候、植被、人为活动等多因素影响, 碳同位素技术需要结合质谱、光谱技术实现原位示踪, 结合分子生物学技术阐明其微生物驱动机制, 从而构建灵敏、准确、多尺度、多方位的同位素示踪技术体系。因此, 该文以稳定碳同位素为主, 综述了碳同位素示踪技术的原理、分析方法和在陆地生态系统碳循环过程中的应用进展, 归纳总结了碳同位素示踪技术结合原位检测技术和分子生物学技术的研究进展和应用前景, 并对碳同位素示踪技术存在的问题进行了分析和展望。

关键词： 陆地生态系统; 土壤有机质; 碳循环; 碳同位素示踪技术; 稳定碳同位素探针技术

Abstract

Recently developed in recent decades, the carbon isotope tracing technology is one of the most reliable methods, which has been widely used in the study of carbon (C) cycling in terrestrial ecosystems due to its high specificity and sensitivity. Here, the principle, analysis method and application process of C isotope tracing technology in C cycling in terrestrial ecosystem have been reviewed. Four different methods are currently being used in laboratory or field conditions, including natural abundance method, Free-Air Concentration Enrichment (FACE) technology coupling with 13C dilution method, pulse and continuous labeling with 13C enriched CO2, and labeling with 13C enriched substrates. Results of field experiments and lab incubation experiments employing carbon isotope tracing technology were combined in order to quantify the transformation and distribution of photosynthetic C in plant-soil system. Furthermore, these techniques also help to understand the contribution of plant photosynthetic C to soil organic matter, the stabilization of soil organic matter and its microbial mechanism, to illustrate the dynamic changes of soil organic carbon (SOC), evaluate the contribution of new and old organic C to soil C storage, and estimate the micromechanism of SOC input, conversion and the stabilization in terrestrial ecosystems. Carbon cycle is affected by climate, vegetation, human activities and other factors, and therefore it is imperative to further develop a sensitive, accurate, multiscale and multidirectional isotope tracing system by combining carbon isotopes with mass spectrometry, spectroscopy and molecular biological technology. We have summarized the coupled application of carbon isotope tracing technology and the insitu detection involving molecular and biological approaches, and discussed the existing issues of carbon isotope tracing technology.

Keywords： terrestrial ecosystem; soil organic matter; carbon cycle; carbon isotope tracing technology; stable carbon isotope probe technology

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引用本文

葛体达, 王东东, 祝贞科, 魏亮, 魏晓梦, 吴金水. 碳同位素示踪技术及其在陆地生态系统碳循环研究中的应用与展望. 植物生态学报, 2020, 44(4): 360-372. DOI: 10.17521/cjpe.2019.0208

GE Ti-Da, WANG Dong-Dong, ZHU Zhen-Ke, WEI Liang, WEI Xiao-Meng, WU Jin-Shui. Tracing technology of carbon isotope and its applications to studies of carbon cycling in terrestrial ecosystem. Chinese Journal of Plant Ecology, 2020, 44(4): 360-372. DOI: 10.17521/cjpe.2019.0208

自然界中已知的碳(C)同位素有7种(10C、11C、12C、13C、14C、15C和16C), 其中两种(12C和13C)为稳定碳同位素。不同同位素之间物理化学性质上的差异使反应前和反应后的物质在同位素组成上存在差异, 这种现象称为同位素分馏效应(陈世苹等, 2002; 林光辉, 2013)。基于此, 碳同位素示踪技术是研究陆地生态系统碳氮循环过程的有效手段, 也是揭示全球气候变化成因、控制温室效应的重要工具(葛源等, 2006; Blaser & Conrad, 2016)。碳同位素技术在研究土壤有机质的来源、动态转化与稳定性等方面具有高度的专一性和灵敏度, 且标记方法已比较成熟, 被广泛地应用到生态学研究中, 由原来在叶片、种群尺度的研究提高到了冠层、群落、生态系统乃至生物圈的水平, 从新的角度研究生物及其周围环境的关系(陈世苹等, 2002)。13C稳定同位素作为示踪物, 与放射性碳同位素相比具有安全、无污染和容易控制等优点, 被广泛应用于陆地生态系统碳周转的生物地球化学过程研究。

随着碳同位素示踪技术的广泛应用和迅速发展, 多种碳同位素示踪技术及其与其他光谱和微生物探针技术联用, 对于揭示从微观到宏观尺度的碳循环过程起着重要促进作用(图1)(Radajewski et al., 2000; Herrmann et al., 2007)。目前自然丰度法、与13C贫化示踪技术结合的自由空气中气体浓度增加(Free-Air Concentration Enrichment, FACE)实验、脉冲与连续标记法以及碳同位素高丰度底物富集标记法是研究陆地生态系统中碳循环过程常用的碳同位素示踪方法(Balesdent & Mariotti, 1996; Bernoux et al., 1998; Conley et al., 2001; Epron et al., 2012; Ge et al., 2017; 张蕊等, 2017)。同时, 随着氢、氮(N)、氧等元素同位素示踪技术的发展与应用, 那些单独用碳同位素无法解决的问题在与其他同位素示踪技术、光谱和分子生物学技术的联用下得以解决(陈世苹等, 2002)。例如, Kušlienë等(2014)利用13C/15N双标记法, 结合生物标志物分析, 研究了13C/15N标记的不同类型的根系分泌物对土壤碳氮动态的贡献, 这些技术的联用进一步促进了碳同位素示踪技术在生态学研究中的广泛应用。本文重点从稳定碳同位素示踪技术的原理、方法与应用进展等方面综述其在陆地生态系统碳循环过程中的研究进展。

图1

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图1   碳同位素示踪技术及其与其他技术的耦合应用。

Fig. 1   Coupled application of carbon isotope tracing technology and other technologies. DNA/RNA-SIP, DNA/RNA-Stable isotope probing; FISH, fluorescence in situ hybridization; NanoSIMS, nano-scale secondary ion mass spectrometry; PLFA, phospholipid fatty acid. SOM, soil organic matter.


