三峡库区两种森林中土壤细与环境因子的空间分布关系
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
土壤微生物在土壤生态系统中扮演着重要角色。然而,由于其个体尺寸的显微性、数量的庞大性、种类的多样性、时空分布的变异性和影响因素的复杂性,土壤微生物及其与环境的关系一直是微生物生态学研究的难点问题。近年来,生态系统中地下部分与地上部分关系研究已成为生态学研究的热点问题,因此,揭示土壤微生物地理学分布规律及其与环境因子关系对于准确评价土壤微生物在生态系统中的维持功能显得重要而且有意义。
     本论文以长江三峡库区两种典型的植被类型杉木马尾松混交林和马尾松纯林为研究对象。在林分尺度上,采用平板稀释方法结合16SrDNA和Biolog鉴定技术,对土壤可培养细的数量和多样性进行了比较分析;在此基础上,利用简单相关分析、线性回归分析、典范对应分析(CCA)方法对土壤细各大类群数量和细多样性与环境因子的关系进行了研究;最后,运用地统计学方法研究了土壤细的时空异质性规律以及土壤细与其环境影响因子的空间结构的相关性。获得以下研究结论:
     1.土壤可培养微生物数量:两种植被类型下不同季节三大类群土壤微生物的数量的大小关系均为:细>放线>真,其中,土壤细数量在2.475×106cfu/g-3.675×106cfu/g之间,放线数量1.031×106cfu/g~1.635×106cfu/g之间,真数量在4.836×104cfu/g~11.880×104cfu/g之间;相同季节马尾松林三大类群微生物数量均高于杉木马尾松混交林,且除细和秋季真外差异性显著。杉木马尾松林中,春季土壤细和真数量低于秋季,放线数量略高于秋季,但它们差异性不显著。马尾松纯林中春季土壤细和放线高于秋季,真数量低于秋季,差异性同样不显著。植被类型是引起土壤微生物数量差异的主要因素,而春、秋两季变化对微生物数量的差异影响不显著。
     2.土壤可培养细的群落组成:杉木马尾松混交林中春季主要的细类群及比例为:假单胞(Pseudomonas),69.86%、伯克霍尔德氏(Burkholderia),1.97%、芽孢杆(Bacillus),14.39%;秋季:假单胞,50.56%、伯克霍尔德氏,16.29%、芽孢杆,14.82%、溶杆(Lysobacter),1.95%、不动杆(Acinetobacter),0.52%、金黄杆(Chryseobacterium),2.81%,马尾松纯林中春季主要的细类群及比例为:假单胞,66.02%、伯克霍尔德氏,13.45%、假杆菌属(Flavobacterium),0.04%、芽孢杆,10.10%;秋季:假单胞,49.22%、伯克霍尔德氏,8.42%、克雷伯氏(Klebsiella),4.73%、金黄杆,1.86%、微球(Micrococcus),0.11%、芽孢杆,16.48%。两种植被类型的细群落组成有一定的区别。秋季细种类增加,但增加的几种细数量较少,同一植被类型季节间主要细类群比较相似。
     3.土壤可培养细多样性:春季两种植被类型4个多样性指数没有差异,秋季除了Pielou均匀度指数外,马尾松混交林其余多样性指数显著的高于杉木马尾松混交林。不同季节下,两种植被类型土壤细多样性指数的变化不同;杉木马尾松林中:除了Margalef丰富度指数外,秋季多样性指数均高于春季。马尾松林中,秋季4个多样性指数均高于春季。土壤可培养细的多样性受到季节和植被类型同时作用。与地上植被乔、灌、草3层的多样性比较表明地下土壤细的多样性在植被类型间的变化与地上植被正好相反。
     4.土壤细和土壤理化性质的相关性:杉木马尾松林土壤有机质、全氮、碳氮比、含水量和pH值均显著的高于马尾松纯林;绝大多数土壤细与土壤理化性质的相关性不显著。典范对应分析(CCA)的结果表明,CCA排序结果很好的解释了土壤细和本研究选取的5个土壤理化性质因子的关系,两种植被类型下CCA前两个排序轴的解释量分别高达90.40%和81.70%。表明土壤细受到理化性质因子的共同作用。
     5.土壤细与森林结构因子的回归分析:杉木马尾松林中,平均胸径(AverageDBH)与土壤细的线性相关性最强。其中,又以4.00m范围内平均胸径(AverageDBH(4))与土壤细的线性相关最为普遍,它与假单胞、伯克霍尔德氏、溶杆、蜡状芽孢杆数量和细总数以及Shanon-Wiener多样性指数构成显著的一元线性回归方程。马尾松纯林中,平均胸径和最大胸径(Max DBH)两个森林结构因子进入线性回归方程,分别是6.00m范围内平均胸径(Average DBH (6))、5.00m范围内平均胸径(Average DBH (5))和8.00m、7.00m、4.00m、3.00m范围内最大胸径(Max DBH (8)、Max DBH (7)、Max DBH (4)、Max DBH (3)),其中。以Average DBH (6)与土壤细的线性相关性最为普遍,它分别与假单胞、芽孢杆、蜡状芽孢杆数量、其他未鉴定土壤细数量和细总数以及Shanon-Wiener多样性指数构成显著的一元线性回归方程。典范对应分析(CCA)的结果表明,CCA排序结果很好的解释了土壤细和加入森林结构因子后的环境因子的关系,两种植被类型下CCA前两个排序轴的解释量有所下降,分别为83.4%和77.1%。
     6.地统计学空间异质性分析:在林分尺度上,半方差函数分析和克里格插值表明两种植被类型土壤细存在明显的空间结构和时空异质性。其空间自相关范围为51.00m。两种植被类型土壤细的空间异质性在不同方向上和尺度上,存在明显的区别。即土壤细空间变异存在各向异性。两种植被类型下与X轴呈45°和135°两个方向的土壤细的各向异性比在春、秋两季变化趋势相似。土壤细的时空变异不受土壤细数量和多样性季节变化的影响。
     7.土壤细与环境因子的空间变异的关系:地统计学的分析结果表明土壤细和与其紧密联系的土壤理化性质和森林结构这些环境因子存在明显相似的空间结构。它们之间的半方差函数平均值存在极显著相关性。土壤细不仅数量、多样性与环境因子紧密联系,其空间结构的形成也由这些环境因子的空间异质性引起。
