Spatial scaling of microbial biodiversity

A central goal in ecology is to understand the spatial scaling of biodiversity. Patterns in the spatial distribution of organisms provide important clues about the underlying mechanisms that structure ecological communities and are central to setting conservation priorities. Although microorganisms comprise much of Earth's biodiversity, little is known about their biodiversity scaling relationships relative to that for plants and animals. Here, we discuss current knowledge of microbial diversity at local and global scales. We focus on three spatial patterns: the distance-decay relationship (how community composition changes with geographic distance), the taxa-area relationship, and the local:global taxa richness ratio. Recent empirical analyses of these patterns for microorganisms suggest that there are biodiversity scaling rules common to all forms of life.     

Scaling properties of multivariate landscape structure

Analyses of landscape context is essential for understanding how ecological patterns and processes relate to space. This requires that we quantify variation patterns of different landscape parameters, which may change relative to one another at different spatial scales. Here, we analyzed how statistical relationships of land-use composition parameters changed as a function of extent in 20 real agricultural landscapes. Furthermore, we tested the generality of these scaling relations in numerical simulations using 300 artificial landscapes. We analyzed proportions of artificial habitat types at different extent and compared these patterns with three dominant habitat types in real landscapes (forest, arable land and grassland) at four spatial scales (quadrates of 1–4 km). Both real and simulated landscapes showed that variance of landscape parameters (data differentiation) decreased and their correlations (data consistency) increased as scale increased, thereby suggesting general scaling laws. The potential statistical impact of these scaling relationships is revealed in simultaneous analyses of variation of (local) site parameters of 20 arable fields and their surrounding landscape context. At small and medium extent (quadrates of 1–3 km), variability of local site parameters (e.g. fertilization, pH-value) was high relative to those of landscape parameters. In contrast, at large extent (quadrates of 4 km) variability of landscape parameters was greater than that of site parameters indicating a fundamental shift in the relationship between these sets of parameters at different scales. Hence, it is clear that there is a high risk of artefactual correlations in hierarchical multi-scale landscape analyses when ecological data are related to the landscape context. Accordingly, there is a necessity for multi-scale analyses in landscape ecology to accurately evaluate the relative importance of landscape context at different spatial scales.     

Statistical mechanics unifies different ecological patterns

Recently there has been growing interest in the use of maximum relative entropy (MaxREnt) as a tool for statistical inference in ecology. In contrast, here we propose MaxREnt as a tool for applying statistical mechanics to ecology. We use MaxREnt to explain and predict species abundance patterns in ecological communities in terms of the most probable behaviour under given environmental constraints, in the same way that statistical mechanics explains and predicts the behaviour of thermodynamic systems. We show that MaxREnt unifies a number of different ecological patterns: (i) at relatively local scales a unimodal biodiversity-productivity relationship is predicted in good agreement with published data on grassland communities, (ii) the predicted relative frequency of rare vs. abundant species is very similar to the empirical lognormal distribution, (iii) both neutral and non-neutral species abundance patterns are explained, (iv) on larger scales a monotonic biodiversity-productivity relationship is predicted in agreement with the species-energy law, (v) energetic equivalence and power law self-thinning behaviour are predicted in resource-rich communities. We identify mathematical similarities between these ecological patterns and the behaviour of thermodynamic systems, and conclude that the explanation of ecological patterns is not unique to ecology but rather reflects the generic statistical behaviour of complex systems with many degrees of freedom under very general types of environmental constraints.     

生态学研究中的尺度问题

尺度是生态学中的核心问题,时间和空间尺度包含于任何生态过程中,尺度在生态学研究中越来越显现出其重要性,原因是解决地球环境问题要求在大尺度上理解格局和过程,而以前生态学调查的数据主要是基于小尺度的,并且有许多研究表明,一个生态问题的结论在很大程度上取决于研究所采纳的尺度。但目前尺度研究还存在很多的问题;一些相关概念易与尺度混淆;缺乏成熟的尺度识别方法和系统完备的尺度转换方法等。针对这些问题,本文首先阐明了尺度的确切含义,并对尺度研究的发展历史,重要性作了问题的阐述：分析探讨了尺度与等级理论,格局的关系,及尺度识别,尺度转换的方法和发展趋势。     

The Temporal Scaling of Bacterioplankton Composition: High Turnover and Predictability during Shrimp Cultivation