1 碳同位素示踪技术的原理与分析方法

1.1 碳同位素示踪技术的基本原理——同位素分馏过程

碳同位素原子质量的微小差异, 使得含有不同同位素的物质具有不同的理化性质, 导致不同的动力学和热力学效应, 在物理、化学和生物等反应过程中出现同位素分馏现象。碳循环过程中, 同位素交换反应使无机盐(如碳酸盐)富集重同位素(如13C); 而碳的有机循环, 如光合作用的动力分馏效应导致生物成因碳(有机物)中富集轻同位素(如12C), 从而导致陆地生态系统中有机体和大气CO2之间的碳循环过程中, 出现明显的碳同位素分馏现象(Tieszen & Boutton, 1989)。植物在光合作用过程中固定CO2途径的差异, 导致不同光合型植物选择吸收13C的比例不同, 从而使C3和C4植株体内的稳定碳同位素比值(δ13C)存在差异, 因此δ13C可以作为表征植物株生理生态特性差异的指标。土壤有机碳(SOC)经过微生物分解矿化为CO2的过程中13C分馏作用较小, 仅为1‰-3‰, 而SOC在厌氧条件下产生甲烷的过程中13C的分馏作用相对较大, 为10‰-70‰ (Conrad et al., 2010; 林光辉, 2013; 袁红朝等, 2014)。但SOC主要来源于陆生高等植物, 与植物光合作用固定CO2时的分馏作用相比, SOC分解过程中的分馏作用几乎可以忽略不计, 因此SOC与该土壤上种植植物的δ13C基本一致(Fernandez et al., 2003; Crow et al., 2006; 张林等, 2010; Bai et al., 2012)。所以, 通过测定各个碳库中碳同位素比值的变化, 可以量化“大气-植物-土壤”生态系统中碳的来源、分配及其周转规律。

1.2 稳定碳同位素的测定方法

质谱法、核磁共振法和光谱法是测定稳定碳同位素常用的方法, 质谱法是稳定碳同位素分析中最通用、最精确的方法(Paul et al., 2007; Eiler et al., 2012; 袁红朝等, 2014)。质谱法的原理是先将制备的样品转化成含所测元素的纯气体(CO2、H2、N2、SO2等), 经离子源电离, 形成的离子束在电场、磁场的作用下, 导致具有不同质荷比的离子束分开从而检测同位素丰度。普通质谱仪在计算机辅助下直接给出同位素比值, 更先进的仪器如美国热电公司的Thermo Scientific MAT253, 不仅具有更高的灵敏度和更宽的测量范围, 而且可以与同位素数据系统整合, 自动化分析样品的同位素比值。核磁共振法是在磁场恒定条件下, 基于共振时的射频电场频率来测定不同基团上的同位素丰度, 与碳同位素示踪技术相结合可以检测底物(如葡萄糖)在土壤碳循环过程中的化学组分动态(Webster et al., 1997)。光谱法是根据组成分子的不同同位素质量数不同从而使红外光谱谱线发生位移的现象来测定碳同位素丰度。近年来, 随着元素生物地球化学循环研究的进一步发展, 以及元素分析仪-同位素质谱(EA-IRMS)和热分析-傅里叶变换红外光谱-气相色谱-质谱(STA-FTIR-GC-MS)等联用技术的兴起, 极大促进了碳同位素分析测试技术的发展(曹亚澄等, 2008; 刘春波等, 2014)。

1.3 稳定碳同位素示踪技术与其他技术的耦合

1.3.1 稳定碳同位素示踪技术与原位检测技术的耦合

拉曼光谱成像能高分辨率捕捉不同化学成分在样品中的空间分布特征, 具有样本需求量小、无需前处理、非侵入性、无损伤、对样品物理状态要求低等诸多优势, 自开发以来得到广泛应用。13C稳定同位素探针-拉曼显微光谱技术(SIRM)利用重同位素的光谱红移, 可原位连续检测含碳化合物在生态系统和生物体内的合成、分解、运输、分配等过程, 不仅能够在单细胞水平上提供微生物的生理生态特征信息, 还能够检测到自然环境丰度较低但能发挥重要作用的微生物类群, 对深入理解生态系统碳循环过程有重要意义(图1)(Li et al., 2012; Wiesheu et al., 2018)。近年发展起来的表面增强拉曼光谱成像技术通过强化拉曼散射信号, 有效提高了拉曼光谱的灵敏性(Zong et al., 2018)。

纳米二次离子质谱(NanoSIMS)能够完成对元素空间分布的超高分辨率显微镜成像, 与同位素示踪技术相结合, 在生态系统微观机制研究领域中显示出了巨大的潜力(Herrmann et al., 2007; Musat et al., 2016)。该技术具有较高的灵敏度和准确度, 可应用于碳、氮、硫等元素生物地球化学循环的微生物驱动机制和土壤-微生物-植物相关作用关系的研究, 对于认识多元素耦合的生物地球化学循环规律具有重要意义(图1)(Lechene et al., 2007; Berry et al., 2013; Rumpel et al., 2015)。目前, 稳定碳同位素探针-纳米二次离子质谱技术(NanoSIP)已在植物/动物-微生物共生关系(Lechene et al., 2007; Vidal et al., 2016), 微生物群落中碳的食物链传递(Musat et al., 2016), 厌氧甲烷氧化的碳氮耦合机制(Dekas et al., 2009)等研究中取得重要突破。在土壤有机碳周转的研究中, NanoSIP直接呈现了外源碳与土壤矿物的结合形态与动态过程, 有力推动了SOC的稳定机制的研究(Rumpel et al., 2015)。

