生态学报  2014, Vol. 34 Issue (22): 6640-6654

文章信息

王淼, 曲来叶, 马克明, 李桂林, 杨小丹
WANG Miao, QU Laiye, MA Keming, LI Guilin, YANG Xiaodan
罕山土壤微生物群落组成对植被类型的响应
Response of soil microbial community composition to vegetation types
生态学报, 2014, 34(22): 6640-6654
Acta Ecologica Sinica, 2014, 34(22): 6640-6654
http://dx.doi.org/10.5846/stxb201302200278

文章历史

收稿日期：2013-2-20
网络出版日期：2014-3-17
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王淼, 曲来叶, 马克明, 李桂林, 杨小丹. 罕山土壤微生物群落组成对植被类型的响应[J]. 生态学报, 2014, 34(22): 6640-6654.
WANG Miao, QU Laiye, MA Keming, LI Guilin, YANG Xiaodan. Response of soil microbial community composition to vegetation types[J]. Acta Ecologica Sinica, 2014, 34(22): 6640-6654.
罕山土壤微生物群落组成对植被类型的响应
王淼1, 2, 曲来叶1 , 马克明1, 李桂林3, 杨小丹4    
1. 中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100085;
2. 中国科学院大学, 北京 100049;
3. 内蒙古赛罕乌拉国家自然保护区管理局, 赤峰 025150;
4. 内蒙古赤峰市巴林右旗环境保护局, 赤峰 025150
收稿日期:2013-2-20; 网络出版日期：2014-3-17;
基金项目：国家自然科学基金(31170581,30700639).
*通讯作者Corresponding author.E-mail: lyqu@rcees.ac.cn
摘要：选取分布在中国东北部地区的阔叶林-针叶林-亚高山草甸这一明显的植被垂直带谱来研究植被类型对土壤微生物群落组成的影响.选取5种植被类型-山杨(Populus davidiana)(1250-1300 m),山杨(P. davidiana)与白桦(Betula platyphylla)的混交林(1370-1550 m),白桦(B. platyphylla)(1550-1720 m),落叶松(Larix principis-rupprechtii)(1840-1890 m),亚高山草甸(1900-1951 m),采用磷脂脂肪酸(Phopholipid Fatty Acids, PLFAs)分析方法测定不同植被类型下的土壤微生物群落组成.分别采用主成分分析(Principal Components Analysis, PCA)以及冗余分析(Redundancy Analysis, RDA)来解释单种特征PLFAs的分异以及土壤理化指标与微生物PLFAs指标间的相关性.结果表明不同植被类型下土壤有机碳(SOC)对土壤微生物PLFAs总量,各类群(真菌(f)、细菌(b)、革兰氏阳性菌(G+)、革兰氏阴性菌(G-))生物量以及群落结构影响显著;土壤微生物PLFAs总量及各类群的生物量随土层加深总体上表现降低趋势,G+/G-和f/b分别随土层加深总体上表现升高趋势.不同植被类型下,阔叶混交林土壤PLFAs总量及各类群生物量总体上最高;针叶林比阔叶林下的f/b和G+/G-高;亚高山草甸下低的pH值对有机碳的可利用性有一定的抑制作用,导致f/b和G+/G-的值相对较高.总之,不同植被类型下SOC对土壤微生物群落组成的影响最为显著,而较低的pH对有机碳的可利用性有一定的抑制作用;真菌对植被类型的变化比细菌更敏感,而细菌更易受可利用性养分和pH变异的影响,这对预测不同林型下的土壤微生物群落组成有重要的启示作用.
关键词： 磷脂脂肪酸(PLFAs)    土壤微生物群落    植被    土壤有机碳(SOC)    
Response of soil microbial community composition to vegetation types
WANG Miao1, 2, QU Laiye1 , MA Keming1, LI Guilin3, YANG Xiaodan4    
1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental Science, Chinese Academy of Sciences, Beijing 100085, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. SaihanWuLa National Nature Reserve Administration, Chifeng 025150, China;
4. Barin Youqi Environmental Protection Agency, Chifeng 025150, China
Abstract:There is no unifying conclusion among the considerable studies of soil microbial community composition under different vegetation types. We selected a distinct vertical vegetation distribution belt consisting of broad-leaved forests, coniferous forests, and subalpine meadows to study the effect of vegetation types on soil microbial community composition. Soil samples were collected at three different depths (0-5cm, 5-10cm, 10-20cm) from sites of five vegetation types. These sites were distinguished by their dominating vegetation: poplar (Populus davidiana) (1250-1300m), poplar (P. davidiana) mixed with birth (Betula platyphylla) (1370-1550m), birth (B. platyphylla) (1550-1720m), larch (Larix principis-rupprechtii) (1840-1890m) and subalpine meadow (1890-1951m). Soil microbial community compositions under the various vegetation types were determined by phospholipid fatty acid (PLFA) analysis. Ordination of individual PLFA signatures and correlations among soil properties and soil microbial PLFA indicators were analyzed by principal components analysis (PCA) and redundancy analysis (RDA), respectively. The results indicated that total PLFA contents of soil microbial community, biomasses of four main microbial taxa (fungi (f), bacteria (b), gram-positive bacteria (G+), gram-negative bacteria (G-)), and microbial community structure were significantly affected (P <0.05) by soil organic carbon (SOC) under all vegetations; PLFA contents of total microbial community and main taxa generally decreased as soil depth increased, while G+/G-and f/b increased with soil depth. Among different vegetations, total PLFA contents and main taxa biomass under mixed broad-leaved forests were the highest; f/b and G+/G-under coniferous forests were higher than those under broad-leaved forests; the availability of SOC under subalpine meadows was constrained on some level by the low pH value, which led to a relatively high f/b and G+/G-. In conclusion, the effect of SOC on soil microbial community composition was the most significant of all soil parameters under all vegetation types, though the availability of SOC could be constrained by relatively low pH values on some level; fungi was more sensitive to the changes of vegetation types while bacteria was more sensitive to the variability of nutrient availability and pH. This conclusion could have a significant impact on forecasting soil microbial community composition under different vegetations.
Key words: PLFAs    soil microbial community    vegetation    soil organic carbon    