The spatial distribution of microbial communities has recently been reliably documented in the form of a distance–similarity decay relationship. In contrast, temporal scaling, the pattern defined by the microbial similarity–time relationships (STRs), has received far less attention. As a result, it is unclear whether the spatial and temporal variations of microbial communities share a similar power law. In this study, we applied the 454 pyrosequencing technique to investigate temporal scaling in patterns of bacterioplankton community dynamics during the process of shrimp culture. Our results showed that the similarities decreased significantly (P?=?0.002) with time during the period over which the bacterioplankton community was monitored, with a scaling exponent of w?=?0.400. However, the diversities did not change dramatically. The community dynamics followed a gradual process of succession relative to the parent communities, with greater similarities between samples from consecutive sampling points. In particular, the variations of the bacterial communities from different ponds shared similar successional trajectories, suggesting that bacterial temporal dynamics are predictable to a certain extent. Changes in bacterial community structure were significantly correlated with the combination of Chl a, TN, PO₄ ^3-, and the C/N ratio. In this study, we identified predictable patterns in the temporal dynamics of bacterioplankton community structure, demonstrating that the STR of the bacterial community mirrors the spatial distance–similarity decay model.     

In search of microbial indicator taxa: shifts in stream bacterial communities along an urbanization gradient

A majority of environmental studies describe microbiomes at coarse scales of taxonomic resolution (bacterial community, phylum), ignoring key ecological knowledge gained from finer-scales and microbial indicator taxa. Here, we characterized the distribution of 940 bacterial taxa from 41 streams along an urbanization gradient (0%–83% developed watershed area) in the Raleigh-Durham area of North Carolina (USA). Using statistical approaches derived from macro-organismal ecology, we found that more bacterial taxa were classified as intolerant than as tolerant to increasing watershed urbanization (143 vs 48 OTUs), and we identified a threshold of 12.1% developed watershed area beyond which the majority of intolerant taxa were lost from streams. Two bacterial families strongly decreased with urbanization: Acidobacteriaceae (Acidobacteria) and Xanthobacteraceae (Alphaproteobacteria). Tolerant taxa were broadly distributed throughout the bacterial phylogeny, with members of the Comamonadaceae family (Betaproteobacteria) presenting the highest number of tolerant taxa. Shifts in microbial community structure were strongly correlated with a stream biotic index, based on macroinvertebrate composition, suggesting that microbial assemblages could be used to establish biotic criteria for monitoring aquatic ecosystems. In addition, our study shows that classic methods in community ecology can be applied to microbiome datasets to identify reliable microbial indicator taxa and determine the environmental constraints on individual taxa distributions along environmental gradients.     

Spatial and temporal heterogeneity of the bacterial communities in stream epilithic biofilms

The spatial and temporal variability in bacterial communities within freshwater systems is poorly understood. The bacterial composition of stream epilithic biofilms across a range of different spatial and temporal scales both within and between streams and across the profile of individual stream rocks was characterised using a community DNA-fingerprinting technique (Automated Ribosomal Intergenic Spacer Analysis, ARISA). The differences in bacterial community structure between two different streams were found to be greater than the spatial variability within each stream site, and were larger than the weekly temporal variation measured over a 10-week study period. Greater variations in bacterial community profiles were detected on different faces of individual stream rocks than between whole rocks sampled within a 9-m stream section. Stream temperature was found to be the most important determinant of bacterial community variability using distance-based redundancy analysis (dbRDA) of ARISA data, which may have broad implications for riparian zone management and ecological change as a consequence of global warming. The combination of ARISA with multivariate statistical methods and ordination, such as multidimensional scaling (MDS), permutational manova and RDA, provided rapid and effective methods for quantifying and visualising variation in bacterial community structure, and to identify potential drivers of ecological change.     

Statistical analysis of denaturing gel electrophoresis (DGE) fingerprinting patterns

Technical developments in molecular biology have found extensive applications in the field of microbial ecology. Among these techniques, fingerprinting methods such as denaturing gel electrophoresis (DGE, including the three options: DGGE, TGGE and TTGE) has been applied to environmental samples over this last decade. Microbial ecologists took advantage of this technique, originally developed for the detection of single mutations, for the analysis of whole bacterial communities. However, until recently, the results of these high quality fingerprinting patterns were restricted to a visual interpretation, neglecting the analytical potential of the method in terms of statistical significance and ecological interpretation. A brief recall is presented here about the principles and limitations of DGE fingerprinting analysis, with an emphasis on the need of standardization of the whole analytical process. The main content focuses on statistical strategies for analysing the gel patterns, from single band examination to the analysis of whole fingerprinting profiles. Applying statistical method make the DGE fingerprinting technique a promising tool. Numerous samples can be analysed simultaneously, permitting the monitoring of microbial communities or simply bacterial groups for which occurrence and relative frequency are affected by any environmental parameter. As previously applied in the fields of plant and animal ecology, the use of statistics provides a significant advantage for the non-ambiguous interpretation of the spatial and temporal functioning of microbial communities.     