1.3.2 稳定碳同位素示踪技术与分子生物学技术的耦合

将稳定碳同位素示踪技术与分子生物学方法相结合, 在表征以微生物为主要研究对象的土壤生物群落功能过程研究中发挥了重要作用(Lu & Conrad, 2005; Ruyters et al., 2013; Fan et al., 2014)。13C同位素探针-磷脂脂肪酸(PLFA-SIP)是在土壤碳循环微生物驱动机制的研究中广泛应用的技术。该方法灵敏度高, 常用于指示活体微生物生物量和群落结构, 能够定量检测微生物对标记底物的同化利用。近年来, 以氨基糖为主要成分的微生物残留物的相对稳定性及其对SOC的贡献得到广泛关注(Liang et al., 2017), 将PLFA-SIP与氨基糖生物标志物相结合, 建立外源碳-微生物-土壤碳库的碳周转连续体模型, 有助于揭示SOC的累积机制。PLFA-SIP对物种组成的分辨率较低, 难以判断功能微生物, 且无法检测古菌。以DNA/RNA测序为基础的DNA/RNA-SIP技术可以很好地解决上述问题, 只要选择合适的底物和目的基因, DNA/RNA-SIP技术能准确解析关键微生物在土壤中的群落结构和功能变化, 特异性地将微生物群体与生态过程、生理特性相关联, 揭示微生物驱动下的土壤元素循环过程及其调控机制。近年来, Hungate等(2015)将13C与18O标记相结合, 建立了定量DNA-SIP技术(DNA-qSIP), 揭示了参与土壤中新输入的有机碳(新碳)和土壤原有有机碳(老 碳)周转过程的微生物群落差异(Morrissey et al., 2017)。

2 稳定碳同位素示踪技术在“大气-植物-土壤”系统碳循环过程中的应用

2.1 稳定碳同位素自然丰度示踪技术原理、技术与应用进展

不同光合型植物光合作用过程中固定CO2途径的差异, 导致其对13C选择吸收的比例不同, 从而使不同植物的δ13C存在差异。大气CO2经植物气孔向叶片内扩散, 使进入叶肉细胞间的CO2比叶外大气CO2具有更低的δ13C值; 而CO2溶于水的过程中, 植物优先从叶胞间空气吸收12CO2使之溶于细胞质中, 使空气中CO2浓度越高分馏效应越大; 此外, 光合作用过程中, 光合羧化酶同化CO2, 使细胞质中的12CO2通过酶的作用优先结合到磷酸甘油酸中, 合成的光合产物贫化13C丰度。所以, 由于不同植被类型光合作用过程中的碳同位素分馏过程不同, 而具有不同的δ13C值, 通常C3植物(光合作用中同化CO2的最初产物是三碳化合物3-磷酸甘油酸的植物) δ13C的变化范围为-20‰-35‰, C4植物(CO2同化的最初产物是四碳化合物苹果酸或天门冬氨酸的植物) δ13C的变化范围为-17‰- -9‰ (Ehleringer & Pearcy, 1983)。碳同位素自然丰度比值法在植物光合作用途径、C3/C4植被变化、土壤碳循环、全球碳平衡等研究中得到日益广泛的应用。

13C自然丰度法是基于C3/C4植物具有不同的δ13C, 将C3 (C4)植物种植在具有C4 (C3)植物种植历史的土壤, 植物来源与原有SOM来源的δ13C存在差异, 可以以此来区分土壤源呼吸和根源呼吸的C (Kingston et al., 1994)。该方法成本低且容易操作, 但是需要注意的是, 选取的C4 (C3)土壤需要长期一直种植C4 (C3)植物, 且该方法灵敏度相对较低, 在植物生长季后期可靠性下降(Rochette et al., 1999)。

基于C3/C4植物δ13C的不同, 碳同位素组成可以用于区分植物的C3和C4光合途径以及不同植物功能群结构变化(Garten Jr. & Taylor Jr., 1992; Peñuelas et al., 1999)。通过测定不同碳组分(可溶性碳水化合物和结构性碳库)的δ13C值, 从而估计植物水分利用效率和光合作用在不同时间尺度上的变化(Ebdon et al., 1998; Arslan et al., 1999; 陈世苹等, 2002)。而在一个复杂的生态系统(如热带雨林)中, 水分利用效率的高低可能影响植物种间竞争的结果, 最终导致生态系统内部群落结构的差异, 因此δ13C为指示生