土壤微生物在养分循环中扮演着重要角色[1, 2, 3]。了解不同驱动因子对土壤微生物群落变化的相对影响力具有重要的生态学意义[4, 5]；为更好地解释植被演替过程[6, 7]、人为干扰(放牧、火烧)[8, 9]后及不同土地利用类型下[10]土壤微生物群落组成、植被组成及土壤性质之间的相互作用机制提供重要启示。土壤微生物群落的组成和功能随气候[11, 12]，土壤理化性质[13, 14]和植被组成[15, 16]而变化。

土壤是植被与土壤微生物相互作用的载体，因此，土壤理化性质会影响微生物群落组成[17, 18]。由于植被与土壤相互影响的复杂性，目前对于各种因素对微生物不同类群的影响机制和效应还没有一致性的结论。总的来说，在同一气候区内，影响土壤微生物组成的众多理化性质中，土壤有机质、土壤含水量、pH值、可利用性的C和N通常比较主要[15, 19, 20, 21]。植物凋落物分解是陆地生态系统中养分和能量流动中的重要环节[22]，通过向土壤中输入凋落物[23]，枯死根[24]以及根系分泌物[25]，为微生物生长提供养分，因此对土壤微生物的组成和活性有关键性影响。不同植物种的凋落物和根系分泌物中碳的质量有差异[26, 27]，可以显著影响土壤微生物组成[28]。Merila 等[6]通过研究阔叶林与针叶林下土壤微生物群落组成和功能的差异，发现碳源的可利用性与微生物群落密切相关，来源于凋落物和根系分泌物的有机质组分的差异对土壤微生物群落组成和植被演替过程影响很大。Brockett 等[12]通过研究区域性的气候梯度下七种林型的土壤微生物群落组成，表明土壤含水量对微生物的影响很大。土壤pH升高可以使土壤微生物群落由真菌主导型发展为细菌主导型[13, 14]。真菌更适宜在高C ∶ N土壤中生长，而细菌则相反[14, 29]，因此土壤C ∶ N可以很好地预测土壤微生物群落[30]；而土壤有机层中的可利用性C和N绝大部分来自植物凋落物分解过程释放的有机质[31]。

磷脂脂肪酸(PLFAs)分析方法最早由Bligh and Dyer[32]提出，经过不断完善[33, 34]，已经被广泛用于微生物群落组成的测定。土壤中，PLFAs的总量提供了微生物生物量的信息[33, 35]，而特征脂肪酸的组成则可反映微生物群落结构[34, 36]。

本文以罕山阴坡连续分布的阔叶林—针叶林—亚高山草甸这一明显的植被垂直带谱为对象，研究植被类型变化对土壤微生物群落组成的影响，其中山杨(Populus davidiana)、白桦(Betula platyphylla)和落叶松(Larix principis-rupprechtii)，均为中国东北部森林常见的优势树种。通过对阔叶林与针叶林、纯林与混交林、乔木与草本下主要土壤微生物类群-真菌(f)、细菌(b)、革兰氏阳性菌(G+)及革兰氏阴性菌(G-)的PLFAs在不同土层中分布与含量规律的研究，探讨土壤微生物群落结构对植被类型的响应以及影响土壤微生物群落结构的因素。