Soil microbes play an important role in soil ecosystem system. However, the research of interrelationship between soil microbes and the environment has been one of the tough issues in microbial ecology field, due to the microscopic size of the individual, mass quantities, soil microbial diversity, the variability of space-time distribution, and the complexity of the influence factors. The correlation between above-ground and under-ground parts in ecological system is a hotspot in the current field of ecology research. Therefore, it's important and meaningful to reveal the laws of soil geographic distribution and interrrelationship with environmental factors, in order to give an accurate assessment of the soil microbes'role in maintaining the functions of the ecosystem.
     In this paper, two typical vegetation types (the Chinese-fir and Masson-pine mixed forests and the Masson-pine pure forests) in three gorges reservoir area were chosen to carry out this research work. In the scale of standing forest, using dilution-plate method combining 16SrDNA sequence analysis and Biolog identification technology, the number and diversity of the bacterium which soil can nurture were compared and analised; On this basis, using simple correlation analysis, the linear regression analysis, and canonical correspondence analysis (CCA) method, the relationship between the major groups of soil bacteria and bacterial diversity with the environmental factors were studied; Finally, geostatistics was used for analyzing the space-time heterogeneous distribution of the soil bacteria and the correlation with the spatial structure of the environmental impact factors. Conclusions are as follows:
     1.The quantity of culturable soil microbes:Under two vegetation types mentioned above and different seasons conditions, the quantity of the three major soil microbes groups sorted by size always are:bacteria> actinomycetes> fungi, in which the quantity of bacteria is 2.475×106cfu/g~3.675×106cfu/g, actinomycetes1.031×106cfu/g 1.635×106cfu/g, fungi 4.836×104cfu/g~11.88×104cfu/g; Quantities of the three types are all higher in pine forests than in mixed fir-pine forests in different seasons, in addition, exclude of bacteria and fungi in autumn there are significant differences. Fir-pine forests has less soil bacteria and fungi but slightly more actinomycetes in spring than in fall, and there are not significant differences. In pure pine forests, the quantities of bacteria and actinomycetes in spring are higher than in fall, spring fungi below autumn, also no significant difference. Vegetation type is the main reason lead to quantity variance of the soil microbes and that there are no significant differences between spring and fall seasons.