态系统的功能群多样性以及区分不同功能群植物提供了一条重要途径(Garten Jr & Taylor Jr, 1992; 陈世苹等, 2002)。任书杰和于贵瑞(2011)整合了中国区域的数据发现水分条件越好, C3植物叶片δ13C越低, 因此C3植物叶片的δ13C可以作为指示植物的水分利用效率的指标。C3/C4植物交互种植时会改变土壤有机质的δ13C, 因此, 以未交互种植作物土壤为对照, 通过土壤有机碳δ13C的变化可以研究土壤有机碳的周转速度, 以及不同植物来源碳占土壤各碳库和呼吸的比例以及激发效应的方向和强度(Kuzyakov, 2010; Werth & Kuzyakov, 2010; Blagodatskaya et al., 2011a), 也可以通过土壤碳自然丰度δ13C的变化来研究土壤碳库中活性组分碳(微生物生物量碳(MBC))的周转速率和稳定性(Blagodatskaya et al., 2011b)。研究结果发现, SOC和MBC的周转时间分别为16.8 a和29-30 d, 且随种植时间增加, 周转时间延长。新碳和老碳对土壤各碳库和呼吸的贡献不同, MBC中20%的碳来源于老碳(来自C3作物), CO2气体中60%的碳来源于老碳(来自C3作物), 由于土壤微生物偏好矿化老碳而同化新碳, SOC中新碳(来自C4作物)的贡献将逐年增加。此外, 长期种植C3作物(小麦(Triticum aestivum))的土壤在连续耕种C4作物(玉米(zea mays)) 13年后, 土壤有机碳库更新了22%, 且SOC的周转速率在不同粒径团聚体中各不相同, 在>50 µm与<2 µm粒径的团聚体中包含更多新碳, 而黏粒中有机碳的更新速度最慢(Balesdent & Mariotti, 1996; 袁红朝等, 2014)。13C自然丰度法为明确土壤新老碳库对SOC、MBC和CO2排放的贡献, 解析陆地生态系统δ13C的变化及其在全球碳平衡中的应用提供了重要依据(Cheng, 2009)。随着测试精度的提高, 采用碳同位素配对标记实验, 只要SOC和其他一种或是两种碳源的碳同位素比值间有较为明显的差别, 也可以解析多碳源相互作用及其对土壤呼吸的贡献, 这对于研究复杂生态系统中碳通量和净碳平衡具有重要的应用价值(Whitman & Lehmann, 2015)。

2.2 稳定碳同位素人工标记技术

2.2.1 13C贫化示踪技术结合的FACE标记技术原理、技术与应用进展

13C贫化示踪技术结合的FACE实验是研究CO2浓度增加的微域环境中SOC周转的常用方法。大气CO2的δ13C值约为-8‰, 在13C贫化示踪技术结合的FACE实验(13C-FACE)中供给13C贫化的CO2的δ13C值远低于大气, 其范围是-45‰- -18‰ (张蕊等, 2017)。FACE系统是一个开放的模拟未来CO2增加的微域环境, 系统内部的气候条件如水分、光照、温度等非常接近自然生态环境。因此, 在这样的系统内进行模拟CO2升高的实验更接近于真实状况(McLeod & Long, 1999)。目前全球已建立十余个涉及森林(Hagedorn et al., 2003), 农田(Leavitt et al., 1994)和草地(Six et al., 2001)等多个生态系统和多种植被类型的13C-FACE平台, 阐释了大气CO2浓度升高条件下, 不同生态系统植物生理与光合效率、养分和水分利用、土壤呼吸、土壤碳的输入量及周转率和微生物响应等过程机制(Leavitt et al., 1994; Canadell et al., 1995; Kiem et al., 2000)。

FACE实验系统一般由FACE圈(CO2递增圈)和Ambient圈(常规对照圈)构成(Leavitt et al., 1994)。采用人为添加的方式(如通过围绕在FACE圈的纯CO2气体管道向圈中心喷散CO2)提高FACE圈CO2气体浓度, 由远程控制器实时监测并控制FACE圈内CO2浓度, 使其始终保持在目标浓度。实验过程中FACE圈和Ambient圈的大小和降水量、温度等自然条件要保持一致, 且相邻的两圈之间设置缓冲区, 以避免CO2气体增加对其他圈的影响。

在草地生态系统中, FACE实验研究发现CO2浓度升高, 黑麦草(Lolium perenne)草地0-10 cm 土层土壤团聚体中的颗粒有机碳增加了40%, 表明大气CO2浓度升高可促进草地生态系统的碳周转(Six et al., 2001)。在森林生态系统中, Griepentrog等(2015)也发现, CO2浓度升高增加了C3植物向土壤碳的输入量, 提高了土壤碳的周转率。新碳在SOM中的积累程度受有机质密度的影响, 有机质密度增加, 新碳的积累程度减小, 大部分的新碳进入到颗粒态有机质中(Dorodnikov et al., 2011; Hofmockel et al., 2011)。同时, 13C-FACE结合PLFA技术, 进一步揭示了CO2浓度升高条件下革兰氏阴性菌和真菌可矿化更多的植物来源碳, 促进了植物新合成的光合碳的分解, 也加快了SOM的分解速率(Pendall et al., 2003; Hagedorn et al., 2013)。

2.2.2 13CO2脉冲和连续标记技术原理、技术与应用进展

13CO2同位素标记法, 是指以高丰度13CO2为示踪物, 在植物通过光合作用固定这些示踪物后, 通过在特定的时间内测定植物-土壤系统中各碳库示踪物量的变化, 可定量研究该时间段内13C在植物-土壤系统的转移和分布, 阐明植物某一生长期的碳分配特征, 定量植物碳向土壤的输入以及土壤碳周转, 从而明确植物光合碳对SOM的来源、稳定化过程的影响及其微生物驱动机制(Hanson et al., 2000; Kuzyakov & Cheng, 2001; Kuzyakov, 2006; Yuan et al., 2016; 张蕊等, 2017; Liu et al., 2019b)。脉冲标记法是一次或分几次加入13CO2, 标记数小时后在特定时间内测定13C在土壤-植物系统各碳库的分配比例(Kuzyakov & Gavrichkova, 2010)(表1)。脉冲标记法只能提供植物生长关键时期光合碳的分配特征, 但是在植物生长期内标记13CO2, 就可以估计植物来源碳输入土壤各组分的量(Lu et al., 2002a; Kimura et al., 2004; Xiao et al., 2019a, 2019b; Zang et al., 2019)。连续标记法是指在植物生长某个生长期(或全生长期), 在可控气室等封闭的空间内对植物进行不间断标记, 可以定量全部生长期投入碳的净分配(表1)。例如, 在水稻(Oryza satira)拔节期和灌浆期进行13CO2连续标记, 可以定量研究生长过程中水稻光合碳对SOC各组分的贡献(Kuzyakov, 2005; Conrad et al., 2012; Wang et al., 2016), 以及对土壤原有有机质的激发效应(Zhu et al., 2018a)。