1 材料和方法 1.1 样地描述

研究区域位于内蒙古赛罕乌拉自然保护区(43°59′—44°27′N,118°18′—118°55′E)东南部的第2高峰——罕山(海拔1951 m)。该区年平均气温2 ℃，7月份最热，最高气温29 ℃，年平均降水量达400 mm，多集中在6—8月份，夏季降水历年平均在300 m左右，占全年降水量70%—80%。植被类型在阴坡呈现明显的垂直分布，沿海拔由低到高优势种依次为山杨(Populus davidiana)(P)(海拔1250—1300 m)，白桦(Betula platyphylla)和山杨混交林(BP)(1370—1550 m)，白桦(B. platyphylla)(B)(1550—1720 m)，落叶松(Larix principis-rupprechtii)(L)(1840—1890 m)，亚高山草甸(SM)(1900—1951 m)。在林下灌丛植被中，虎榛子(Ostryopsis davidiana)灌丛分布最为广泛，多生于白桦林下缘与采伐迹地；在虎榛子灌丛上缘，小面积的兴安杜鹃(Rhododendron dahuricum)灌丛多分布在森林破坏后的地块中。落叶阔叶林下的土壤为典型的棕壤，针阔混交林下为灰色森林土，随海拔升高，山顶亚高山草甸植被下分布着山地黑土。样地的土壤理化性质如表 1所示。

表1 5种植被类型下土壤理化性质 (平均值±标准差，n=3) Table 1 Soil chemical and physical properties under five vegetation types (mean±SD,n=3)
土壤性质 Soil Properties土层深度/ cmPBPBLSM
P:山杨林；BP:白桦与山杨混交林；B:白桦林；L:落叶松林；SM:亚高山草甸; TC:总碳；TN:总氮；AN:有效氮；SOC:土壤有机碳；C/N=SOC/TN；SWC:土壤质量含水率；同一行中不同小写字母代表不同植被间差异显著
TC/(g/kg)0—5117.12±12.56a106.98±13.94a80.23±9.82b68.64±9.89b81.89±12.12b
5—1094.85±6.37a61.25±12.51b56.66±14.04b55.09±6.16b77.09±7.84ab
10—2067.70±10.93a64.95±13.68ab48.43±6.63ab53.37±3.44b69.70±3.31a
TN/(g/kg)0—58.64±0.70a8.39±0.93a6.41±0.73b5.97±0.81b7.04±0.80bc
5—107.16±0.74a5.19±1.01b4.73±1.01b4.98±0.51b6.81±0.68a
10—205.33±0.57a5.50±1.23a4.25±0.50a4.88±0.34a6.08±0.30a
AN/(g/kg)0—50.38±0.55ab0.38±0.03ab0.33±0.03b0.40±0.02a0.47±0.06a
5—100.41±0.01a0.30±0.03b0.32±0.02b0.32±0.04b0.38±0.03a
10—200.33±0.04a0.29±0.06a0.26±0.02a0.30±0.04a0.37±0.01a
SOC/(g/kg)0—597.06±9.41a99.62±8.12a66.57±7.43b63.77±7.14b71.44±8.29b
5—1084.08±5.89a48.63±11.54b46.92±13.33b42.97±7.40b60.32±7.07b
10—2060.59±10.01a46.43±1.16b39.94±6.09b43.72±0.87b56.34±3.65ab
C/N0—511.22±0.28ab11.90±0.38a10.39±0.18b10.75±1.09b10.14±0.12c
5—1011.78±0.40a9.90±4.36a9.83±0.67a8.76±2.13a8.86±0.60a
10—2011.33±0.66a8.66±1.54b9.38±0.32b8.98±0.68b9.27±0.19b
SWC0—50.74±0.04a0.65±0.11a0.63±0.05a0.62±0.06a0.96±0.15a
5—100.48±0.02a0.44±0.02bd0.40±0.00d0.52±0.02c0.77±0.03a
10—200.40±0.01b0.39±0.01b0.40±0.01b0.45±0.01a0.61±0.02a
pH0—56.29±0.13a6.03±0.25ab5.94±0.26ab5.87±0.06b5.75±0.16b
5—106.05±0.09a5.83±0.18a5.93±0.19a5.84±0.01a5.72±0.09a
10—205.81±0.12a5.82±0.20a5.80±0.22a5.78±0.05a5.78±0.06a
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1.2 样品采集

2010年8月，在上述五种植被类型下，分别随机选取3块20 m×20 m的样地；用直径为5 cm的土钻在每块样地的优势物种下分别随机采集3份0—5 cm，5—10 cm，10—20 cm土样，将每个土层的3份土样混合后作为一个样；每份土样分成两部分，一部分过2 mm筛后用于微生物磷脂脂肪酸(PLFAs)测定的土样保存于-80 ℃；另一部分土样用于土壤理化性质测定，样品过2 mm筛后自然风干。