     2.The community composition of culturable soil bacteria:The main bacteria groups and their proportions in spring fir-pine mixed forests are:Pseudomonas 69.86%, Burkholderia 1.97%, Bacillus 14.39%; In autumn fir-pine mixed forests are: Pseudomonas 50.56%, Burkholderia 16.29%, Bacillus 14.82%, Lysobacter 1.95%, Acinetobacter 0.52%, Chryseobacterium 2.81%. The main bacteria groups and their proportions in spring pure pine forests are:Pseudomonas,66.02%, Burkholderia,13.45%, Flavobacterium 0.04%, Bacillus,10.10%; In autumn pure pine forests are:Pseudomonas, 49.22%, Burkholderia,8.42%, Klebsiella 4.73%, Chryseobacterium 1.86%, Micrococcus 0.11%, Bacillus subtilis,16.48%. The community composition of soil bacteria is varied between different vegetation types. There are more bacterial species in autumn than spring, and those new types are with relatively low quantities, for the same vegetation type there are not much difference in the major soil bacteria species despite different seasons.
     3.The diversity of culturable soil bacteria:There is no significant difference between the 4 diversity indexs of the soil bacteria under two kinds of forests in spring, but indexs except Pielou are significantly higher in pine forests than that of fir-pine mixed forests in the fall. The indexmovement varied under different seasons for both vegetation types. Exclude Margalef index, other diversity indexs are all higher for fir-pine mixed forests in autumn than spring. In pine forests, all 4 diversity indexs are higher in the fall. The diversity of culturable soil bacteria is effected by both season and vegetation type. Compared with the diversity of above ground Arbor-Bush-Grass layers vary from vegetation types, the change of diversity of soil bacteria proves the contrary.
     4.The correlation between soil bacteria and soil properties:The content of soil organic matter, total nitrogen, carbon nitrogen ratio, moisture content and PH value are all significantly higher in fir-pine mixed forests than pure pine forests. The overwhelming majority of the correlations between soil bacteria and soil properties are not significant. Canonical Correlation Analysis(CCA) ordination well demonstrated the relationship between soil bacteria and the 5 soil properties selected for this research, the first two axises explained as high as 90.4% and 81.7% separately for two vegetation types.
     5.The regression analysis between soil bacteria and forest structure factors:Among those factors Average DBH (diameter at breast height) has the strongest linear correlation with soil bacteria in fir-pine mixed forests, moreover, soil bacteria is commonest relevant with Average DBH within 4.00m, Average DBH (4) together with quantities of Pseudomonas, Burkholderia, Dissolving bacteria, Bacillus cereus and total bacteria and the diversity index of Shanon-Wiener constitute a significant linear regression equation. In pure pine forests, Average DBH and Max DBH were entered in the equation of linear regression,separately with Average DBH (6), Average DBH (5), and Max DBH (8), Max DBH (7), Max DBH (4), Max DBH (3), among all this the Average DBH(6) had most significant linear correlation with soil bacteria, it together with quantities of Pseudomonas, Bacillus, Bacillus cereus, other unidentified bacteria and total bacteria, and the diversity index of Shanon-Wiener constitute a significant linear regression equation. Canonical Correlation Analysis(CCA) ordination well demonstrated the relationship between soil bacteria and the environmental factors added with forest structure factors, the correctly explained volume of the first two axises slightly dropped, accounted for 87.4% and 77.1% separately for two vegetation types.
     6.The analyse of spatial heterogeneity based on geostatistics method:On the forest stand scale, the results of semivariance analysis and kriging interpolation showed significant spacestructure and space-time heterogeneity for soil bacteria under two vegetation types. The range of spatial autocorrelation is 51.00m. Spatial heterogeneity obviously differs from directions and scales for soil bacteria under two vegetation types, thus spatial variation of soil bacteria shows remarkable anisotropy. The variation trend of the soil bacteria anisotropy ratio between the two directions of X-axis 45°and 135°is nearly the same in spring and autumn season under the two kinds forests. The space-time heterogeneity of soil bacteria is independent of the seasonal variation in quantity and diversity.
     7.The relationship of spatial variation between soil bacteria and environmental factors:Results of geostatistical analysis showed that the spatial structure of soil bacteria is very similar to that of environmental factors including their closely interrelated soil properties and forest structure factors. There is very significant correlation between their average semivariograms. Not only the quantity and diversity of soil bacteria closely related to environment factors but also the spacial structure formed by spatial heterogeneity of environmental factors.
引文
1. 毕江涛,贺达汉,黄泽勇,杨磊,马巧荣.退化生态系统植被恢复过程中土壤微生物群落活性响应.水土保持学报,2008,22(4):195-200
    2.陈国阶,徐琪,杜榕桓.三峡工程对生态与环境的影响及对策研究.北京:科学出版社,1995,204-224
    3. 陈华癸,李阜棣,陈文新,曹燕珍.土壤微生物学.上海:上海科学技术出版社,1981,39-40.