How to allow SAR collapse across local and continental scales: a resolution of the controversy between Storch et al. (2012) and Lazarina et al. (2013)

Up‐scaling species richness from local to continental scales is an unsolved problem of macroecology. Macroecologists hope that proper up‐scaling can uncover the hidden rules that underlie spatial patterns in species richness, but a machinery to up‐scale species richness also has a purely practical side at the scales and for the habitats where direct observations cannot be performed. The species–area relationship (SAR) could provide a tool for up‐scaling, but no valid method has yet been put forward. Such a method would have resulted from Storch et al.'s (2012) suggestion that there is a universal curve to which each rescaled SAR collapses, if Lazarina et al. (2013) had not shown that it does not: both arguments were supported by data analyses. Here we present an analytical model for mainland SAR and argue in favour of the latter authors. We identify 1) the variation in mean species‐range size, 2) the variation in forces that drive SAR at various scales, and 3) the finite‐area effect, as the reasons for the absence of collapse. Finally, we suggest a rescaling that might fix the problem. We conclude, however, that ecologists are still far from finding a practical, robust and easy‐to‐use solution for up‐scaling species richness from SARs.     

Power laws and plant trait variation in spatio-temporally heterogeneous environments

A challenge

Variation is ubiquitous in nature across all spatial and temporal scales and underlies prominent ecological and evolutionary theories. Although understanding the causes and consequences of trait variation is a central goal of trait-based ecology, the scaling of trait variance across space and time (variance scaling) is unresolved.

A solution

We argue that characterizing trait variance across spatio-temporal scales using a combination of prominent power laws can elucidate the role of environmental variability in trait variation and potential mechanisms driving trait patterns. In particular, the species–time–area relationship and Taylor's power law help to establish a generalizable framework for developing and testing variance scaling theory. Finally, we outline priority research questions and tractable systems for answering them. Successional forests, long-term forest monitoring networks and censuses of short-lived taxa are ideal for coupling high-resolution environmental data with measurements of trait variance across scales to test the models proposed here.

Main conclusions

Characterizing the behaviour of variance across spatio-temporal scales is feasible and a prerequisite for developing a predictive theory of trait-based ecology.     

Spatial scaling of arbuscular mycorrhizal fungal diversity is affected by farming practice

Evidence suggests that microbial communities show patterns of spatial scaling which can be driven by geographical distance and environmental heterogeneity. Here we demonstrate that human management can have a major impact on microbial distribution patterns at both the local and landscape scale. Mycorrhizal fungi are vital components of terrestrial ecosystems, forming a mutualistic symbiosis with plant roots which has a major impact on above ground ecology and productivity. We used contrasting agricultural systems to investigate the spatial scaling of the most widespread mycorrhizal fungus group, the arbuscular mycorrhizal fungi (AMF). Using multiple sampling sites with a maximum separation of 250 km we describe for the first time the roles which land management, environmental heterogeneity and geographical distance play in determining spatial patterns of microbial distribution. Analysis of AMF taxa–area relationships at each sampling site revealed that AMF diversity and spatial turnover was greater under organic relative to conventional farm management. At the regional scale (250 km) distance–decay analyses showed that there was significant change in AMF community composition with distance, and that this was greater under organic relative to conventional management. Environmental heterogeneity was found to be the major factor determining turnover of AMF taxa at the landscape scale. Overall we demonstrate that human management can play a key role in determining the turnover of microbial communities at both the local and regional scales.     