1.3 研究方法 1.3.1 土壤微生物群落PLFAs测定

土壤微生物群落PLFAs采用Bligh and Dyer[32]和Frostegård 等[33]介绍的方法提取，将样品进到GC-MS中测定。脂肪酸的命名规则如下：一般用总碳原子数：双键数ω烯键距甲基端的位置表示。后缀c，t分别表示双键两侧-H键的顺式与反式，前缀a，i分别表示支链的异型和同型，环丙烷脂肪酸用cy表示[36, 37]。特征磷脂脂肪酸的分类如表 2所示。饱和脂肪酸/单不饱和脂肪酸(SATFA/MUFA)通常作为细菌群落中养分胁迫的指示者[19]。

表2 特征磷脂脂肪酸(PLFAs)分类 Table 2 Classifications for PLFA signatures
磷脂脂肪酸分类 Classifications for PLFAs特征磷脂脂肪酸 PLFA signitures参考文献 References
SATFA: 饱和脂肪酸saturated fatty acids；MUFA:单不饱和脂肪酸mono-unsaturated fatty acids；G+，革兰氏阳性菌；G-，革兰氏阴性菌；f，真菌；b，细菌
SATFA14:0，15:0，16:0，17:0，18:0，20:0，i15:0，a15:0，i16:0，i17:0，a17:0 [19, 37]
MUFAi15:1，15:1ω6c，i16:1，16:1ω9c，16:1ω7c，16:1ω5c，i17:1，17:1ω8c，18:1ω5c，18:1ω9[19, 37]
G+i15:0，a15:0，i16:0，i17:0，a17:0[38, 39]
G-15:1ω6c，16:1ω9c，16:1ω7c，17:1ω8c，18:1ω5c，15:0 3-OH，cy17:0，cy19:0[38, 40]
f18:1ω9，18:2ω6,9[41, 42]
bG+，G-，14:0，15:0，16:0，17:0，18:0，20:0，i15:1，i16:1，i17:1，16:1 2-OH[36, 42, 43]
表选项
1.3.2 土壤理化性质测定

过2 mm筛后自然风干后的土壤参考《土壤农化分析》[44]测定理化性质。土壤含水量(SWC)经105 ℃连续烘干恒重后计算得出；pH值用酸度计(土 ∶ 水＝1 ∶ 2.5)测定；土壤有机碳(SOC)采用重铬酸钾氧化外加热法；土壤有效氮(AN)采用碱解扩散法；土壤总碳、总氮采用元素分析仪(Vario EL,Elementar,Ger)测定。不同植被类型下的土壤理化性质如附表所示。

1.4 数据分析

采用SPSS13.0 (SPSS Institute Inc.,2002)进行简单统计分析。采用单因素方差分析(One-way ANOVA)检验不同植被，不同土层下土壤理化性质、土壤微生物量及土壤微生物群落PLFAs差异的显著性。

采用CANOCO软件(Canoco for Windows 4.5) 对不同植被类型，不同土层中微生物群落的特征PLFAs (mol%)进行主成分分析(Principal Components Analysis,PCA)。对土壤理化性质、土壤微生物各类群PLFAs比例及土壤微生物量之间的关系进行冗余分析(Redundancy Analysis,RDA)。数据分析前进行log转换，通过蒙特卡罗显著性检验(Monte Carlo permutation test)筛选出对土壤微生物参数解释度呈现显著性(P<0.05)的环境变量。

2 结果分析 2.1 土壤微生物群落PLFAs分析

利用PCA分析不同植被类型，不同土层中的土壤微生物群落PLFAs图谱(图 1—图 3)。如图 1a、图 2a、图 3a所示，3个土层中，PC1和PC2对不同植被类型下PLFAs(mol%)变异的总解释度均大于80%，可以较全面地反映出PLFAs(mol%)的变异信息，且PC1的解释度均远远高于PC2；代表不同植被类型的点聚集程度不一致，表明不同植被类型下土壤微生物群落组成具有很大差异。特征PLFAs(mol%)在5种植被类型下的分布如图 1b、图 2b、图 3b所示，可以将单个特征PLFAs的坐标与植被类型对应起来。通过特征PLFAs(mol%)与PC1、PC2的相关性分析(表 3)可以得出两个主成分轴上所包含的PLFAs信息。

