中国草原土壤呼吸作用研究进展

植物生态学报 2010, 34 (6): 713–726 doi: 10.3773/j.issn.1005-264x.2010.06.011

Chinese Journal of Plant Ecology

中国草原土壤呼吸作用研究进展

鲍 芳1,2 周广胜3,1*

1

23中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093; 中国科学院研究生院, 北京 100049; 中国气象科学研究院, 北京 100081

摘 要 中国草原面积约占国土面积的40%, 且大都位于生态脆弱区, 对气候和环境变化十分敏感, 在未来大气CO2调控中有着重要的作用。为增进对中国草原土壤呼吸作用的理解, 该文综述了近10年来中国草原土壤呼吸作用的最新研究进展, 指出中国草原土壤呼吸作用的研究主要集中在东北平原、内蒙古高原和青藏高原。草原土壤呼吸作用日动态的主导控制因子是温度, 季节动态的主导控制因子可以是温度、水分或二者的交互作用, 取决于研究地点的限制性环境因子, 而年际动态的主导控制因子为水分。草原土壤呼吸作用还存在着巨大的空间变异, 年降水和土壤全氮含量是不同类型草原土壤呼吸作用空间异质性的主导控制因子。土壤呼吸作用对全球变化的响应比较复杂, 取决于各因子之间相互影响的贡献。现有的土壤呼吸作用模型大多只考虑了水热因子, 很少包含土壤因子和生物因子及其协同作用的影响。在此基础上, 指出未来中国草原土壤呼吸作用拟加强的研究重点: 1)温带荒漠草原土壤呼吸作用研究; 2)非生长季土壤呼吸作用研究; 3)多时空尺度草原土壤呼吸作用的比较研究; 4)草原土壤呼吸作用过程模拟研究; 5)草原土壤呼吸作用的遥感监测评估研究。 关键词 控制因子, 中国草原, 模拟模型, 土壤呼吸, 时空动态

Review of research advances in soil respiration of grassland in China

BAO Fang1,2 and ZHOU Guang-Sheng3,1*

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; 2Graduate University of Chinese Academy of Sciences, Beijing 100049, China; and 3Chinese Academy of Meteorological Sciences, Beijing 100081, China

1

Abstract

Grasslands in China cover vast, continuous areas and account for about 40% of Chinese land area. Most are lo-cated in the eco-geographical fragile region, are sensitive to climate change, and play important roles in regulating the carbon dioxide concentration in the atmosphere. Our objective was to review recent studies on soil respiration of grassland in China. Most studies were conducted in Northeast Plain, Inner Mongolia and Tibetan Plateau. Di-urnal dynamics of soil respiration are controlled by temperature, seasonal patterns are controlled by temperature and/or water depending on the limiting environmental factors, and inter-annual variability is mainly determined by water. In addition, there is great spatial heterogeneity driven by mean annual precipitation and soil total nitro-gen content. Responses of soil respiration to global changes were complicated and depended on the interaction of each factor. Most recent soil respiration models failed to incorporate the modulation of soil and biotic factors and their interaction. Key issues and suggested future research topics are 1) soil respiration in temperate desert grass-land, 2) soil respiration during non-growing season, 3) comparison study of grassland soil respiration on different spatial and temporal scales, 4) simulation study of grassland soil respiration and 5) remote sensing of grassland soil respiration.

Key words driving factors, grassland in China, simulation model, soil respiration, spatio-temporal variation

陆地生态系统2/3以上的碳储存在土壤中。土壤呼吸作用是陆地生态系统向大气输出碳的主要途径, 是陆地生态系统碳循环的重要组成部分。全球每年因土壤呼吸排放到大气中的碳是化石燃料燃烧排放量的10倍以上(Marland et al., 2006; IPCC, 2007)。土壤碳库及其碳排放量如此巨大, 使得土壤

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收稿日期Received: 2009-05-07 接受日期Accepted: 2010-03-24 * 通讯作者Author for correspondence (E-mail: gszhou@)

呼吸速率的微小变化都会导致土壤碳素的周转速率, 特别是大气CO2浓度发生重大改变, 从而可加剧或减缓全球气候变暖(Schlesinger & Andrews, 2000)。在全球范围内, 土壤呼吸作用在不同生态系统类型之间和之内都存在很大变异, 不同生物群区之间年平均土壤呼吸速率与其年平均气温、年平均