表1   脉冲和连续标记在根际沉积碳(C)输入土壤应用中的比较

Table 1  Comparison of pulse and continuous labeling method in the study of rhizosphere deposition carbon (C) input to soil

脉冲标记 Pulse labeling连续标记 Continuous labeling
1. 标记持续时间 Labeling duration短(小时或天) Very short (hours or days)植物生长的全部时期 Entire duration of plant growth
2. CO2输入的同位素组成变化 Changes in the isotopic composition of the CO2 input时间短而丰度高 Short time and high abundance时间长(不一定需要高丰度); 长期持续富集 Long time (not necessarily high abundance), constant enrichment over a long period
3. 应用 Application简单 Simple复杂 Complicated
4. 成本 Cost便宜 Cheap昂贵 Expensive
5. 目的 Aim不同生长阶段植物-土壤系统的C流动力学 Dynamics of C flow in the plant, soil, and CO2 at various growth stages可以达到脉冲标记的所有目的
All the aims of pulse labeling can be achieved
地下C分配 Belowground C allocation植物C在CO2, 微生物生物量碳(MBC), 可溶性有机质(DOM)和土壤有机质(SOM)等中的分配 Partitioning of plant derived C in CO2, microbial biomass C (MBC), dissolved organic matter (DOM), soil organic matter (SOM), etc.
净C同化 Net C assimilation植物C分配的季节动态
Seasonal dynamics of plant C partitioning
植物根系和土壤呼吸 Root-soil respiration根际激发效应 Rhizosphere-priming effects
新同化C的命运(分配和运输) Fate of newly assimilated C
C转移速度 Speed of C transfer
6. C输入到土壤中
C input into the soil		在特定的植物生长阶段 At specific plant development stages在植物生长的整个时期 Over the whole growth period
7. 缺点 Disadvantage特定生长阶段不能代表整个生长时期, 同位素丰度在植物整株中分布不均匀 A specific growth stage cannot be transferred for the whole growth period, isotope abundance is unequal across plant parts需要特殊设备长期标记植物 Requires special equipment for exposing the plant over a long period
同位素丰度随时间变化 Isotope abundance in pools change over time为保证空气湿度, 需要良好的温度控制和空气循环系统 Temperature control and good air circulation are necessary to maintain air humidity

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13CO2脉冲标记在密闭的植物生长箱中进行, 当植物生长到一定时期, 在光照充足时通入13CO2或通过化学反应(如NaH13CO3和H2SO4)获得13CO2, 标记数个小时后移出, 在标记期间由CO2发生器和检测系统实时监测并控制CO2浓度, 生长箱内温湿度需与对照处理(未标记13CO2的植物)保持一致(Kuzyakov & Gavrichkova, 2010); 为避免13CO2污染, 对照处理要距离标记的植物至少5 m以上(Lu et al., 2003)。13CO2连续标记的方法与脉冲标记基本一致, 但是并不是短时间标记, 而是在植物生长某个生长期, 甚至整个生长期内, 对植物进行不间断标记。

采用13CO2脉冲标记技术, 对生长在不同SOM含量水稻土上的水稻进行标记, 发现高、低SOM水稻土壤对水稻光合碳的转化和周转具有显著差异, 揭示了水稻光合碳向地下传输过程的“马太效应”(Zhu et al., 2017b)。在水稻拔节期和灌浆期内, 通过水稻的根际沉积作用有4%-6%的光合碳进入土壤有机碳库, 土壤可溶性有机碳的2%-4%和土壤微生物生物量碳的9%-18%来源于这部分新碳(Lu et al., 2002a, 2002b; 祝贞科等, 2016)。此外, 利用13CO2脉冲标记结合生物标志物(磷脂脂肪酸)的稳定碳同位素探针技术(13C-PLFA-SIP), 发现了碳从水稻向土壤微生物快速转移的现象, 且随着水稻生长期的变化根际碳的微生物同化量显著增加, 真菌及革兰氏阴性菌是主要的同化微生物(Yuan et al., 2016; Liu et al., 2019a)。采用13CO2连续标记技术研究了水稻光合碳的传输和转化数量特征及其对氮肥施用的响应, 发现了高氮肥施用条件下水稻光合碳在土壤中的传输和积累比率非线性骤升的规律(Bradford et al., 2008; Ge et al., 2017)。结合13C-PLFA-SIP技术发现, 外源C (羧甲基纤维素)和氮肥的联合施用降低了革兰氏阳性菌(G+)和革兰氏阴性菌(G-)的比值, 降低了G+和真菌的丰度, 单施氮肥则刺激了G-和放线菌的增长, 说明可利用碳氮的不同施用策略可显著影响土壤微生物的群落结构(Zhao et al., 2019)。Atere等(2017)利用13CO2连续标记技术和土壤有机质的粒径组分分离技术联用, 发现水稻光合碳的固定效率在氮肥施用结合干湿交替条件下有显著提高。连续标记法有助于定量全部生长期或某一生长期全部投入C的净分配, 但是由于成本高以及技术困难, 运用受到限制。