图 1 5种植被类型下0—5 cm土层中土壤微生物PLFAs (mol%) 分布的PCA分析 Fig. 1 PCA of soil microbial PLFAs(mol%) collected within 0—5 cm soil depth under five vegetation types 图a中误差线分别为PC1、PC2得分的标准差，n=3; a: 不同土壤中的土壤微生物群落PLFAs图谱; b: 特征PLFAs在5种植被类型下的分布
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图 2 5种植被类型下5—10 cm土层中土壤微生物PLFAs (mol%) 分布的PCA分析 Fig. 2 PCA of soil microbial PLFAs(mol%) collected within 5—10 cm soil depth under five vegetation types 图a中误差线分别为PC1、PC2得分的标准差，n=3
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图 3 5种植被类型下10—20 cm土层中土壤微生物PLFAs (mol%) 分布的PCA分析 Fig. 3 PCA of soil microbial PLFAs (mol%) collected within 10—20 cm soil depth under five vegetation types 图a中误差线分别为PC1、PC2得分的标准差，n=3
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表3 5种植被类型下3个土层中特征PLFAs (mol%) 与主成分PC1和PC2的相关性分析 Table 3 Correlation analyses of PLFA signatures (mol%) with PC1 and PC2 within three soil layers under five vegetation types
单种特征脂肪酸 Individual PLFA Signitrures0—5 cm土层 0—5 cm soil depth 5—10 cm土层 5—10 cm soil depth 10—20 cm土层 10—20 cm soil depth
列主成分1 PC1主成分2 PC2 列主成分1 PC1主成分2 PC2 列主成分1 PC1主成分2 PC2
* P<0.05; ** P<0.01
i14:00.466*-0.064-0.0820.470*0.726* *0.577* *
14:00.825* *0.0770.501* *0.471*0.903* *0.159
i15:10.817* *-0.197-0.197-0.2080.840* *0.284
15:1ω6c0.2240.885* *0000
i15:0-0.0900.779* *0.896* *0.0480.915* *-0.237
a15:00.440*-0.504* *-0.437*0.1870.821* *-0.054
15:00.940* *0.0840.427*0.503* *0.889* *0.060
i16:10.942* *-0.0030.427*0.476*0.708* *0.382
16:1 2OH-0.380*-0.330-0.639* *0.376*0.4500.850* *
i16:00.586* *0.097-0.0050.449*0.768* *0.374
16:1ω9c0.832* *-0.0350.1880.373-0.133-0.931* *
16:1ω7c0.885* *0.0340.743* *0.2190.848* *-0.392*
16:1ω5c0.1600.622* *0.512* *0.388*0.857* *-0.434*
16:00.895* *0.1850.486* *0.670* *0.861* *-0.033
i17:1-0.679* *0.0780.917* *0.163-0.989* *0.098
15:0 3OH0.271-0.777* *-0.914* *-0.0580.700* *-0.357
i17:00.803* *0.106-0.3150.640* *0.790* *0.464*
a17:00.869* *0.040-0.1840.379*0.858* *0.238
17:1ω8c0.849* *0.372-0.0140.669* *0.1910.481*
cy17:00.865* *-0.1930.324-0.0360.647* *-0.198
17:00.842* *0.283-0.873* *0.457*00
18:2ω6c000.564* *-0.800* *0.731* *-0.171
18:1ω9c-0.497* *0.3380.848* *0.0660.853* *-0.213
18:1ω9t-0.0140.437*0.671* *0.287-0.1540.472*
18:1ω5c0.765* *-0.373-0.525* *-0.1810.747* *-0.596* *
18:00.918* *0.2350.622* *0.608* *0.821* *-0.310
cy19:0-0.583* *0.561* *-0.3470.605* *0.872* *-0.117
20:4ω6c0.862* *0.377*-0.838* *0.417*0.2860.244
20:00.442*-0.679* *-0.907* *0.390*00
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图 1中0—5cm土层中，BP在PC1上的得分最高，其下土壤特征PLFAs中，i17:1、i15:0、i16:0、16:1ω7c与16:1ω5c占的比重较高；L与PC1、PC2均具有很高的相关性，15:0 3OH，a15:0，16:1 2OH，18:1ω5c和cy17:0为主要特征PLFAs；P、SM、B 3种植被聚集分布，在PC2上的得分较高，其下土壤特征PLFAs主要包括20:4ω6c，17:1ω8c，17:0，16:0，i17:0，15:0，14:0，a17:0，16:1ω7c。

5 —10 cm土层中，5种植被分布较分散，其下特征PLFAs分布差异大(图 2)。B在PC1上的得分最高，其下PLFAs主要为16:1 2OH，a15:0，17:0，20:4ω6c，20:0；SM下PLFAs主要包括i17:1，16:1ω7c，18:1ω9t，18:0，16:1ω5c，16:0，14:0，15:0；18:2ω6c，18:1ω9c，i15:0及cy17:0在L下占比重很大；BP下特征PLFAs主要包括18:1ω5c与15:0 3OH；P下主要为16:0，18:0，15:0，i16:1，14:0，16:1ω5c。

10—20 cm土层中，PC1与PC2对PLFAs(mol%)总变异的解释度在3个土层中最高(98.2%)(图 3)。BP和SM在PC1下得分较高，但其下PLFAs种类较少，主要为 i17:1和a15:0；B与L聚集分布，特征PLFAs主要为16:1ω7c，16:1ω5c，16:0，15:0 3OH，cy17:0，18:2ω6c，18:1ω9c，18:1ω5c，18:0；P下主要为i14:0，i17:0，i16:0，i16:1，i15:1，a17:0，i15:0。