    4.陈声明,林海萍,张立钦.微生物生态学导论。北京:高等教育出版社,2007,172
    5.陈义强,刘国顺,习红昂.微尺度下烟田铁的空间变异性及其与烟叶铁的相关分析.生态学报,2009,(03):1448-1458
    6.陈志雄,Michel V.封丘地区土壤水分平衡研究——Ⅰ.田间土壤湿度的空间变异.土壤学报,1989,(04):309-315
    7.池振明.现代微生物生态学.北京:科学出版社,2005,5
    8.樊智毅.基于遥感和地统计学的植被生物量估算与空间分析.[硕士学位论文].广州:中山大学,2007
    9.冯益明,张海军,吕全,梁军,张星耀.松材线虫病在我国适生性分布的定量估计.林业科学,2009,45(2):65-71
    10.龚元石,廖超子,李保国.土壤含水量和容重的空间变异及其分形特征.土壤学报,1998,35(01):10-15
    11.郭旭东,傅伯杰,马克明,陈利顶,杨福林.基于GIS和地统计学的土壤养分空间变异特征研究——以河北省遵化市为例.应用生态学报,2000,(04):557-563
    12.贺金生, 方精云,马克平,黄建辉.生物多样性与生态系统生产力: 为什么野外观测和受控实验结果不一致?植物生态学报,2003,27(6):835-844
    13.洪滔,吴承祯,范海兰,宋萍.应用地统计学方法定量评价森林截留的地理变化规律.山地学报,2007,25(6):691-697
    14.华建峰,姜勇,梁文举.植被覆盖对土壤线虫营养类群空间分布的影响.应用生态学报,2006,17(2):295-299
    15.霍霄妮.北京耕作土壤重金多尺度空间结构.农业工程学报,2009,25(3):221-229
    16.雷志栋,杨诗秀,许志荣,瓦肖尔G.土壤特性空间变异性初步研究.水利学报, 1985,(09):10-21
    17.李子忠,龚元石.不同尺度下田间土壤水分和混合电导率空间变异性与套合结构模型.植物营养与肥料学报,2001,7(3):255-261
    18.梁文举,姜勇,闻大中.土壤生物空间异质性及其在土壤生态学研究中的意义.面向农业与环境的土壤科学综述篇,2004,419-426
    19.林艳.基于地统计学与GIS的土壤重金污染及评价.[硕士学位论文].长沙:中南大学图书馆,2009
    20.刘恩科,赵秉强,李秀英,姜瑞波,李燕婷,Hwat B S.长期施肥对土壤微生物量及土壤酶活性的影响,植物生态学报,2008,32(1):176-182
    21.罗勇,陈家宙,林蓉,王双.基于土地利用和微地形的红壤丘岗区土壤水分时空变异性.农业工程学报,2009,25(2):36-45
    22.马克平,钱迎倩.生物多样性研究的原理与方法.北京:中国科学技术出版社,1994,5-10
    23.马晓慧.羊草草原土壤微生物量磷的空间异质性分析.首都师范大学学报(自然科学版),2008,29(1):60-66
    24.牛丽丽,余新晓,岳永杰.北京松山自然保护区天然油松林不同龄级立木的空间点格局.应用生态学报,2008,19(7):1414-1418
    25.欧芷阳,苏志尧,叶永昌,朱剑云,刘颂颂.东莞地表植被对表层土壤化学特性的指示作用.生态学报,2009.29(2):984-992
    26.潘维旺,李景英,周启水,阮若江.土壤微生物与森林环境因子关系初探.南昌水专学报,1998,17(4):39-41
    27.田柳.北京杨树人工林微生物区系比较分析.[硕士学位论文].北京:中国林业科学研究院,2008
    28.王波.迁安市农田重金含量空间变异性.应用生态学报,2006a,17(8):1495-1500
    29.王波.海河低平原区农田重金含量的空间变异性——以河北省肥乡县为例.生态学报,2006b,26(12):4078-4090
    30.王纪华,沈涛,陆安祥,刘良云,马智宏.田块尺度上土壤重金污染地统计分析及评价.农业工程学报,2008,24(11):226-229
    31.王其兵,贺金生.长江三峡地区退化生态系统土壤微生物的初步研究.生物多样性,1997,5(4):241-245
    32.王晓春.长白山岳桦种群格局的地统计学分析.应用生态学报,2002,13(7):781-784
    33.王政权.地统计学及其在生态学中的应用.北京:科学出版社,1999,1-5
    34.吴愉萍.基于磷脂脂肪酸(PLFA)分析技术的土壤微生物群落结构多样性的研究.[博士学位论文].杭州:浙江大学图书馆,2009
    35.吴宇澄,骆永明,滕应,李振高.多氯联苯污染农田土壤的细群落结构差异及其影响因素.土壤学报,2007,44(5):854-859
    36.席雪飞,贾建伟,王磊,唐玉姝,王红丽,张文俭,付小花,乐毅全.长江口九段沙湿地土壤有机碳及微生物陆向分布.农业环境科学学报,2009,28(12):2574-2579
    37.肖慈英,阮宏华,屠六邦.宁镇山区不同森林土壤生物学特性的研究.应用生态学报,2002,13(9):1077-1081
    38.肖文发,李建文,于长青,马娟,程润梅,刘少英,王金锡,葛继稳.长江三峡库区陆生动植物生态.重庆:西南师范大学出版社,2000,1-11
    39.许光辉,李振高.微生物生态学。南京:东南大学出版社,1991,110-118
    40.杨涛,徐慧,李慧,方德华,朱教君.樟子松人工林土壤养分、微生物及酶活性的研究.水土保持学报,2005,19(3):50-53
    41.于婧.基于GIS和地统计学方法的土壤养分空间变异及应用研究.[博士学位论文].武汉:华中农业大学图书馆,2007
    42.张金屯,孟东平.芦芽山华北落叶松林不同龄级立木的点格局分析.生态学报,2004,24(1):35-40
    43.张奇春,王光火,方斌.不同施肥处理对水稻养分吸收和稻田土壤微生物生态特性的影响.土壤学报,2005,1:116-121
    44.张薇等.土壤微生物多样性及其环境影响因子研究进展.生态学杂志,2005,24(1):48-52
    45.赵萌,方晰,田大伦.第2代杉木人工林地土壤微生物数量与土壤因子的关系.林业科学,2007,43(6):7-12
    46.赵彦锋,史学正,于东升,黄标,王洪杰,赵永存,Ingrid O, Karin B.小尺度土壤养分的空间变异及其影响因素探讨—以江苏省无锡市典型城乡交错区为例.土壤通报,2006,37(02):2214-2219
    47.中国林业科学研究院林业研究所森林土壤研究室.LY/T 1210-1275-1999.中华 人民共和国共和国林业行业标准(森林土壤分析方法).北京: 中国标准出版社,2000
    48.周隽,国庆喜.林木竞争指数空间格局的地统计学分析.东北林业大学学报,2007,35(9):42-44
    49.朱宏.柽柳林地土壤微生物量碳及相关因素的分布特征.土壤学报,2008,45(2):375-383
    50. Adams G A, Wall D H. Biodiversity above and below the surface of soils and sediments:linkages and implications for global change. BioScience,2002,50: 1043-1048
    51. Agnelli A, Ascher J, Corti G., Ceccherini M T, Nannipieri P, Pietramellara G Distribution of microbial communities in a forest soil profile investigated by microbial biomass, soil respiration and DGGE of total and extracellular DNA. Soil Biology & Biochemistry,2004,36(5):859-868
