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      Spatial Ecology of Bacteria at the Microscale in Soil

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      PLoS ONE
      Public Library of Science

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          Abstract

          Despite an exceptional number of bacterial cells and species in soils, bacterial diversity seems to have little effect on soil processes, such as respiration or nitrification, that can be affected by interactions between bacterial cells. The aim of this study is to understand how bacterial cells are distributed in soil to better understand the scaling between cell-to-cell interactions and what can be measured in a few milligrams, or more, of soil. Based on the analysis of 744 images of observed bacterial distributions in soil thin sections taken at different depths, we found that the inter-cell distance was, on average 12.46 µm and that these inter-cell distances were shorter near the soil surface (10.38 µm) than at depth (>18 µm), due to changes in cell densities. These images were also used to develop a spatial statistical model, based on Log Gaussian Cox Processes, to analyse the 2D distribution of cells and construct realistic 3D bacterial distributions. Our analyses suggest that despite the very high number of cells and species in soil, bacteria only interact with a few other individuals. For example, at bacterial densities commonly found in bulk soil (10 8 cells g −1 soil), the number of neighbours a single bacterium has within an interaction distance of ca. 20 µm is relatively limited (120 cells on average). Making conservative assumptions about the distribution of species, we show that such neighbourhoods contain less than 100 species. This value did not change appreciably as a function of the overall diversity in soil, suggesting that the diversity of soil bacterial communities may be species-saturated. All in all, this work provides precise data on bacterial distributions, a novel way to model them at the micrometer scale as well as some new insights on the degree of interactions between individual bacterial cells in soils.

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          Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients

          Terrestrial ecosystems are receiving elevated inputs of nitrogen (N) from anthropogenic sources and understanding how these increases in N availability affect soil microbial communities is critical for predicting the associated effects on belowground ecosystems. We used a suite of approaches to analyze the structure and functional characteristics of soil microbial communities from replicated plots in two long-term N fertilization experiments located in contrasting systems. Pyrosequencing-based analyses of 16S rRNA genes revealed no significant effects of N fertilization on bacterial diversity, but significant effects on community composition at both sites; copiotrophic taxa (including members of the Proteobacteria and Bacteroidetes phyla) typically increased in relative abundance in the high N plots, with oligotrophic taxa (mainly Acidobacteria) exhibiting the opposite pattern. Consistent with the phylogenetic shifts under N fertilization, shotgun metagenomic sequencing revealed increases in the relative abundances of genes associated with DNA/RNA replication, electron transport and protein metabolism, increases that could be resolved even with the shallow shotgun metagenomic sequencing conducted here (average of 75 000 reads per sample). We also observed shifts in the catabolic capabilities of the communities across the N gradients that were significantly correlated with the phylogenetic and metagenomic responses, indicating possible linkages between the structure and functioning of soil microbial communities. Overall, our results suggest that N fertilization may, directly or indirectly, induce a shift in the predominant microbial life-history strategies, favoring a more active, copiotrophic microbial community, a pattern that parallels the often observed replacement of K-selected with r-selected plant species with elevated N.
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            Pyrosequencing enumerates and contrasts soil microbial diversity.

            Estimates of the number of species of bacteria per gram of soil vary between 2000 and 8.3 million (Gans et al., 2005; Schloss and Handelsman, 2006). The highest estimate suggests that the number may be so large as to be impractical to test by amplification and sequencing of the highly conserved 16S rRNA gene from soil DNA (Gans et al., 2005). Here we present the use of high throughput DNA pyrosequencing and statistical inference to assess bacterial diversity in four soils across a large transect of the western hemisphere. The number of bacterial 16S rRNA sequences obtained from each site varied from 26,140 to 53,533. The most abundant bacterial groups in all four soils were the Bacteroidetes, Betaproteobacteria and Alphaproteobacteria. Using three estimators of diversity, the maximum number of unique sequences (operational taxonomic units roughly corresponding to the species level) never exceeded 52,000 in these soils at the lowest level of dissimilarity. Furthermore, the bacterial diversity of the forest soil was phylum rich compared to the agricultural soils, which are species rich but phylum poor. The forest site also showed far less diversity of the Archaea with only 0.009% of all sequences from that site being from this group as opposed to 4%-12% of the sequences from the three agricultural sites. This work is the most comprehensive examination to date of bacterial diversity in soil and suggests that agricultural management of soil may significantly influence the diversity of bacteria and archaea.
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              Using ecological diversity measures with bacterial communities.

              Abstract There are many ecological diversity measures, but their suitability for use with highly diverse bacterial communities is unclear and seldom considered. We assessed a range of species richness and evenness/dominance indices, and the use of species abundance models using samples of bacteria from zinc-contaminated and control soils. Bacteria were assigned to operational taxonomic units (OTUs) using amplified ribosomal DNA restriction analysis of 236 clones from each soil. The reduced diversity apparent in the contaminated soil was reflected by the diversity indices to varying degrees. The number of clones analysed and the weighting given to rare vs. abundant OTUs are the most important considerations when selecting measures. Our preferences, arrived at using theory and practical experience, include: the log series index alpha; the Q statistic (but only if coverage is 50% or more); the Berger-Parker and Simpson's indices, although their ecological relevance may be limited; and, unexpectedly, the Shannon-Wiener and Shannon evenness indices, even though their meanings may not be clear and their values inaccurate when coverage is low. For extrapolation, the equation for the log series distribution seems the best for extrapolating from OTU accumulation curves while non-parametric methods, such as Chao 1, show promise for estimating total OTU richness. Due to a preponderance of single-occurrence OTUs, none of the five species abundance models fit the OTU abundance distribution of the control soil, but both the log and log normal models fit the less diverse contaminated soil. Species abundance models are useful, irrespective of coverage, because they address the whole distribution of a sample, aiding comparison by revealing overall trends as well as specific changes in particular abundance classes.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                28 January 2014
                : 9
                : 1
                : e87217
                Affiliations
                [1 ]Sorbonne Universités, UPMC Univ Paris 06, Institute of Ecology and Environmental Sciences – Paris, Paris, France
                [2 ]CNRS, Institute of Ecology and Environmental Sciences – Paris, Campus AgroParisTech, Thiverval-Grignon, France
                University of Catania, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: XR NN. Performed the experiments: XR. Analyzed the data: XR NN. Contributed reagents/materials/analysis tools: NN. Wrote the paper: XR NN.

                Article
                PONE-D-13-35688
                10.1371/journal.pone.0087217
                3905020
                24489873
                99a06634-7ebb-409a-ad56-4943d4bae478
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 August 2013
                : 26 December 2013
                Page count
                Pages: 9
                Funding
                This research was supported by the INSU EC2CO Microbiologie Environnementale and ANR Syscomm Programme MEPSOM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Agriculture
                Soil Science
                Biology
                Ecology
                Biodiversity
                Microbial Ecology
                Soil Ecology
                Spatial and Landscape Ecology
                Microbiology
                Bacteriology
                Microbial Ecology
                Earth Sciences
                Environmental Sciences
                Mathematics
                Statistics
                Statistical Methods

                Uncategorized
                Uncategorized

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