对五种植被下3个土层中的微生物总量(PLFAs(T))及各类群PLFAs(真菌(f)、细菌(b)、革兰氏阳性菌(G+)、革兰氏阴性菌(G-))浓度进行比较分析，结果如表 4所示。PLFAs(T)和微生物各类群PLFAs浓度在0—20cm土层梯度上总体表现出降低趋势；但是，5—10 cm和10—20 cm土层，PLFAs(T)、G-和f分别在B、L和SM下表现出相反趋势。0—5 cm和5—10 cm土层，PLFAs(T)和微生物各类群PLFAs浓度随植被类型的变化规律总体上一致。0—10 cm土层，PLFAs(T)和各类群PLFAs浓度(除f外)在BP下均为最高，在其余4种植被下，PLFAs(T)和b随海拔升高而降低。10—20 cm土层，PLFAs(T)和微生物各类群PLFAs浓度(除b外)在P下均最高；PLFAs(T)和b在B下均最低；G-和f分别在L和BP下最低。

表4 五种植被类型下3个土层中各种微生物类群的PLFAs浓度 (平均值(标准差，n=3))/(nmol/g干土) Table 4 PLFAs Concentration of different soil microbial groups at three soil depths under five vegetation types
微生物类群 Microbial Taxa土层深度 Soil Depth/cmPBPBLSM
PLFAs(T): 磷脂脂肪酸总量；同一行中不同小写字母代表不同植被间差异显著
PLFAs(T)0—5118.81(4.10)b147.15(9.75)a114.16(3.26)b112.14(3.48)b111.97(14.45)b
5—1081.08(13.85)a91.21(3.06)a61.69(3.22)b57.16(1.41)bc45.82(0.23)c
10—2071.54(5.17)a67.63(0.55)a45.00(4.54)c51.69(2.06)b70.45(3.57)a
G+0—531.80(0.93)a32.25(1.76)a31.89(1.87)a20.55(0.95)c24.83(3.83)b
5—1023.18(3.10)a23.05(1.04)a17.16(0.98)b16.32(0.34)b12.71(0.22)c
10—2019.80(1.11)a14.60(0.31)b12.45(1.91)c13.71(0.75)bc11.67(0.98)c
G-0—527.94(1.18)a29.51(3.33)a27.81(1.58)a27.36(0.57)a21.14(4.63)b
5—1017.33(3.23)b20.84(0.58)a17.66(1.00)b2.90(0.10)d7.59(0.19)c
10—2016.74(1.66)a8.89(0.20)c12.66(0.58)b8.82(0.34)c13.03(0.78)b
f0—51.81(0.10)b2.15(0.28)b2.09(0.19)b1.62(0.15)b8.18(2.64)a
5—106.12(1.75)b7.92(0.55)a3.87(0.14)c7.00(0.08)ab6.77(0.27)ab
10—207.14(0.82)a4.24(0.13)c5.39(0.65)b4.85(0.07)bc5.09(0.51)bc
b0—5107.94(4.29)b122.99 (7.28)a104.03(2.90)b82.74(3.08)c82.05(12.17)c
5—1070.41(12.01)a69.21(2.32)a53.51(3.21)b42.30(1.20)c36.65(0.40)c
10—2059.93(4.38)a61.02(0.61)a37.44(4.04)b38.36(1.57)b63.40(2.98)a
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根据特征PLFAs的分类(表 1)，对各类PLFAs的比值在不同植被类型、不同土层间的差异进行分析(图 4)。可以看出，在0—20 cm土层梯度上，G+/G-在各植被类型(除SM)下总体上表现出增大趋势。0—5 cm土层中，G+/G-在L下最低，在其余四种植被下无显著性差异(P>0.05)；5—10 cm土层中，G+/G-在L下最高，其次是SM、P，在BP和B下无显著性差异；10—20 cm土层中，G+/G-在BP和L下最高，其次是P，在B和SM下无显著性差异。

图 4 5种植被类型下3个土层中G+/G-与f/b比较 (标准差，n=3) Fig. 4 Comparison of G+/G- and f/b within three soil layers under five vegetation types (SD，n=3) G+/G-: 革兰氏阳性菌/革兰氏阴性菌；f/b: 真菌/细菌
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0 —20 cm土层梯度上，f/b在各植被类型(除SM外)下总体上表现出升高趋势。0—5 cm土层中，f/b在前4种植被下无显著差异，在SM下最高；5—10 cm土层中，f/b在L和SM下最高，P与B下最低；10—20 cm土层中，f/b在B和L下最高，BP与SM下最低。

0—10 cm土层梯度上，SATFA/MUFA在各植被类型下(除P外)表现出升高趋势(图 5)；0—20 cm土层梯度上，SATFA/MUFA在B和L下表现出升高趋势，而在SM下表现出降低趋势。3个土层中，SATFA/MUFA均在L下最高，其次是B和P；0—5cm土层，SATFA/MUFA在BP下最低；5—10 cm和10—20 cm土层，SATFA/MUFA在SM下最低。

