微生物生态学理论框架

微生物是生态系统的重要组成部分,直接或间接地参与所有的生态过程。微生物生态学是基于微生物群体的科学,利用微生物群体DNA/RNA等标志物,重点研究微生物群落构建、组成演变、多样性及其与环境的关系,在生态学理论的指导和反复模型拟合下由统计分析得出具有普遍意义的结论。其研究范围从基因尺度到全球尺度。分子生物学技术的发展,使人们可以直接从基因水平上考查其多样性,从而使得对微生物空间分布格局及其成因的深入研究成为可能。进而可以从方法学探讨微生物生物多样性、分布格局、影响机制及其对全球变化的响应等。在微生物生态学研究中,群落构建与演化、分布特征(含植物-微生物相互关系)、执行群体功能的机理(生物地球化学循环等)、对环境变化的响应与反馈机理是今后需要关注的重点领域。概述了微生物生态学的概念,并初步提出其理论框架,在对比宏观生态学基础理论和模型的基础上,分析微生物多样性的研究内容、研究方法和群落构建的理论机制,展望了今后研究的重点领域。     

Multivariate analyses in microbial ecology

Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology.     

Conceptualizing a Genomics Software Institute (GSI)

Microbial ecology has been enhanced greatly by the ongoing 'omics revolution, bringing half the world's biomass and most of its biodiversity into analytical view for the first time; indeed, it feels almost like the invention of the microscope and the discovery of the new world at the same time. With major microbial ecology research efforts accumulating prodigious quantities of sequence, protein, and metabolite data, we are now poised to address environmental microbial research at macro scales, and to begin to characterize and understand the dimensions of microbial biodiversity on the planet. What is currently impeding progress is the need for a framework within which the research community can develop, exchange and discuss predictive ecosystem models that describe the biodiversity and functional interactions. Such a framework must encompass data and metadata transparency and interoperation; data and results validation, curation, and search; application programming interfaces for modeling and analysis tools; and human and technical processes and services necessary to ensure broad adoption. Here we discuss the need for focused community interaction to augment and deepen established community efforts, beginning with the Genomic Standards Consortium (GSC), to create a science-driven strategic plan for a Genomic Software Institute (GSI).     

Improved Bacteriophage Genome Data is Necessary for Integrating Viral and Bacterial Ecology

The recent rise in “omics”-enabled approaches has lead to improved understanding in many areas of microbial ecology. However, despite the importance that viruses play in a broad microbial ecology context, viral ecology remains largely not integrated into high-throughput microbial ecology studies. A fundamental hindrance to the integration of viral ecology into omics-enabled microbial ecology studies is the lack of suitable reference bacteriophage genomes in reference databases—currently, only 0.001 % of bacteriophage diversity is represented in genome sequence databases. This commentary serves to highlight this issue and to promote bacteriophage genome sequencing as a valuable scientific undertaking to both better understand bacteriophage diversity and move towards a more holistic view of microbial ecology.     

Metagenomics--the key to the uncultured microbes

It is widely accepted that up to 99.8% of the microbes present in many environments are not readily culturable. 'Metagenome technology' tries to overcome this bottleneck by developing and using culture-independent approaches. From the outset, metagenome-based approaches have led to the accumulation of an increasing number of DNA sequences, but until this time the sequences retrieved have been those of uncultured microbes. These genomic sequences are currently exploited for novel biotechnological and pharmaceutical applications and to increase our knowledge on microbial ecology and physiology of these microbes. Using the metagenome sequences to fully understand how complex microbial communities function and how microbes interact within these niches represents a major challenge for microbiologists today.     