土壤微生物群落构建理论与时空演变特征

土壤微生物作为陆地生态系统的重要组成部分,直接或间接地参与几乎所有的土壤生态过程,在物质循环、能量转换以及污染物降解等过程中都发挥着重要作用。对土壤微生物时空演变规律及其形成机制的研究,不仅是微生物演变和进化的基础科学问题,也是预测微生物及其所介导的生态功能对环境条件变化响应、适应和反馈的理论依据。讨论了土壤微生物群落的定义、测度方法和指标,认为群落是联系动植物宏观生态学与微生物生态学的基础,群落构建机制是宏观和微观生态学都需要研究的核心科学问题;从生态学的群落构建理论出发,阐述了包括生态位理论/中性理论、过程理论和多样性-稳定性理论在土壤微生物时空演变研究中的应用,以及微生物群落在时间和空间上的分布特征及其尺度效应;确立了以微生物群落构建理论为基础、不同时空尺度下土壤微生物群落演变特征为主要内容的微生物演变研究的基本框架。     

Integrating microbial ecology in bioprocess understanding: the case of gas biofiltration

Biofilters are packed-bed bioreactors where contaminants, once transferred from the gas phase to the biofilm, are oxidized by diverse and complex communities of attached microorganisms. Over the last decade, more and more studies aimed at opening the back box of biofiltration by unraveling the biodiversity-ecosystem function relationship. In this review, we report the insights provided by the microbial ecology approach in biofilters and we emphasize the parallels existing with other engineered ecosystems used for wastewater treatment, as they all constitute relevant model ecosystems to explore ecological issues. We considered three characteristic ecological indicators: the density, the diversity, and the structure of the microbial community. Special attention was paid to the temporal and spatial dynamics of each indicator, insofar as it can disclose the potential relationship, or absence of relation, with any operating or functional parameter. We also focused on the impact of disturbance regime on the microbial community structure, in terms of resistance, resilience, and memory. This literature review led to mitigated conclusions in terms of biodiversity–ecosystem function relationship. Depending on the environmental system itself and the way it is investigated, the spatial and temporal dynamics of the microbial community can be either correlated (e.g., spatial stratification) or uncoupled (e.g., temporal instability) to the ecosystem function. This lack of generality shows the limits of current 16S approach in complex ecosystems, where a functional approach may be more suitable.     

The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China

Bacteria play key roles in the ecology of both aquatic and terrestrial ecosystems; however, little is known about their diversity and biogeography, especially in the rare microbial biosphere of inland freshwater ecosystems. Here we investigated aspects of the community ecology and geographical distribution of abundant and rare bacterioplankton using high-throughput sequencing and examined the relative influence of local environmental variables and regional (spatial) factors on their geographical distribution patterns in 42 lakes and reservoirs across China. Our results showed that the geographical patterns of abundant and rare bacterial subcommunities were generally similar, and both of them showed a significant distance–decay relationship. This suggests that the rare bacterial biosphere is not a random assembly, as some authors have assumed, and that its distribution is most likely subject to the same ecological processes that control abundant taxa. However, we identified some differences between the abundant and rare groups as both groups of bacteria showed a significant positive relationship between sites occupancy and abundance, but the abundant bacteria exhibited a weaker distance–decay relationship than the rare bacteria. Our results implied that rare subcommunities were mostly governed by local environmental variables, whereas the abundant subcommunities were mainly affected by regional factors. In addition, both local and regional variables that were significantly related to the spatial variation of abundant bacterial community composition were different to those of rare ones, suggesting that abundant and rare bacteria may have discrepant ecological niches and may play different roles in natural ecosystems.     

生态学中的尺度问题：内涵与分析方法

尺度问题已成为现代生态学的核心问题之一.尺度问题主要涉及3个方面：尺度概念、尺度分析和尺度推绎.主要评述前两个方面.生态学尺度有三重概念：维数、种类和组分,其中每重概念又包含了多个定义,有必要进行澄清、分类和统一.尺度分析涉及尺度效应分析和多尺度空间格局分析.格局、过程及它们之间的关系,以及某些景观特性均表现出尺度效应,因此多尺度研究非常必要和重要.多尺度空间格局分析（尤其是特征尺度的识别）是进行尺度效应分析和跨尺度推绎的基础.多尺度分析需要特定的方法,景观指数法是最常用和最简单的方法,但也常产生误导;空间统计学方法（如半方差分析法、尺度方差分析法、空隙度指数法和小波分析法等）和分维分析法在最近十几年发展起来,并逐渐应用于生态学,在尺度分析上具有很大的应用潜力.各种方法在尺度分析上各有优势和不足,有必要同时使用两种或两种以上方法进行比较和评估.总之,有关尺度分析的研究需要进一步加强,从而为下一步的尺度推绎提供可靠的依据.     