714 植物生态学报Chinese Journal of Plant Ecology 2010, 34 (6): 713–726

降雨量及年平均净初级生产力显著相关(Raich & Schlesinger, 1992; Raich & Pooter, 1995; Raich & Tufekcioglu, 2000)。草地占全球陆地总面积的1/3 (Scurlock & Hall, 1998), 在相同环境条件下, 草地土壤呼吸速率较森林高约20%, 在碳循环对全球变化的响应和反馈过程中发挥着重要作用(Raich & Tuf- ekcioglu, 2000)。因此, 弄清草地土壤呼吸作用的变化规律及其控制机制, 不仅是准确评估全球碳收支的关键, 而且是制定应对全球变化措施的关键。

中国草原面积约占国土面积的40% (陈佐忠和汪诗平, 2000), 且大多位于生态脆弱地带, 正在经受着越来越严重的人为与自然因素的干扰, 如土地利用变化、大气氮沉降增加、施肥、CO2浓度和温度升高等。对中国草原土壤呼吸作用研究现状的系统考察, 有助于认识和把握中国草原碳循环研究取得的成果, 找出存在的不足。为此, 本文试图从研究区域、时空变异及其控制因子对全球变化的响应及模拟模型等方面, 综述中国草原土壤呼吸作用的最新研究进展, 探讨未来中国草原土壤呼吸作用的研究重点, 为全球碳收支的准确评估和草原碳增汇减排对策的制定提供参考。

等, 2000)和克氏针茅草原(师广旭等, 2008; Liu et al., 2009; Xia et al., 2009a)、荒漠草原(珊丹等, 2009; Wang et al., 2009)、高寒草甸草原(Cao et al., 2004; Zhao et al. 2006; 陶贞等, 2007; Hu et al., 2008; Pei et al., 2009; Zhang et al., 2009)以及部分高山森林草原(常宗强等, 2005)、高山荒漠草原(常宗强等, 2007)和新疆亚高山草原(董自红等, 2007)等。

2 时空变异及其控制因子

2.1 时间变异及其控制因子 2.1.1 日动态及其控制因子

不同类型的草原土壤呼吸作用的日变化多呈单峰型曲线, 土壤呼吸速率早晚低、中午高。温度是草原土壤呼吸作用日变化的主要控制因子(崔骁勇等, 1999; 李明峰等, 2003; 孙伟, 2003; Cao et al., 2004; Jia & Zhou, 2009), 其他环境因子, 如土壤含水量、生物量和土壤特性等在一天当中的变化相对较小, 对土壤呼吸作用的影响不明显(Han et al., 2007)。如果发生降水事件, 土壤呼吸作用会迅速激增, 持续一段时间后逐渐下降 (Fierer & Schimel, 2003; Huxman et al., 2004; Sponseller, 2007; Chen et al., 2008, 2009)。

2.1.2 季节动态及其控制因子

不同类型的草原土壤呼吸作用的季节动态基本一致, 夏季高、秋冬低。峰值通常出现在植物的生长盛期。土壤呼吸作用日均CO2排放量在植物的不同生长期差异显著, 这种差异的80%是由地上活体生物量变化引起的(齐玉春等, 2005a; 师广旭等, 2008)。不同类型草原土壤呼吸作用的季节变化的主导因子可以是温度、水分或二者的交互作用, 取决于研究地点的限制性环境因子。如在水分不受限制的草甸草原, 温度是土壤呼吸作用季节变化的主要控制因子(王娓和郭继勋, 2006); 在水分较差的荒漠草原, 土壤呼吸作用的季节变化主要受水分控制, 其次才是温度(Wang et al., 2009); 在水热配置较好的典型羊草草原, 温度、水分和植物绝对生长速率共同驱动土壤呼吸作用的季节变化(Jia & Zhou, 2009); 在气温较低的高寒草甸, 温度是土壤呼吸作用的季节变化的主要控制因子(张金霞等, 2001; Cao et al., 2004; Saito et al., 2009)。 2.1.3 年际动态及其控制因子