Landscape ecology development supported by geospatial technologies: A review

Numerous technologies have contributed to the recent development of landscape ecology, especially the geospatial technological advances constitute a revolution in landscape ecology. Extensive applications of geospatial technologies, such as fractal theory, geographic information systems (GIS) and remote sensing (RS) in landscape ecology research, suggest the necessity of such a review of research progress in this field. In this study, a brief introduction to fractal theory, GIS and RS and how they were applied in landscape ecology were provided first. Then, the current state-of-the-art was summarized and analyzed as reference for further promoting the development of landscape ecology science and its applications. Finally, opportunities and challenges of landscape ecology applications using these new technologies were discussed and concluded for future research. It was contended that a combination and integration of these technologies can substantially advance the study of landscape ecology for data acquisition, process modeling, scale transformation, result analysis and visualization. However, as no theoretical framework and application cases of applying an integration of fractal theory, GIS and RS techniques to landscape ecology research have been established, further studies are still much needed.     

Predicting bacterial community assemblages using an artificial neural network approach

Understanding the interactions between the Earth's microbiome and the physical, chemical and biological environment is a fundamental goal of microbial ecology. We describe a bioclimatic modeling approach that leverages artificial neural networks to predict microbial community structure as a function of environmental parameters and microbial interactions. This method was better at predicting observed community structure than were any of several single-species models that do not incorporate biotic interactions. The model was used to interpolate and extrapolate community structure over time with an average Bray-Curtis similarity of 89.7. Additionally, community structure was extrapolated geographically to create the first microbial map derived from single-point observations. This method can be generalized to the many microbial ecosystems for which detailed taxonomic data are currently being generated, providing an observation-based modeling technique for predicting microbial taxonomic structure in ecological studies.     

Application of the microbial community coalescence concept to riverine networks

Flows of water, soil, litter, and anthropogenic materials in and around rivers lead to the mixing of their resident microbial communities and subsequently to a resultant community distinct from its precursors. Consideration of these events through a new conceptual lens, namely, community coalescence, could provide a means of integrating physical, environmental, and ecological mechanisms to predict microbial community assembly patterns better in these habitats. Here, we review field studies of microbial communities in riverine habitats where environmental mixing regularly occurs, interpret some of these studies within the community coalescence framework and posit novel hypotheses and insights that may be gained in riverine microbial ecology through the application of this concept. Particularly in the face of a changing climate and rivers under increasing anthropogenic pressures, knowledge about the factors governing microbial community assembly is essential to forecast and/or respond to changes in ecosystem function. Additionally, there is the potential for microbial ecology studies in rivers to become a driver of theory development: riverine systems are ideal for coalescence studies because regular and predictable environmental mixing occurs. Data appropriate for testing community coalescence theory could be collected with minimal alteration to existing study designs.     

代谢异速生长理论及其在微生物生态学领域的应用

新陈代谢是生物的基本生理过程,影响生物在不同环境中参与物质循环和能量转化的过程.代谢速率作为生物体重要的生命过程指标,几乎影响所有的生物活性速率,且在很多研究中均表现出异速生长现象.所谓代谢异速是指生物体代谢速率与其个体大小(或质量)之间存在的幂函数关系.代谢异速生长理论的提出,从机制模型角度解释了代谢异速关系这一普遍存在的生命现象.该理论利用分形几何学及流体动力学等原理,从生物能量学角度阐释了异速生长规律的机理,证实了3/4权度指数的存在;但同时有研究表明,权度指数因环境因素等影响处于2/3-1范围之间而非定值.随着研究工作的深入,代谢异速生长理论研究从起初的宏观动植物领域拓展到了微生物领域,在研究微生物的代谢异速生长理论时,可将微生物的可操作分类单元(Operational taxonomic unit,OTU)或具有特定功能的功能群视为一个微生物个体,基于其遗传多样性和功能多样性特征进行表征,以便于将微生物群落多样性与其生态功能性联系起来,使该理论在微生物生态学领域得到有效的补充和完善.尽管细菌具有独特的生物学特性,但与宏观生物系统中观测到的现象表现出明显的一致性.有研究表明,3个农田土壤细菌基于遗传多样性的OTU数的平均周转率分别为0.71、0.80和0.84,介于2/3与1之间,可能与生物代谢异速指数有一定关联,为微生物代谢异速指数的研究提出了一个参考解决方案.鉴于微生物个体特征和生物学特性,在分析代谢速率与个体大小关系中,从微生物单位个体的定义、个体大小表征到计量单位的统一,仍需更多的理论支持.分析了代谢异速生长理论在微生物与生态系统功能关系研究中的可能应用,延伸了该理论的应用范围,并对尚待加强的研究问题进行了评述和展望.     