2.2.3 13C富集底物标记技术研究土壤SOC周转过程的原理、技术与应用

SOC周转是一个连续动态变化的过程, 利用碳同位素技术能够示踪新碳在土壤中的转化过程和赋存状态, 揭示外源碳在土壤-微生物之间的周转过程和机理(金鑫鑫等, 2017)。利用13C富集标记小分子有机物或是植物秸秆, 结合生物标志物技术等, 可以表征不同来源的有机质在土壤中的相对丰度和降解状况, 以及SOC的动态变化和储量的转化和迁移, 也可以定量评价新老SOC对碳储量的相对贡献, 可有效评估陆地生态系统碳平衡过程及其微观过程机制(Kuzyakov & Domanski, 2000; Kemmitt et al., 2008; Ge et al., 2017; Zhu et al., 2017a)。外源C输入土壤后可以通过物理和生物化学途径形成新的SOM (Cotrufo et al., 2013, 2015), 从而增加SOC库; 另一方面, 外源C的输入也会促进土壤中原有SOM降解(Guenet et al., 2018; Zhu et al., 2018b; Perveen et al., 2019), 从而减少SOC库。因此, 这两个过程之间的平衡对SOC库变化的方向和程度起到至关重要的作用(Liang et al., 2018)。

13C富集底物标记技术一般用于室内培养实验, 13C富集底物可分为简单的小分子有机物(如葡萄糖、丙氨酸等)和复杂的大分子有机物(如纤维素、木质素、植物秸秆等)。在实验土壤中加入13C标记的底物, 以未标记的底物为对照, 结合PLFA、氨基糖等生物标志物技术, 测定13C在土壤各组分有机碳的量及其来源, 有助于阐明外源碳对土壤碳储量的贡献(Yuan et al., 2016; Liang et al., 2017)。

13C同位素示踪法技术可以很好地研究外源C对SOM周转的影响。例如, 将13C标记的有机物加入土壤进行田间或室内培养实验, 通过测定13CO2可以区分外源有机物和原有SOM来源的碳, 量化外源有机物添加SOM分解的影响(Bernoux et al., 1998; Blagodatskaya & Kuzyakov, 2008)。高丰度13C标记的有机物结合13C-PLFA-SIP和13C-DNA-SIP技术, 可以准确示踪微生物在土壤有机碳周转过程中的参与机制(Williams et al., 2006; Apostel et al., 2015; Wang et al., 2016)。而13C-微生物残留物对于量化和评估微生物对SOM积累的贡献起到了关键作用(Tian et al., 2014; Zhang et al., 2015; Liang et al., 2017)。

3 问题和展望

目前应用碳同位素示踪技术对C3/C4植被演替、土壤有机质源汇和各组分的周转及其微生物响应机制等方面进行了大量研究并日趋成熟(Six et al., 2001; Kuzyakov, 2006; Werth & Kuzyakov, 2010; Wang et al., 2016; Liu et al., 2019b), 而对于植物-土壤碳循环中植物、微生物以及土壤动物(如原生动物、线虫和节肢动物等)所扮演的角色及其循环机理研究较少。尽管碳同位素示踪在植物生理生态研究中已发挥重要作用, 但许多物理、化学和生物过程还不十分清楚; 放牧、施肥、耕作等人类活动等多种因素可直接影响或与气候协同作用影响植被变化以及导致土壤有机质变化的综合研究尚需深入; 还缺乏碳与其他养分元素的化学计量学方面的研究, 以及碳、氮、磷、硫、氧等多种同位素联合探测技术在生态学领域的综合开发及应用。鉴于此, 我们提出以下四点展望:

(1)稳定碳同位素耦合光谱成像技术的高精度原位检测方法

目前稳定碳同位素示踪耦合原位检测技术在生态学的应用中还面临诸多问题, 特别是在土壤系统中, 由于土壤的复杂性, 多种原位检测技术的性能均受到限制, 稳定碳同位素示踪与原位检测技术耦合性能的提升是其推广应用的前提。由于土壤颗粒的强荧光背景以及土壤孔隙气体的干扰, 拉曼光谱成像对土壤样品的检测难度很大, 目前主要通过类土壤介质和土壤有机体分离间接实现对土壤体系的研究, 干扰掩蔽和矫正技术的研发对稳定碳同位素耦合拉曼光谱成像技术在植物-土壤系统研究中的应用至关重要(何勇, 2016)。

(2)碳同位素耦合超高分辨率成像技术

尽管NanoSIP能够完成对土壤稳定碳同位素空间分布的超高分辨率成像, 但成本太高, 且测量范围在纳米尺度(Rumpel et al., 2015)。将14C同位素示踪与放射自显影相结合, 能够直观呈现植物、土壤碳的分布与传输过程, 为准确解析碳元素源-库关系和分配动态提供了有力工具(Pausch & Kuzyakov, 2011)。在利用放射自显影技术研究14C在土壤中的空间分布时, 土壤颗粒对β-粒子的激发、电离和轫致辐射等过程可强烈削弱14C信号, 通过基质转移(Holz et al., 2018)、衰减系数核算(Holz et al., 2019)等手段有望校正测定偏差, 提高该技术在土壤样品检测中的可信度。磷光体成像的应用提高了放射自显影技术的安全性和灵敏度, 为其在生态学的推广应用提供了良好的机遇。同位素示踪与超高分辨率成像技术相结合, 能在微观尺度上研究碳周转中植物-微生物-土壤的互相作用过程及碳与其他元素的耦合关系, 对全球碳循环的机理揭示有重要意义。