草原土壤呼吸作用还表现出明显的年际波动,

1 研究区域

中国草原资源丰富, 集中分布于东北平原、内蒙古高原和青藏高原, 少数分布在暖温带和热带地区(Ni, 2002)。其中, 内蒙古草原是欧亚大陆草原的重要组成部分, 受水分和温度驱动, 自东向西依次分布着草甸草原、典型草原和荒漠草原, 是中国草地的主体, 也是中国畜牧业生产的主要基地(陈佐忠和汪诗平, 2000)。青藏高原被称为世界的“第三极”, 草原覆盖了整个青藏高原面积的1/3, 海拔多在3 000 m以上, 特殊的地形和大气环流模式, 使得该地区有着独特的生物地球化学过程, 对气候和环境变化反应非常敏感(Pei et al., 2009)。因此, 目前中国草原土壤呼吸作用的研究主要集中在这两个地区, 其他地区如新疆、黄土高原等草地也有零星研究。土壤呼吸作用研究的草原类型主要涉及到草甸草原(如贝加尔针茅草原(孙伟, 2003; 李明峰等, 2004a)、羊草草原(郭继勋和张宏一, 1991; 王娓等, 2002a, 2002b; 王娓和郭继勋, 2002, 2006))、典型草原(如羊草草原(Jia et al., 2006, 2007a, 2007b; Jia & Zhou, 2009)、大针茅草原(陈四清等, 1999; 崔骁勇

鲍芳等: 中国草原土壤呼吸作用研究进展 715

年际间的差异一般表现为年CO2排放量的差异, 年际间土壤呼吸作用的季节动态基本一致(李凌浩等, 2000; 王跃思等, 2003)。如降水是驱动一个混生针叶林土壤呼吸作用年际变异的主导因素(Concilio et al., 2009)。降水是中国温带草原土壤呼吸作用年际变化的第一控制因子(Liu et al., 2009)。在世界上其他存在季节性干旱的生物群区, 土壤呼吸作用的年际变化与年降水之间也存在显著的相关关系, 而全球尺度的土壤呼吸作用的年际变化与年平均气温的相关性更强, 气候变暖有可能导致全球土壤CO2排放量增加(Raich et al., 2002)。 2.2 空间变异及其控制因子

Raich和Schlesinger (1992)综述了全球范围内土壤呼吸作用的实测数据, 指出土壤呼吸的平均速率在不同植被类型之间和之内存在着很大的变异, 冻原和荒漠生态系统的呼吸速率最低, 热带雨林的土壤呼吸速率最大。即使在同一生态系统内, 不同空间尺度的土壤呼吸作用差异也很大(Martin & Bolstad, 2009)。中国不同类型草原的生长季平均土壤呼吸速率及其日、季节动态峰值出现的时间均不

表1 中国草原土壤呼吸作用空间异质性

Table 1 Spatial variation of soil respiration of grassland in China

草原类型 Grassland type

群落类型

Community type

测定方法 Measurement method 静态暗箱法 Static closed chamber method

相同(表1), 体现出巨大的空间异质性。加上不同研究者对土壤呼吸作用的观测时间和测定方法不同, 使得不同研究结果之间的可比性降低。有关土壤呼吸作用测定方法的分析可参考文献(Jensen et al., 1996; Norman et al., 1997; Kuzyakov, 2006; 苏永红等, 2008)。至于哪种方法可作为土壤呼吸作用测量的标准, 目前仍没有一致的看法, 但开放式动态气室法被认为是最可靠的一种方法(Luo et al., 2006)。

在全球尺度上, 不同类型草原的土壤呼吸作用与年平均气温呈显著的线性正相关关系, 与年降水量呈二次函数关系(Wang & Fang, 2009)。中国不同类型草原的土壤呼吸作用与年降水和年平均气温之间均呈显著的指数相关关系(图1A、1B), 降水梯度不够大可能是造成土壤呼吸作用与年降水量之间的相关关系与全球尺度不同的主要原因; 土壤呼吸作用与经度呈显著的指数关系, 与纬度呈显著的线性负相关关系(图1C、1D)。

除水热因子外, 植被组成(Raich & Tufekcioglu, 2000; Smith & Johnson, 2004; Johnson et al., 2008)、光合作用(Moyano et al., 2008)、土壤微生物生物量

草甸草原

Meadow steppe 典型草原 Typical steppe

贝加尔针茅 Stipa baicalensis 克氏针茅 Stipa krylovii 羊草

Leymus chinensis 大针茅

Stipa grandis

土壤呼吸速率 峰值出现时间 Soil respiration Maximum value occurrence time* rate 日动态 季节动态 (mg CO2·m–2·h–1) Daily variationSeasonal variation 316.8 10:00–13:00 6–8月

Jun.–Aug. 78.8 10:00–13:00 6–8月

Jun. –Aug. 218.9 13:00–15:00 6–8月

Jun. –Aug. 189.4 13:00–17:00 6–8月

Jun.–Aug. 72.8 14:00 8月

Aug.