    52. Balser T C, Firestone M K. Linking microbial community composition and soil processes in a California annual grassland and mixed-conifer forest.Biogeochemistry 2005,73,395-415
    53. Barg A K, Edmonds R L. Influence of partial cutting on site microclimate, soil nitrogen dynamics, and microbial biomass in Douglas-fir stands in western Washington. Canadian Journal of Forest Research,1999,29(6):705-713
    54. Bengtson P, Falkengren-Grerup U, Bengtsson G. Spatial distributions of plants and gross N transformation rates in a forest soil. Journal of Ecology,2006,94(4):754-764
    55. Bradford M A, Jones T H, Bardgett R D, Black H I J, Boag B, Bonkowski M, Cook R, Eggers T, Gange A C, Grayston S J, Kandeler E,McCaig A E, Newington J E, Prosser J I, Setala H, Staddon P L, Tordoff G M,Tscherko D, Lawton J H Impacts of soil faunal community composition on model grassland ecosystems. Science,2002,298:615-618
    56. Cambardella C A, Moorman T B, Novak J M, Parkin T B, Karlen D L, Turco R F, Konopka A E. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal,1994,58(5):1501-1510
    57. Cho J-C, Tiedje J M. Biogeography and degree of endemicity of fluorescent Pseudomonas strains in soil. Applied and Environmental Microbiology,2000,66(12): 5448-5456
    58. Delbari M, Afrasiab P, Loiskandl W. Using sequential Gaussian simulation to assess the field-scale spatial uncertainty of soil water content. Catena,2009,79(2): 163-169
    59. Falush D, Wirth T, Linz B, Pritchard J K, Stephens M, Kidd M, Blaser M J, Graham D Y, Vacher S, Perez-Perez G I, Yamaoka Y, Megraud F, Otto K Reichard U, Katzowitsch E, Wang X Y, Achtman M, Suerbaum S. Traces of human migrations in Helicobacter pylori populations. Science,2003,299(5612): 1582-1585
    60. Gans J, Wolinsky M, Dunbar J. Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science,2005,309(5739):1387-1390
    61. Ge Y, He J Z, Zhu Y G, Zhang J B, Xu Z H, Zhang L M, Zheng Y M. Differences in soil bacterial diversity:driven by contemporary disturbances or historical contingencies? Isme Journal,2008,2(3):254-264
    62. Gomes N M, Da Silva A M, De Mello C R, De Faria M A, De Oliveira P M Fitting methods and semi-variogram models applied to the study of spatial variability of physical-hydric soil attributes. Revista Brasileira De Ciencia Do Solo,2007,31(3): 435-443
    63. Graham M H, Haynes R J. Catabolic diversity of soil microbial communities under sugarcane and other land uses estimated by Biolog and substrate-induced respiration methods. Applied Soil Ecology,2005,29(2):155-164
    64. Green J L, Bohannan B J M, Whitaker R J. Microbial biogeography:from taxonomy to traits. Science,2008,320(5879):1039
    65. Guo D L, Mou P, Jones R H, Mitchell R J. Spatio-temporal patterns of soil available nutrients following experimental disturbance in a pine forest. Oecologia, 2004,138(4):613-621
    66. Hanan E J, Ross M S. Across-scale patterning of plant-soil-water interactions surrounding tree islands in Southern Everglades landscapes. Landscape Ecology, 2010,25(3):463-476
    67. Hong H, Pruden A, Reardon K F. Comparison of CE-SSCP and DGGE for monitoring a complex microbial community remediating mine drainage. Journal of Microbiological Methods,2007,69(1):52-64