图 5 5种植被类型下3个土层中SATFA/MUFA的比较 (标准差，n=3) Fig. 5 Comparison of SATFA/MUFA within three soil layers under five vegetation types (SD,n=3) SATFA/MUFA： 饱和脂肪酸/单不饱和脂肪酸saturated fatty acids/mono-unsaturated fatty acids
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2.2 土壤理化性质与土壤微生物群落组成的相关性分析

如图 6所示，PC1和PC2分别解释微生物群落组成变异的39.3%和2.2%。土壤微生物各类群PLFAs浓度在PC1上的得分中，b(0.74)、G+(0.74)和SATFA(0.74)最高，f(-0.44)最低；各类群PLFAs的比例在PC1上的得分由大到小依次为f/b(-0.6)、G+/G-(-0.29)和SATFA/MUFA(-1.1)。通过Monte Carlo置换检验，得出对微生物群落组成各参数变异的解释度具有显著性的土壤理化指标为土壤有机碳(SOC)(P=0.001；F=24.08)；由各环境因子的箭头长度和与PC轴的夹角可以看出，对微生物指标影响最高的是SOC，其次是总碳(TC)，影响最低的是C/N。土壤微生物各类群PLFAs(除f外)浓度均与各理化指标呈现正相关关系，其中，b、G+和SATFA与SOC的正相关关系最显著；f与各理化指标(除有效氮(AN)外)均呈现负相关关系，其中，与pH的负相关关系最显著。土壤微生物各类群PLFAs的比例均与土壤理化指标呈现负相关关系；其中，f/b受SOC和pH的影响最大；G+/G-和SATFA/MUFA受SOC和TC的影响最大。

图 6 五种植被类型下土壤微生物群落组成与土壤理化性质的RDA分析 Fig. 6 RDA analyses for soil microbial community composition and soil properties PLFAs(T): PLFAs总量；TC: 总碳；TN: 总氮；SWC: 土壤含水量；SOC: 土壤有机碳；AN: 有效氮；C/N=SOC/AN
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3 讨论

PLFAs(T)和各类群(G+,G-,f,b)的PLFAs含量随土层加深总体上表现降低趋势(表 4)，这与可利用性养分的含量随凋落物分解进程在土层梯度上不断降低[45, 46]密切相关。微生物的特征PLFAs含量与养分的可利用性呈现正相关关系[6, 10, 29, 47]，所以不同植被下土壤养分的可利用C和N的差异导致了微生物群落组成的变异[48, 49]。Fierer 等[50]在平原和谷地的不同土层剖面上对土壤微生物群落组成变异的研究结果一致，随土层深度而降低的可利用性C含量与土层间的微生物组成差异有关[18, 51]。

RDA分析表明，SOC对土壤微生物群落组成的影响最大(图 6)，这与Saetre和Baath[52]的观点一致，虽然不同树种对土壤湿度及下层植被的影响有差异，但植被对土壤微生物群落组成的影响主要来自植被向土壤中输入的不同质量的有机质[53]。对不同土层、不同植被类型下单种特征PLFAs(mol%)的PCA分析(图 1—图 3)表明不同植被下PLFAs的种类及含量有显著差异[7, 15, 16]；源于不同植物的凋落物[54]或根系分泌物[55]释放的不同质量的碳[27]。3个土层中，BP在PC1的得分最高，表明其下土壤特征PLFAs(mol%)对总变异的贡献最大。BP由落叶阔叶林B和P组成，凋落物的多样性对基质的可利用性存在正效应[56]，导致其下PLFAs(T)在0—10 cm土层最高(表 4)。而SM下0—10 cm土层PLFAs(T)表现出最低值，可能是pH较低降低了C的可利用性[57, 58]。王欣[59]对燕山华北落叶松人工林叶凋落物分解特性的研究中表明，凋落叶中的C/N由大到小依次为落叶松、白桦、山杨，落叶松凋落物高浓度酚类物质和高C ∶ N[31, 60]导致其向土壤中输入的可利用性养分较少[6, 61](表 1)。