13:00–15:00 8月

Aug.

315–3 685 14:00–16:00 168.7 15:00–18:00 7–8月

Jul.–Aug.

49.1–1105.6 14:00–16:00 7–8月

Jul.–Aug. 307.3–1476.2

7–8月 Jul.–Aug.

参考文献 Reference

紫花针茅 Stipa purpure 矮蒿草

Kobresia humilis

亚高山草原 针茅

Sub-alpine steppe Stipa capillata

LI-6400 荒漠草原 短花针茅

Desert steppe Stipa breviflora

高山森林草原 大针茅-黄花蒿 Mountain forest Stipa grandis- grassland Artemisia annua 高山荒漠草原 大针茅-黄花蒿 Mountain desert Stipa grandis- grassland Artemisia annua *不同研究地点间的时区差异忽略不计。

*The time zone differences at each site were ignored.

高寒草甸 Alpine steppe

Li et al., 2004a; Dong et al., 2005

Li et al., 2004a; Dong et al., 2005

Li et al., 2004a; Dong et al., 2005

Li et al., 2004a; Dong et al., 2005

Pei et al., 2003; Hu et al., 2008 Dong et al., 2007

Shan et al., 2009

Chang et al., 2005 Chang et al., 2007

doi: 10.3773/j.issn.1005-264x.2010.06.011

716 植物生态学报Chinese Journal of Plant Ecology 2010, 34 (6): 713–726

图1 中国草原土壤呼吸作用和年平均降水量(A)、年平均气温(B)、经度(C)及纬度(D)的相关关系。

Fig. 1 Relationships of soil respiration of grassland in China with mean annual precipitation (A), mean annual temperature (B), lon-gitude (C) and latitude (D).

(Ruess & Seagle, 1994)、地表特征(Maestre & Cortina, 2003)等因素也会导致土壤呼吸作用产生空间差异。其中, 年降水量通常是预测区域尺度上土壤呼吸作用空间变异性的重要因子(Luo & Zhou, 2006; Herbst et al., 2009)。如年降水可以解释北美大平原土壤呼吸作用区域变异的56% (McCulley et al., 2005), 水分是匈牙利黄土草原土壤呼吸作用空间异质性的主导控制因子(Foti et al., 2008)。对中国温带草原土壤呼吸作用及其影响因子(包括年平均气温、年降水量、土壤有机碳和全氮含量、碳氮比)进行逐步多元线性回归分析, 结果表明, 年降水量和土壤全氮含量是中国温带草原土壤呼吸作用空间变异的主导控制因子(方程(1)) , 二者共同解释了中国温带草原土壤呼吸作用空间变异的84%, 年降水量可以单独解释72%。

Rs = 1.85MAP + 1024.54N – 578.10 (R2 = 0.84, p < 0.0001) (1) 其中, Rs为土壤呼吸作用(g C·m–2·a–1), MAP为年降水量(mm), N为土壤全氮含量(%)。

不同空间尺度上, 土壤呼吸作用异质性的主导

影响因子不同: 在0–1 m尺度上, 根系和凋落物是决定土壤呼吸作用的空间变异的主要因素; 在1–10 m尺度上, 根系生物量、土壤碳/氮含量、根系含氮量是土壤呼吸作用空间异质性的主要影响因子; 在景观尺度上, 地形通过改变土壤含水量等理化特性而间接主导土壤呼吸作用的空间变异(Martin & Bolstad, 2009)。

3 对全球变化的响应

全球变化(主要包括CO2浓度升高、全球变暖、大气氮沉降和施肥、土地利用变化等)将对草原碳循环产生重要的影响, 而土壤呼吸作用在对全球变化的响应与反馈过程中起着非常重要的作用。模拟试验表明: CO2浓度升高通常会使土壤呼吸作用增加(Pendall et al., 2003)。CO2浓度升高通常使植物的光合作用增加, 导致植物生物量和凋落物量增加, 使得向地下输入的呼吸底物增加; 同时, CO2浓度升高有利于增加土壤湿度, 促进细菌运动和呼吸底物扩散, 进而导致土壤呼吸作用增加(Pendall et al., 2003; Nelson et al., 2004; Luo & Zhou, 2006; Luo et