The microbiology of biological phosphorus removal in activated sludge systems

Activated sludge systems are designed and operated globally to remove phosphorus microbiologically, a process called enhanced biological phosphorus removal (EBPR). Yet little is still known about the ecology of EBPR processes, the microbes involved, their functions there and the possible reasons why they often perform unreliably. The application of rRNA-based methods to analyze EBPR community structure has changed dramatically our understanding of the microbial populations responsible for EBPR, but many substantial gaps in our knowledge of the population dynamics of EBPR and its underlying mechanisms remain. This review critically examines what we once thought we knew about the microbial ecology of EBPR, what we think we now know, and what still needs to be elucidated before these processes can be operated and controlled more reliably than is currently possible. It looks at the history of EBPR, the currently available biochemical models, the structure of the microbial communities found in EBPR systems, possible identities of the bacteria responsible, and the evidence why these systems might operate suboptimally. The review stresses the need to extend what have been predominantly laboratory-based studies to full-scale operating plants. It aims to encourage microbiologists and process engineers to collaborate more closely and to bring an interdisciplinary approach to bear on this complex ecosystem.     

Synthesizing traditional biogeography with microbial ecology: the importance of dormancy

The discovery of biogeographical patterns among microbial communities has led to a focus on the empirical evaluation of the importance of dispersal limitation in microbial biota. As a result, the spatial distribution of microbial diversity has been increasingly studied while the synthesis of biogeographical theory with microbial ecology remains undeveloped. To make biogeographical theory relevant to microbial ecology, microbial traits that potentially affect the distribution of microbial diversity need to be considered. Given that many microorganisms in natural environments are in a state of dormancy and that dormancy is an important microbial fitness trait, I provide a first attempt to account for the effects of dormancy on microbial biogeography by treating dormancy as a fundamental biogeographical response. I discuss the effects of dormancy on the equilibrium theory of island biogeography and on the unified neutral theory of biodiversity and biogeography, and suggest how the equilibrium theory of island biogeography can produce predictions approaching those of the Baas‐Becking hypothesis (i.e. everything is everywhere, but the environment selects). In addition, I present a conceptual model of the unified neutral theory of biodiversity and biogeography, generalized to account for dormancy, from which a full model can be constructed for species with or without dormant life history stages.     

MAR-FISH技术及其在环境微生物群落与功能研究中的应用

对复杂环境中微生物群落结构和功能的研究是微生物生态学的重要任务。尽管现代分子生物学技术已经成功地用于解析环境中微生物的群落结构, 但是这些方法并不能提供微生物的原位生理学信息。而一种新的方法, 微观放射自显影和荧光原位杂交集成技术(MAR-FISH)则能够同时在单细胞水平上, 检测复杂环境中微生物的系统发育信息及其生理特性。本文总结了MAR-FISH方法的原理, 实验步骤及其在环境微生物群落与功能研究中的应用。     

Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors

MOTIVATION: Characterizing the diversity of microbial communities and understanding the environmental factors that influence community diversity are central tenets of microbial ecology. The development and application of cultivation independent molecular tools has allowed for rapid surveying of microbial community composition at unprecedented resolutions and frequencies. There is a growing need to discern robust patterns and relationships within these datasets which provide insight into microbial ecology. Pearson correlation coefficient (PCC) analysis is commonly used for identifying the linear relationship between two species, or species and environmental factors. However, this approach may not be able to capture more complex interactions which occur in situ; thus, alternative analyses were explored. RESULTS: In this paper we introduced local similarity analysis (LSA), which is a technique that can identify more complex dependence associations among species as well as associations between species and environmental factors without requiring significant data reduction. To illustrate its capability of identifying relationships that may not otherwise be identified by PCC, we first applied LSA to simulated data. We then applied LSA to a marine microbial observatory dataset and identified unique, significant associations that were not detected by PCC analysis. LSA results, combined with results from PCC analysis were used to construct a theoretical ecological network which allows for easy visualization of the most significant associations. Biological implications of the significant associations detected by LSA were discussed. We also identified additional applications where LSA would be beneficial. AVAILABILITY: The algorithms are implemented in Splus/R and they are available upon request from the corresponding author.     

rRNA技术及其在分子微生物生态上的应用

传统的基于微生物培养与纯种分离的技术所具有的局限性,以及分子生物学及其有关技术的长足进展,使微生物生态学的研究进入了分子的阶段.其中rRNA技术的建立、发展及其成功应用,为分子微生物生态和微生物系统分类学的研究掀开了崭新的一页.对rRNA分子技术的研究进展、以之为基础的主要方法及其在环境微生物研究中的应用,以及应用过程中所存在的一些潜在问题及其解决办法等作了详细综述.     

Bacteria—A link among ecosystem constituents

Microbial ecology has undergone a revolution over the past two decades due to the numerous innovations in techniques, allowing bacteria to be detected more accurately by direct means. Thus, bacterial life can be distinguishedin situ by direct counting under epifluorescence microscopy; automatically counting and sizing by image analyzer equipped with epifluorescence microscopy or by use of flow cytometry; specific radioisotope techniques; and molecular techniques to detect specific taxa. All of these approaches do not require cultivation, which provides a biased view of bacterial communities in nature. Bacteria are abundant in aquatic environments and play important roles as links between dissolved nutrients and the grazers in the food web. The new techniques allow an evaluation of bacterial population dynamics and function in relation to other organisms of higher trophic levels. This mini review aims to show briefly the state-of-the-art in microbial ecology for ecologists concerned with organisms other than microbes, in order to develop further intensive study together with microbial ecologists.     

荧光原位杂交技术及其在环境微生物生态学中的应用研究

荧光原位杂交技术是一种能够同时对微生物进行定性、定量和研究微生物群落空间分布情况的有力工具。简要介绍了荧光原位杂交技术的方法,并对其在人为创制环境和自然环境中特征性微生物种群及群落生态学中的应用研究进行了讨论,指出了该种技术在应用中存在的问题与缺陷,最后对荧光原位杂交技术在堆肥微生物生态中的应用及与其他方法的组合应用进行了展望。     

微生物生态研究中BIOLOG方法数据分析及R语言实现

微生物生态研究中,对微生物群落结构、群落特征以及其与环境因素的关系的揭示,一直受到广泛关注;适当的数据分析方法有助于更清晰地刻画微生物群落结构特征,明确其与环境因素的关系。结合实例,对微生物生态研究中基于BIOLOG微平板技术的数据分析方法进行梳理,分别介绍数据读取整理、特征指数计算、非限制性排序、限制性排序、聚类分析、环境向量拟合、蒙特尔检验等常用数据操作及生态分析方法;针对不同方法结论,结合研究目标和生态理论给出具有统计学意义的解释,并评价不同方法特点及适用场景;分析过程以R语言实现,并提供全部代码。结果表明,BIOLOG方法产生数据能从多个角度表征微生物群落功能特征,并结合环境指标梯度进行分析;但BIOLOG数据可能不满足正态性分布,在基于正态分布的分析前应提前进行检验;排序分析时应慎用主成分分析,可优先采用其他基于距离矩阵的排序方法;R语言能够简化BIOLOG数据读取及操作,易于完成各类统计分析。本研究能够对微生物生态研究者科学选择应用统计分析方法、提高数据处理效率提供直接参考。