(3)稳定碳同位素探针技术耦合微生物生态学多组学分析技术

尽管PFLA-SIP、DNA/RNA-SIP技术有力推动了微生物群落动态与生态系统功能的关联研究, 但生物地球化学循环微生物驱动机制的深入揭示仍需要稳定碳同位素示踪技术与多组学技术的结合。稳定碳同位素探针-宏基因组/宏转录组技术能够揭示特定生态过程中活性微生物群落组成、功能多样性、生态网络关系等, 实现对碳氮等元素微生物周转过程的代谢通路预测和重构(Dumont et al., 2006; Neufeld et al., 2008)。宏基因组和宏转录组整合分析了代谢过程的可能性, 蛋白质组学、代谢组学技术则为特定底物的代谢通路的建立提供直接依据, 是生态系统中生物地球化学循环过程完整解析的必经之路。

(4)有机碳的来源与周转通量的多碳源解析技术

基于质量平衡原理解析土壤-植物-微生物连续体系中有机物的二元或三元组成与贡献, 量化生态系统中小分子根系分泌物-植物残体-生物质炭-土壤有机质-微生物活体与残留物等碳源的相对比例, 揭示生态系统碳源流通过程与机理(Phillips & Gregg, 2003; Whitman & Lehmann, 2015; Liang et al., 2017)。然而, 生态系统中有机物是多碳源组成的, 质量平衡并不能适应所有环境条件下有机物的源解析。因此, 需要基于同位素示踪基本原理, 构建合理的方程模型与原创性理论, 准确区分与量化复杂生态系统中有机碳的来源与周转通量, 揭示生态系统碳循环与调控机理。

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热分析-傅里叶变换红外光谱-气相色谱-质谱联用测定葡萄糖的热分解产物

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[本文引用: 1]

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Microorganisms living in anoxic rice soils contribute 10 to 25% of global methane emissions. The most important carbon source for CH4 production is plant-derived carbon that enters soil as root exudates and debris. Pulse labeling of rice plants with 13CO2 resulted in incorporation of 13C into the ribosomal RNA of Rice Cluster I Archaea in the soil, indicating that this archaeal group plays a key role in CH4 production from plant-derived carbon. This group of microorganisms has not yet been isolated but appears to be of global environmental importance.

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[本文引用: 2]

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Normalization of measured stable isotopic compositions to isotope reference scales—A review

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In stable isotope ratio mass spectrometry (IRMS), the stable isotopic composition of samples is measured relative to the isotopic composition of a working gas. This measured isotopic composition must be converted and reported on the respective international stable isotope reference scale for the accurate interlaboratory comparison of results. This data conversion procedure, commonly called normalization, is the first set of calculations done by the users. In this paper, we present a discussion and mathematical formulation of several existing routinely used normalization procedures. These conversion procedures include: single-point anchoring (versus working gas and certified reference standard), modified single-point normalization, linear shift between the measured and the true isotopic composition of two certified reference standards, two-point and multi-point linear normalization methods. Mathematically, the modified single-point, two-point, and multi-point normalization methods are essentially the same. By utilizing laboratory analytical data, the accuracy of the various normalization methods (given by the difference between the true and the normalized isotopic composition) has been compared. Our computations suggest that single-point anchoring produces normalization errors that exceed the maximum total uncertainties (e.g. 0.1 per thousand for delta(13)C) often reported in the literature, and, therefore, that it must not be used for routinely anchoring stable isotope measurement results to the appropriate international scales. However, any normalization method using two or more certified reference standards produces a smaller normalization error provided that the isotopic composition of the standards brackets the isotopic composition of unknown samples.

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Stable isotopes are increasingly being used as tracers in environmental studies. One application is to use isotopic ratios to quantitatively determine the proportional contribution of several sources to a mixture, such as the proportion of various pollution sources in a waste stream. In general, the proportional contributions of n+1 different sources can be uniquely determined by the use of n different isotope system tracers (e.g., delta13C, delta15N, delta18O) with linear mixing models based on mass balance equations. Often, however, the number of potential sources exceeds n+1, which prevents finding a unique solution of source proportions. What can be done in these situations? While no definitive solution exists, we propose a method that is informative in determining bounds for the contributions of each source. In this method, all possible combinations of each source contribution (0-100%) are examined in small increments (e.g., 1%). Combinations that sum to the observed mixture isotopic signatures within a small tolerance (e.g., +/-0.1 per thousand ) are considered to be feasible solutions, from which the frequency and range of potential source contributions can be determined. To avoid misrepresenting the results, users of this procedure should report the distribution of feasible solutions rather than focusing on a single value such as the mean. We applied this method to a variety of environmental studies in which stable isotope tracers were used to quantify the relative magnitude of multiple sources, including (1) plant water use, (2) geochemistry, (3) air pollution, and (4) dietary analysis. This method gives the range of isotopically determined source contributions; additional non-isotopic constraints specific to each study may be used to further restrict this range. The breadth of the isotopically determined ranges depends on the geometry of the mixing space and the similarity of source and mixture isotopic signatures. A sensitivity analysis indicated that the estimated ranges vary only modestly with different choices of source increment and mass balance tolerance parameter values. A computer program (IsoSource) to perform these calculations for user-specified data is available at http://www.epa.gov/wed/pages/models.htm.

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Stable isotopes have proved to be a transformative tool; their application to distinguish between two sources in a mixture has been a cornerstone of biogeochemical research. However, quantitatively partitioning systems using two stable isotopes (for example, (13)C and (12)C) has been largely limited to only two sources, and systems of interest often have more than two components, with interactive effects. Here we introduce a dual-isotope approach to allow conclusive partitioning between three sources, using only two stable isotopes. We demonstrate this approach by partitioning soil CO2 emissions derived from microbial mineralization of soil organic carbon (SOC), added pyrogenic organic matter (PyOM) and root respiration. We find that SOC mineralization in the presence of roots is 23% higher (P<0.05) when PyOM is also present. Being able to discern three sources with two isotopes will be of great value not only in biogeochemical research, but may also expand hitherto untapped methodologies in diverse fields.