鲍芳等: 中国草原土壤呼吸作用研究进展 717

al., 2006), 缓解干旱胁迫的不利影响(高素华等, 2003)。

由于土壤呼吸作用对温度的敏感性, 通常认为增温可以导致土壤呼吸增加(Schimel et al., 1994; Cox et al., 2000; Scheffer et al., 2006; Schindlbacher et al., 2009)。研究发现, 增温对土壤呼吸作用的影响因地点而异。如温度升高对美国高草草原土壤呼吸作用的刺激效应会因土壤呼吸作用的“适应性”逐渐减弱(Luo et al., 2001; Zhou et al., 2007a); 但也有研究指出, 德国南部一个农场的土壤呼吸作用经历了10年的持续增温后并没有观察到“适应现象” (Reth et al., 2009)。增温对土壤呼吸作用的影响还会随着草原类型、水分条件及观测时间的不同而改变(Wan et al., 2005, 2007; Lellei-Kovács et al., 2008; 珊丹等, 2009; Xia et al., 2009a)。水分对增温效果具有明显的影响, 在水分不受限制时, 增温使美洲中北部大草原土壤呼吸速率显著增加; 而当水分受限时, 增温对土壤呼吸作用的刺激效应会被水分缺乏导致的负效应抵消(Bontti et al., 2009)。在中国, 模拟增温使温带克氏针茅草原的土壤呼吸作用降低(Liu et al., 2009), 且白天和夜间增温对土壤呼吸作用的影响机制不同, 二者之间不具有加和性(Xia et al., 2009a)。白天增温对土壤呼吸作用产生的正效应会被土壤水分降低和生态系统碳同化速率降低产生的负效应抵消(Liu et al., 2009; Xia et al., 2009a), 珊丹等(2009)也观察到增温导致短花针茅草原土壤呼吸作用降低的现象; 夜间增温则可以促进土壤呼吸作用, 因为夜间呼吸作用使白天积累的同化产物被消耗, 进而刺激植物第二天产生更多的同化产物, 这种“光合过补偿”作用可以使中国温带草原积累更多的碳, 并使其由碳源向碳汇转变(Wan et al., 2009)。也有研究发现, 夜间增温由于阻碍了潘诺尼亚平原夜间露水的形成, 使其土壤含水量降低, 而导致土壤呼吸作用下降(Lellei-Kovács et al., 2008)。

降水可以使克氏针茅草原土壤呼吸作用和微生物呼吸作用显著提高(Liu et al., 2009), 干旱则导致其土壤呼吸作用下降(Wan et al., 2007; Shen et al., 2009)。降水量不同, 对土壤呼吸作用的影响也不同。如低于5 mm的降水只能激发土壤异养呼吸作用, 且持续时间较短; 高于5 mm的降水才能激发自养呼吸作用, 但持续时间短, 高于10 mm的降水才能使土壤呼吸作用对降水的响应时间延长; 其中异

养呼吸在响应过程的早期贡献较大, 自养呼吸在后期起主导作用, 并决定土壤呼吸作用对降水的响应时间(Chen et al., 2008, 2009)。因此, 将土壤呼吸区分为自养呼吸和异养呼吸对了解生态系统碳交换对气候和植被变化的响应非常重要(Edward & Schuur, 2006; Zhou et al., 2007b)。

全球变化多因子之间的交互作用非常复杂, CO2浓度升高、增温、增水三者对土壤呼吸作用的影响不存在加和效应, 但两两因素之间存在促进或拮抗效应(Luo et al., 2008)。如增温可以增强CO2浓度升高、增水对土壤呼吸作用的影响; 而CO2浓度升高和增水对土壤呼吸作用的影响之间则表现为拮抗效应(Shen et al., 2009)。增温主要通过刺激微生物的呼吸作用增加总的土壤呼吸作用, 而降水通过增强自养呼吸作用导致总的土壤呼吸作用增加(Shen et al., 2009)。