    68. Hooper D U, Bignell D E, Brown V K, Brussard L, Mark Dangerfield J, Wall D H, Wardle D A, Coleman D C, Giller K E, Lavelle P. Interactions between aboveground and belowground biodiversity in terrestrial ecosys-tems. BioScience,2000,50: 1049-1061
    69. Horner-Devine M C, Lage M, Hughes J B, Bohannan B J M. A taxa'Carea relationship for bacteria. Nature,2004,432(7018):750-753
    70. Johnson D, Booth R E, Whiteley A S, Bailey M J, Read D J, Grime J P, Leake J R. Plant community composition affects the biomass, activity and diversity of microorganisms in limestone grassland soil. European Journal of Soil Science.2003,54,671-677
    71. Katayama A, Kume T, Komatsu H, Ohashi M, Nakagawa M, Yamashita M, Otsuki K, Suzuki M, Kumagai T. Effect of forest structure on the spatial variation in soil respiration in a Bornean tropical rainforest. Agricultural and Forest Meteorology,2009,149:1666-1673
    72. Kong Y H, Beer M, Seviour R J, Lindrea K C, Rees G N. Structure and functional analysis of the microbial community in an aerobic:Anaerobic sequencing batch reactor (SBR) with no phosphorus removal. Systematic and Applied Microbiology, 2001,24(4):597-609
    73. Kozdroj J. Microbial community in the rhizosphere of young maize seedlings is susceptible to the impact of introduced pseudomonads as indicated by FAME analysis. Journal of General and Applied Microbiology,2008,54(4):205-210
    74. Lamarche J, Bradley R L, Pare D, Legare S, Bergeron Y. Soil parent material may control forest floor properties more than stand type or stand age in mixedwood boreal forests. Ecoscience,2004,11(2):228-237
    75. Lane D J, Pace B, Olsen G J, Stahl D A, Sogin M L, Pace N R. Rapid determination of 16S rRNA sequences for phylogeneticanalysis. Proc. Natl. Acad. Sci. U.S.A.1985,82,6955-6959
    76. Lee S H, Lee H J, Kim S J, Lee H M, Kang H, Kim Y P. Identification of airborne bacterial and fungal community structures in an urban area by T-RFLP analysis and quantitative real-time PCR. Science of the Total Environment,2010,408(6): 1349-1357
    77. Li J, Zhao C Y, Zhu H, Wang F, Wang L J, Kou S Y. Multi-scale heterogeneity of soil moisture following snow thawing in Hatoxylon ammodendron shrubland. Science in China Series D-Earth Sciences,2007,50:49-55
    78. Li J, Zhao C Y, Song Y J, Sheng Y, Zhu H. Spatial patterns of desert annuals in relation to shrub effects on soil moisture. Journal of Vegetation Science,2010,21(2): 221-232
    79. Liang W J, Jiang Y, Li Q, Liu Y J, Wen D Z. Spatial distribution of bacterivorous nematodes in a Chinese Ecosystem Research Network (CERN). Ecological Research, 2005,20(4):481-486
    80. Liu Z F, Liu G H, Fu B J, Zheng X X. Relationship between plant species diversity and soil microbial functional diversity along a longitudinal gradient in temperate grasslands of Hulunbeir, Inner Mongolia, China. Ecological Research,2008,23(3): 511-518
    81. Liu Z F, Fu B J, Zheng X X, Liu G H. Plant biomass, soil water content and soil N:P ratio regulating soil microbial functional diversity in a temperate steppe:A regional scale study. Soil Biology & Biochemistry,2010,42(3):445-450
    82. Macgregor B J, Amann R. Single-stranded conformational polymorphism for separation of mixed rRNAS (rRNA-SSCP), a new method for profiling microbial communities. Systematic and Applied Microbiology,2006,29(8):661-670
    83. Masseret E, Sukenik A. Amplified rDNA Restriction Analysis (ARDRA) for monitoring of potentially toxic cyanobacteria in water samples. Israel Journal of Plant Sciences,2008,56(1-2):75-82