Historical foundations and future directions in macrosystems ecology

Macrosystems ecology is an effort to understand ecological processes and interactions at the broadest spatial scales and has potential to help solve globally important social and ecological challenges. It is important to understand the intellectual legacies underpinning macrosystems ecology: How the subdiscipline fits within, builds upon, differs from and extends previous theories. We trace the rise of macrosystems ecology with respect to preceding theories and present a new hypothesis that integrates the multiple components of macrosystems theory. The spatio‐temporal anthropogenic rescaling (STAR) hypothesis suggests that human activities are altering the scales of ecological processes, resulting in interactions at novel space–time scale combinations that are diverse and predictable. We articulate four predictions about how human actions are “expanding”, “shrinking”, “speeding up” and “slowing down” ecological processes and interactions, and thereby generating new scaling relationships for ecological patterns and processes. We provide examples of these rescaling processes and describe ecological consequences across terrestrial, freshwater and marine ecosystems. Rescaling depends in part on characteristics including connectivity, stability and heterogeneity. Our STAR hypothesis challenges traditional assumptions about how the spatial and temporal scales of processes and interactions operate in different types of ecosystems and provides a lens through which to understand macrosystem‐scale environmental change.     

Human impacts on the species-area relationship in reef fish assemblages

The relationship between species richness and area is one of the oldest, most recognized patterns in ecology. Here we provide empirical evidence for strong impacts of fisheries exploitation on the slope of the species–area relationship (SAR). Using comparative field surveys of fish on protected and exploited reefs in three oceans and the Mediterranean Sea, we show that exploitation consistently depresses the slope of the SAR for both power-law and exponential models. The magnitude of change appears to be proportional to fishing intensity. Results are independent of taxonomic resolution and robust across coral and rocky reefs, sampling protocols and statistical methods. Changes in species richness, relative abundance and patch occupancy all appear to contribute to this pattern. We conclude that exploitation pressure impacts the fundamental scaling of biodiversity as well as the species richness and spatial distribution patterns of reef fish. We propose that species–area curves can be sensitive indicators of community-level changes in biodiversity, and may be useful in quantifying the human imprint on reef biodiversity, and potentially elsewhere.     

基于模型的景观格局与生态过程研究

景观格局与生态过程关系的研究是景观生态学的主要特色和理论核心之一。模型可以充分利用实验和观测数据并综合不同时间和空间尺度上的信息提炼规律或揭示内在机制,模拟景观格局与生态过程的动态与相互关系,成为景观生态学研究的有力工具。结合研究实例,总结了基于模型的景观格局研究、生态过程研究和格局-过程关系研究的发展现状和薄弱环节,同时探讨了通过构建耦合模型研究格局-过程相互关系的途径。总结了景观模型研究亟待发展的领域与发展趋势。     

Environmental and biotic drivers of soil microbial β‐diversity across spatial and phylogenetic scales

Soil microbial communities play a key role in ecosystem functioning but still little is known about the processes that determine their turnover (β‐diversity) along ecological gradients. Here, we characterize soil microbial β‐diversity at two spatial scales and at multiple phylogenetic grains to ask how archaeal, bacterial and fungal communities are shaped by abiotic processes and biotic interactions with plants. We characterized microbial and plant communities using DNA metabarcoding of soil samples distributed across and within eighteen plots along an elevation gradient in the French Alps. The recovered taxa were placed onto phylogenies to estimate microbial and plant β‐diversity at different phylogenetic grains (i.e. resolution). We then modeled microbial β‐diversities with respect to plant β‐diversities and environmental dissimilarities across plots (landscape scale) and with respect to plant β‐diversities and spatial distances within plots (plot scale). At the landscape scale, fungal and archaeal β‐diversities were mostly related to plant β‐diversity, while bacterial β‐diversities were mostly related to environmental dissimilarities. At the plot scale, we detected a modest covariation of bacterial and fungal β‐diversities with plant β‐diversity; as well as a distance–decay relationship that suggested the influence of ecological drift on microbial communities. In addition, the covariation between fungal and plant β‐diversity at the plot scale was highest at fine or intermediate phylogenetic grains hinting that biotic interactions between those clades depends on early‐evolved traits. Altogether, we show how multiple ecological processes determine soil microbial community assembly at different spatial scales and how the strength of these processes change among microbial clades. In addition, we emphasized the imprint of microbial and plant evolutionary history on today's microbial community structure.     

Modelling spatial patterns in host-associated microbial communities

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.