细菌和真菌共同组成了超过90%的土壤微生物生物量[13, 62]，且细菌主要利用易分解有机质[63]，所以细菌PLFAs含量与PLFAs(T)的变化具有一致性，与Pennanen 等[29]在芬兰Hailuoto岛的西海岸沿植被原生演替梯度的研究结果一致(图 6，表 4)。由于随土层加深C的可利用性降低[64]，不利于细菌生长[65, 66]；另外，逐渐降低的pH值环境也不利于细菌生长[21, 67]。f/b在4种乔木林下(除SM外)均表现出随土层加深而升高的趋势(图 1—图 4)，与养分梯度和pH值的变化一致(表 5)。落叶松下f/b的值高于3种阔叶林，这是由于针叶中的酚类物质含量显著高于阔叶，限制了细菌的生长[65- 66]，而同时，落叶松根系中共生的菌根真菌也对f/b贡献很大[68]；草本植物的根系大多在0—10 cm土层[50]；而pH值在这层也最低，不利于细菌群落生长[13, 14]，所以草甸f/b在0—10 cm土层内的值最高。该区域的研究表明，植物通过根际沉积物的形式释放的C为根际微生物提供能量和结构物质的来源[69]，pH值对f和b均有较大影响。一些菌根真菌会与特定植物的根形成共生结构[70]；因此真菌对植被类型的变化比细菌更敏感；而细菌对可利用性养分和pH值的变异更敏感。

表5 土壤理化性质，植被类型与土层深度之间的相关性分析 Table 5 Correlation analyses for soil properties,vegetation types and soil depths
因子 Parameters植被类型 Vegetation type土层深度 Soil depthTCTNANSOCC/NSWCpH
PLFAs(T): PLFAs总量；TC: 总碳；TN: 总氮；SWC: 土壤含水量；SOC: 土壤有机碳；AN: 有效氮；C/N=SOC/AN; *P<0.05；**P<0.01
植被类型Vegetation type10.000-0.355*-0.1880.196-0.365*-0.424* *0.441* *-0.492* *
土层深度Soil depth1-0.547* *-0.560* *-0.527* *-0.585* *-0.325*-0.609* *-0.366*
TC10.977* *0.609* *0.926* *0.338*0.546* *0.594* *
TN10.674* *0.889* *0.2350.645* *0.521* *
AN10.631* *0.2770.721* *0.187
SOC10.644* *0.528* *0.577* *
C/N10.0980.335*
SWC10.060
pH1
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G+和G-是细菌的两大类群，G-比G+在富营养环境中生长地更迅速[46]，而G+则对分解木质素和纤维素的贡献相对较大[71]。随土层加深凋落物中难分解的有机质比重增加，导致G+的比重增加；可利用性养分随土层加深而继续降低，因此导致G+/G-随土层梯度表现出升高趋势(除白桦林外)(图 4)。细根对水分和养分有很强的吸收作用[72, 73]，细根对养分的吸收促进了深层土壤中G-的生长[74]。先锋树种的根系分布较浅，北方森林表层30 cm的土壤内细根约占80%—90%，针叶林的细根总量小于阔叶林[75]。落叶松林0—5 cm土层中G+/G-低于其他阔叶林，与总的有机质含量在落叶松最低有关(表 1)，随土层加深针叶中高浓度酚类物质释放抑制G-生长[71]，导致G+/G-在落叶松林下最高。而草甸下G+/G-随土层梯度表现的先升高后降低趋势可能由于5—10 cm土层中易分解有机质降低及较低的pH值抑制了G-[29, 36]，而在10—20 cm土层pH及SOC同时升高促进了G-的生长[57, 58]。

MUFA中大部分是G-的特征PLFAs[19]，而G+的特征PLFAs全部为SATFA，因此0—10 cm土层梯度上，SATFA/MUFA与G+/G-的变化一致，随养分降低而升高[19, 50, 76]。由于混交林有机质种类丰富且含量高导致向土壤输入的可利用性养分多[77]，而SATFA/MUFA通常在养分高的环境下较低[76]，所以导致0—5 cm土层SATFA/MUFA在混交林下最低。而由于针叶中的可利用性养分含量相对较低及高浓度的酚类物质限制了G-的生长[71, 78]，因而3个土层中，SATFA/MUFA在落叶松下均最高。0—20 cm土层中，草甸下SATFA/MUFA与G+/G-随土层梯度的变化一致，受pH值影响较大。

4 结论

本文在不同海拔五种植被类型下的研究发现土壤有机碳(SOC)对土壤微生物群落组成的影响最为显著。土壤微生物PLFAs总量及各类群(f,b,G+,G-)的生物量随土层加深总体上表现降低趋势，与土壤可利用性养分的降低有关。G+/G-和f/b分别随土层加深总体上表现升高趋势，均与可利用性养分的降低和pH值的升高有关。混交林BP凋落物种类丰富，向土壤输入的可利用性碳含量最高，因此其下土壤PLFAs总量及各类群生物量总体上最高；落叶松与阔叶林相比，由于针叶中含高浓度酚类物质，向土壤输入的可利用性碳含量低，因此f/b和G+/G-值高；亚高山草甸下低的pH值影响了有机碳的可利用性，对f/b和G+/G-影响显著。综上，不同植被类型下土壤微生物群落组成的差异显著，而较低的pH值对有机碳的可利用性有一定的抑制作用，这对预测不同林型下的土壤微生物群落组成有重要的启示作用。

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