    84. Mohanty B P, Skaggs T H, Famiglietti J S. Analysis and mapping of field-scale soil moisture variability using high-resolution ground-based data during the Southern Great Plains 1997(SGP97) Hydrology Experiment. Water Resource Research,2000,36(4):1023-1028
    85. Muruganandam S, Israel D W, Robarge W P. Nitrogen Transformations and Microbial Communities in Soil Aggregates from Three Tillage Systems. Soil Science Society of America Journal,2010,74(1):120-129
    86. Nusslein K, Tiedje Jm Soil bacterial community shift correlated with change from forest to pasture vegetation in a tropical soil. Applied and Environmental Microbiology,1999,65(8):3622
    87. O'malley M A. The nineteenth century roots of'everything is everywhere'. Nature Reviews Microbiology,2007,5(8):647-651
    88. Perie C, Munson A D, Caron J. Use of spectral analysis to detect changes in spatial variability of forest floor properties. Soil Science Society of AmericaJournal,2006, 70:439-447
    89. Queiroz J C B, Sturaro J R, Saraiva A C F, Landim P M B. Geochemical characterization of heavy metal contaminated area using multivariate factorial kriging. Environmental Geology,2008,55(1):95-105
    90. Reis A P, Da Silva E F, Sousa A J, Matos J, Patinha C, Abenta J, Fonseca E C Combining GIS and stochastic simulation to estimate spatial patterns of variation for lead at the Lousal mine, Portugal. Land Degradation & Development,2005,16(2): 229-242
    91. Reis A P, Patinha C, Da Silva E F, Sousa A, Figueira R, Sergio C, Novais V Assessment of human exposure to environmental heavy metals in soils and bryophytes of the central region of Portugal. International Journal of Environmental Health Research,2010,20(2):87-113
    92. Remenant B, Grundmann G L, Jocteur-Monrozier L. From the micro-scale to the habitat:Assessment of soil bacterial community structure as shown by soil structure directed sampling. Soil Biology & Biochemistry,2009,41(1):29-36
    93. Rinklebe J, Langer U. Relationship between soil microbial biomass determined by SIR and PLFA analysis in floodplain soils. Journal of Soils and Sediments,2010, 10(1):4-8
    94. Rousk J, Brookes P C, Baath E. The microbial PLFA composition as affected by pH in an arable soil. Soil Biology & Biochemistry,2010,42(3):16-520
    95. Salomo S, Munch C, Roske I. Evaluation of the metabolic diversity of microbial communities in four different filter layers of a constructed wetland with vertical flow by Biolog (TM) analysis. Water Research,2009,43(18):4569-4578
    96. Schroeder W, Pesch R. Synthesizing bioaccumulation data from the German metals in mosses surveys and relating them to ecoregions. Science of the Total Environment, 2007,374(2-3):311-327
    97. Skowronska A. Distribution of microbial-selected populations in Lake North Mamry by fluorescent in situ hybridization. Polish Journal of Environmental Studies,2007, 16(1):123-128
    98. Sollitto D, Romic M, Castrignano A, Romic D, Bakic H. Assessing heavy metal contamination in soils of the Zagreb region (Northwest Croatia) using multivariate geostatistics. Catena,2010,80(3):182-194
    99. Staley J T. Biodiversity:are microbial species threatened? Current Opinion in Biotechnology,1997,8(3):340-345
    100.Stark C H E, Condron L M, Stewart A, Di H J, O'callaghan M. Small-scale spatial variability of selected soil biological properties. Soil Biology & Biochemistry,2004, 36(4):601-608