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稳定同位素分析技术在农田生态系统土壤碳循环中的应用

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[本文引用: 3]

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Investigation on effects of elevated atmospheric CO2 concentration on plant-soil system carbon cycling: based on stable isotopic technique

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Elevated atmospheric CO2 affects plant photosynthesis process and biomass accumulation, furthermore alters the distribution of photosynthetic carbon (C) above- and below-ground. The formation and turnover of soil organic carbon (SOC) depends on the input of photosynthetic C, so the change of plant physiology and metabolism caused by increasing CO2 concentration will further affect the balance of SOC pool. Therefore, stable isotope (13)C technique is powerful for clarifying the influence of elevated atmospheric CO2 on C cycling in plant-soil system, including the distribution of photosynthetic C among plant organs, and the transformation and accumulation of photosynthetic C in soil. This review summarized research focused on the effects of elevated atmospheric CO2 on C cycling in terrestrial ecosystems based on (13)C natural abundance or (13)C tracing technique, including: 1) isotopic fractionation effect in plant photosynthesis; 2) the distribution of photosynthetic C in plant organs; 3) the transformation and stabilization of photosynthetic C in SOC driven by microbial process. Clarifying the above processes and controlling mechanisms is essential to predict long-term influence of elevated CO2 on C cycling and evaluate the source-sink function of SOC in terrestrial ecosystems.

[ 张蕊, 赵钰, 何红波, 张旭东 (2017).

基于稳定碳同位素技术研究大气CO2浓度升高对植物-土壤系统碳循环的影响

应用生态学报, 28, 2379-2388.]

DOI:10.13287/j.1001-9332.201707.034      URL     PMID:29741073      [本文引用: 3]

Elevated atmospheric CO2 affects plant photosynthesis process and biomass accumulation, furthermore alters the distribution of photosynthetic carbon (C) above- and below-ground. The formation and turnover of soil organic carbon (SOC) depends on the input of photosynthetic C, so the change of plant physiology and metabolism caused by increasing CO2 concentration will further affect the balance of SOC pool. Therefore, stable isotope (13)C technique is powerful for clarifying the influence of elevated atmospheric CO2 on C cycling in plant-soil system, including the distribution of photosynthetic C among plant organs, and the transformation and accumulation of photosynthetic C in soil. This review summarized research focused on the effects of elevated atmospheric CO2 on C cycling in terrestrial ecosystems based on (13)C natural abundance or (13)C tracing technique, including: 1) isotopic fractionation effect in plant photosynthesis; 2) the distribution of photosynthetic C in plant organs; 3) the transformation and stabilization of photosynthetic C in SOC driven by microbial process. Clarifying the above processes and controlling mechanisms is essential to predict long-term influence of elevated CO2 on C cycling and evaluate the source-sink function of SOC in terrestrial ecosystems.

Zhang W, Liang C, Kao-Kniffin J, He HB, Xie HT, Zhang H, Zhang XD (2015).

Differentiating the mineralization dynamics of the originally present and newly synthesized amino acids in soil amended with available carbon and nitrogen substrates

Soil Biology & Biochemistry, 85, 162-169.

[本文引用: 1]

Zhao ZW, Ge TD, Gunina A, Li YH, Zhu ZK, Peng PQ, Wu JS, Kuzyakov Y (2019).

Carbon and nitrogen availability in paddy soil affects rice photosynthate allocation, microbial community composition, and priming: combining continuous 13C labeling with PLFA analysis

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Fate of rice shoot and root residues, rhizodeposits, and microbial assimilated carbon in paddy soil-part 2: turnover and microbial utilization

Plant and Soil, 416, 243-257.

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Zhu ZK, Ge TD, Liu SL, Hu YJ, Ye RZ, Xiao ML, Tong CL, Kuzyakov Y, Wu JS (2018a).

Rice rhizodeposits affect organic matter priming in paddy soil: the role of N fertilization and plant growth for enzyme activities, CO2 and CH4 emissions

Soil Biology & Biochemistry, 116, 369-377.

[本文引用: 1]

Zhu ZK, Ge TD, Luo Y, Liu SL, Xu XL, Tong CL, Shibistova O, Guggenberger G, Wu JS (2018b).

Microbial stoichiometric flexibility regulates rice straw mineralizetion and its priming effect in paddy soil

Soil Biology & Biochemistry, 121, 67-76.

DOI:10.1016/j.soilbio.2018.03.003      URL     [本文引用: 1]

Zhu ZK, Ge TD, Xiao ML, Yuan HZ, Wang TT, Liu SL, Atere CT, Wu JS, Kuzyakov Y (2017b).

Belowground carbon allocation and dynamics under rice cultivation depends on soil organic matter content

Plant and Soil, 410, 247-258.

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Biogeochemical processes underlying the input and turnover of crop assimilative carbon in farmland ecosystems

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[本文引用: 1]

[ 祝贞科, 沈冰洁, 葛体达, 王久荣, 袁红朝, 吴金水 (2016).

农田作物同化碳输入与周转的生物地球化学过程

生态学报, 36, 5987-5997.]

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Surface-enhanced raman spectroscopy for bioanalysis: reliability and challenges

Chemical Reviews, 118, 4946-4980.

URL     PMID:29638112      [本文引用: 1]

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