中国温带草原土壤呼吸作用对温度升高和添加氮肥的响应在不同降水年景之间没有显著差异, 这意味着增温、添加氮肥对中国温带草原碳通量的影响不依赖于水分变化(Xia et al., 2009b)。但水分和土壤氮含量是美国科罗拉多州东部半干旱草原对气候变化响应的主要驱动因子(Parton et al., 2007)。放牧和刈割通常会使草原土壤呼吸作用降低(崔骁勇等, 2000; 李凌浩等, 2000; Johnson & Matchett, 2001; 张金霞等, 2001; Wan & Luo, 2003; Cao et al., 2004; 贾丙瑞等, 2004; 齐玉春等, 2005b; Jia et al., 2007a; 陈海军等, 2008; Wang et al., 2009)。放牧使草原地下生物量明显下降(Johnson & Matchett, 2001; Jia et al., 2007a)、微生物和根的呼吸底物供应降低(Cao et al., 2004)、土壤呼吸作用对土壤温度和水分的敏感性改变(Cao et al., 2004; Jia et al., 2007a); 长期的刈割移走了部分本该返还到土壤中的植物生物量, 使土壤库中的碳、氮含量降低(Luo & Zhou, 2006); 草原开垦也会影响土壤呼吸作用, 如草甸草原开垦为农田后, 土壤呼吸作用上升了81% (李明峰等, 2004b)。

4 模拟模型

目前, 土壤呼吸作用模拟模型主要包括日尺度和季节尺度的土壤呼吸作用模型, 如线性模型、幂函数模型、对数模型、指数模型、二次函数模型等 (附表1), 尤其是机理性较强的Arrhenius模型越来越

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受到重视(Lloyd & Tailor, 1994; Hibbard et al., 2005)。这些模型大多只考虑了水热因子, 很少包含土壤因子与生物因子及其协同作用的影响(Zhou et al., 2008a)。越来越多的研究表明, 除气象因子外, 植物光合作用、净第一性生产力(NPP)、根系生物量、土壤呼吸作用底物数量与质量等均显著地影响土壤呼吸作用(Craine et al., 1999; Wan & Luo, 2003; Wang et al., 2003; Davidson et al., 2006; Daidson & Janssens, 2006; Flanagan, 2009; Martin & Bolstad, 2009; Savage et al., 2009)。土壤呼吸作用模拟不能只考虑水热因子, 不同时空尺度的土壤呼吸作用底物供应量等生物因子也应该纳入土壤呼吸作用模拟模型(Raich & Tufekcioglu, 2000; Wan & Luo, 2003; Savage et al., 2009)。Jia和Zhou (2009)将绝对生长速率耦合到土壤呼吸作用模型中, 建立了同时包括水热因子和生物因子在内的中国温带草原土壤呼吸作用模拟模型。由于目前发表的各种模型所采用的温度指标(如气温、5 cm和10 cm土壤温度等)、土壤水分取样深度(如0–10和10–20 cm等)以及土壤呼吸作用测定方法不统一, 使得模型之间缺乏可比性, 且这些模型考虑的水热因子和生物因子存在空间与时间的局限性, 只适应于特定的研究类型或地点, 难以从时间和空间尺度上推广应用到区域或全球尺度(Zhou et al., 2008a)。因此, 为了准确地评估中国草原的碳收支, 弄清楚土壤呼吸作用的时空动态及其控制因子, 必须采用统一的、高时间分辨率的土壤呼吸作用观测仪器, 开展土壤呼吸作用空间异质性及其影响因子的长期综合观测实验, 以获取长期的土壤呼吸作用、水热因子、生物因子及其土壤养分的综合观测资料, 发展和建立耦合多因子影响的土壤呼吸作用普适性评估模型(Zhou et al., 2008a; 韩广轩和周广胜, 2009)。

由于土壤呼吸作用的不同组分, 自养呼吸作用和土壤异养呼吸作用对环境变化及其控制因子的响应规律不一致(Boone et al., 1998), 如土壤异养呼吸作用的Q10 (表观温度敏感性, 温度每升高10 ℃, 土壤呼吸作用的变化率)为4.6, 自养呼吸作用的Q10为2.5 (Boone et al., 1998), 且只有土壤异养呼吸作用与土壤碳库中碳素的损失过程密切相关(Kuzyakov et al., 1999; Kuzyakov & Cheng, 2001)。因此, 将土壤呼吸作用各组分进行分离, 分别建立土壤异养呼吸作用和自养呼吸作用的模拟模型, 对