    101.Sumfleth K, Duttmann R. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecological Indicators,2008,8(5):485-501
    102.Todorova N., Lotsova V., Kararnfilov V., Hiebaurn G. Effect of chronic oil pollution on ARDRA patterns of bacterial communities inhabiting coastal marine sediments. Comptes Rendus De L Academie Bulgare Des Sciences,2008,61(3):357-362
    103.Torsvik V, Daae F L, Sandaa R A. Novel techniques for analysing microbial diversity in natural and perturbed environments. Journal of Biotechnology,1998, 64(1):53-62
    104.Toyota K, Kuninaga S. Comparison of soil microbial community between soils amended with or without farmyard manure. Applied Soil Ecology,2006,33(1):39-48
    105.Traver E, Ewers B E, Mackay D S, Loranty M M. Tree transpiration varies spatially in response to atmospheric but not edaphic conditions. Functional Ecology, 2010,24(2):273-282
    106.Tsegaye T, Hill R L. Intensive tillage effects on spatial variability of soil test, p lant growth, and nutrient up take measurement. Soil Scil,1998,163 (2):155-165.
    107.Vandamme P, Pot B, Gillis M, De Vos P, Kersters K, Swings J. Polyphasic taxonomy, a consensus approach to bacterial systematics. Microbiology and Molecular Biology Reviews,1996,60(2):407
    108.Wei G F,LuHF, Zhou Z H, Xie H B, Wang A S, Nelson K, Zhao L P. The microbial community in the feces of the giant panda (Ailuropoda melanoleuca) as determined by PCR-TGGE profiling and clone library analysis. Microbial Ecology, 2007,54(1):194-202
    109.Wertz S, Degrange V, Prosser J I, Poly F, Commeaux C, Freitag T, Guillaumaud N, Le R X. Maintenance of soil functioning following erosion of microbial diversity. Environmental Microbiology,2006,8(12):2162-2169
    110.Whitaker R J, Grogart D W, Taylor J W. Geographic barriers isolate endemic populations ofhyperthemophillic Archaea Science.2003,301:976-978
    111.Wu L, Yu Y H, Zhang T L, Feng W S, Zhang X, Li W. PCR-DGGE Fingerprinting Analysis of Plankton Communities and Its Relationship to Lake Trophic Status. International Review of Hydrobiology,2009,94(5):528-541
    112.Yankelevich S N, Fragoso C, Newton A C, Russell G, Heal O W. Spatial patchiness of litter, nutrients and macroinvertebrates during secondary succession in a Tropical Montane Cloud Forest in Mexico. Plant and soil,2006,286(1-2):123-139
    113.Zak D R, Holmes W E, White D C, Peacock A D, Tilman D. Plant diversity, microbial communities, and ecosystem function:are there any links? Ecology. 2003,84,2042-2050
    114.Zhang X Y, Sui Y Y, Zhang X D, Meng K, Herbert S J. Spatial variability of nutrient properties in black soil of northeast China. Pedosphere,2007,17(1):19-29
    115.Zhang Y Y, Dong J D, Yang B, Ling J, Wang Y S, Zhang S. Bacterial community structure of mangrove sediments in relation to environmental variables accessed by 16S rRNA gene-denaturing gradient gel electrophoresis fingerprinting. Scientia Marina,2009,73(3):487-498
    116.Zhao X, Wang Q, Kakubari Y. Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient. Soil Biology & Biochemistry,2009,41(7):1466-1474
    117.Zheng J, He M, Li X, Chen Y, Li X, Liu L. Effects of Salsola passerina shrub patches on the microscale heterogeneity of soil in a montane grassland, China. Journal of Arid Environments,2008,72(3):150-161

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700