准确地评估生态系统碳收支及其对全球变化的响应将具有重要意义。

5 研究展望

近十年来, 关于草原土壤呼吸作用已经开展了大量的观测研究工作, 取得了较大进展, 但是关于中国草原土壤呼吸作用的控制机理及其过程认识尚不统一, 对于草原土壤呼吸作用的评估仍具有较大的不确定性。主要表现在以下5个方面:

1)温带荒漠草原土壤呼吸作用研究缺乏。内蒙古温带荒漠草原东起锡林郭勒盟苏尼特, 西至巴彦淖尔市乌拉特, 北面与蒙古国荒漠草原相接, 南至阴山北麓的山前地带, 总面积约11.2万km2。在气候上, 荒漠草原处于半干旱与干旱区的边缘地带, 受气候和人为干扰严重, 生态系统十分脆弱, 在中国草地碳循环研究中具有非常重要的地位(马治华等, 2007)。目前关于中国温带荒漠草原土壤呼吸的观测研究非常缺乏, 极大地制约了国家尺度草地碳收支的准确评估及碳减排增汇措施的实施。因此, 在现有资料的基础上, 大量补充温带荒漠草原土壤呼吸作用的观测数据, 有助于从整体上认识和把握中国草原土壤呼吸作用的规律, 合理地制定草原土壤呼吸作用的减排对策。

2)非生长季土壤呼吸作用研究不足。从草原碳循环来看, 土壤呼吸年排放量是准确地评估草原碳收支的最重要的资料(李凌浩和陈佐忠, 1998)。非生长季土壤呼吸主要来自微生物异养呼吸, 是地面和大气之间CO2交换过程不可忽略的一部分(方精云和王娓, 2007; 王娓等, 2007)。目前, 中国草原非生长季土壤呼吸作用及其关键生态学过程研究非常有限。因此, 未来应当对草原土壤呼吸作用及其主导因子进行长期的全年定位观测研究, 以准确地评估草原碳收支。

3)多时空尺度草原土壤呼吸作用的比较研究不足。不同时间尺度(日尺度、季节尺度、年际间)和空间尺度草原土壤呼吸作用的控制因子不同。目前, 大多数研究集中在土壤呼吸作用的短期流量、季节动态及其影响因素方面(李凌浩和陈佐忠, 1998), 对不同时空尺度草原土壤呼吸作用的比较研究较少。精确地估算草原碳收支必须了解不同时空尺度土壤呼吸作用的变化规律及其控制因子。因此, 开展多时空尺度草原土壤呼吸作用的比较研究是将来

鲍芳等: 中国草原土壤呼吸作用研究进展 719

土壤呼吸作用研究发展的方向。

4)草原土壤呼吸作用模拟模型研究。目前, 研究者通常利用土壤温度、土壤湿度或者两者的交互作用模拟土壤呼吸作用, 但是这些模型很难揭示土壤呼吸作用与其控制因子之间的时空变化规律(方精云和王娓, 2007)。为了提高模型模拟的准确性, 还需要考虑生物因子的影响(Raich & Tufekcioglu, 2000; Wan & Luo, 2003; Zhou et al., 2008a; 韩广轩和周广胜, 2009; Savage et al., 2009)。在全球变化的背景下, 综合考虑环境因子、生物因子及人为干扰对土壤呼吸作用的影响, 发展同时反映多时空尺度土壤呼吸作用异质性的模型是土壤呼吸作用模拟研究中的关键。

5)草原土壤呼吸作用的遥感监测评估研究。发展多时空尺度土壤呼吸作用模型必须要解决土壤呼吸作用测定结果的时空尺度转换问题, 虽然涡度相关技术可以直接测定不同时间尺度(从小时到数年)的生态系统碳通量的连续变化, 但其结果依然不能直接外推到区域或更大尺度上(严燕儿等, 2008)。遥感作为一种具有高时空分辨率的现代技术, 已经被广泛应用于生态学研究(Gilmanov et al., 2005), 为开展多尺度草原土壤呼吸作用长期定量观测提供了可能。建立土壤呼吸作用的遥感监测评估方法, 有助于实现中国草原土壤呼吸作用的快速和准确评估。

致谢 国家自然科学基金(90711001和30770413)和国家高科技研究发展计划项目(2006AA10Z225)共同资助。感谢中国科学院植物研究所隋兴华、胡天宇在作图过程中给予的大力帮助。 参考文献

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