Automated rRNA intergenic spacer analysis (ARISA) was used to characterise bacterial (B-ARISA) and fungal (F-ARISA) communities from different soil types. The 16S-23S intergenic spacer region from the bacterial rRNA operon was amplified from total soil community DNA for B-ARISA. Similarly, the two internal transcribed spacers and the 5.8S rRNA gene (ITS1-5.8S-ITS2) from the fungal rRNA operon were amplified from total soil community DNA for F-ARISA. Universal fluorescence-labeled primers were used for the PCRs, and fragments of between 200 and 1,200 bp were resolved on denaturing polyacrylamide gels by use of an automated sequencer with laser detection. Methodological (DNA extraction and PCR amplification) and biological (interand intrasite) variations were evaluated by comparing the number and intensity of peaks (bands) between electrophoregrams (profiles) and by multivariate analysis. Our results showed that ARISA is a high-resolution, highly reproducible technique and is a robust method for discriminating between microbial communities. To evaluate the potential biases in community description provided by ARISA, we also examined databases on length distribution of ribosomal intergenic spacers among bacteria (L. Ranjard, E. Brothier, and S. Nazaret, Appl. Environ. Microbiol. 66:5334-5339, 2000) and fungi.

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Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. However, a landscape perspective is needed to understand the relative importance of local and regional factors and land management for the microbial communities and the ecosystem services they provide. In the most comprehensive analysis of spatial patterns of microbial communities to date, we investigated the distribution of functional microbial communities involved in N-cycling and of the total bacterial and crenarchaeal communities over 107 sites in Burgundy, a 31 500 km 2 region of France, using a 16 Â 16 km 2 sampling grid. At each sampling site, the abundance of total bacteria, crenarchaea, nitrate reducers, denitrifiers-and ammonia oxidizers were estimated by quantitative PCR and 42 soil physicochemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time, and soil physico-chemical properties to the spatial distribution of the different communities were analyzed by canonical variation partitioning. Our results indicate that 43-85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of microbial communities at the landscape scale. The present study highlights the potential of a spatially explicit approach for microbial ecology to identify the overarching factors driving the spatial heterogeneity of microbial communities even at the landscape scale.

Spatial scaling and determinism of the wide-scale distribution of macroorganism diversity has been largely demonstrated over a century. For microorganisms, and especially for soil bacteria, this fundamental question requires more thorough investigation, as little information has been reported to date. Here by applying the taxa-area relationship to the largest spatially explicit soil sampling available in France (2,085 soils, area covered B5.3 Â 10 5 km 2 ) and developing an innovative evaluation of the habitat-area relationship, we show that the turnover rate of bacterial diversity in soils on a wide scale is highly significant and strongly correlated with the turnover rate of soil habitat. As the diversity of micro-and macroorganisms appears to be driven by similar processes (dispersal and selection), maintaining diverse and spatially structured habitats is essential for soil biological patrimony and the resulting ecosystem services.

The similarities and differences in the structures of the nifH gene pools of six different soils (Montrond, LCSA-p, Vernon, Dombes, LCSA-c, and Thysse Kaymor) and five soil fractions extracted from LCSA-c were studied. Bacterial DNA was directly extracted from the soils, and a region of the nifH gene was amplified by PCR and analyzed by restriction. Soils were selected on the basis of differences in soil management, plant cover, and major physicochemical properties. Microenvironments differed on the basis of the sizes of the constituent particles and the organic carbon and clay contents. Restriction profiles were subjected to principalcomponent analysis. We showed that the composition of the diazotrophic communities varied both on a large scale (among soils) and on a microscale (among microenvironments in LCSA-c soil). Soil management seemed to be the major parameter influencing differences in the nifH gene pool structure among soils by controlling inorganic nitrogen content and its variation. However, physicochemical parameters (texture and total C and N contents) were found to correlate with differences among nifH gene pools on a microscale. We hypothesize that the observed nifH genetic structures resulted from the adaptation to fluctuating conditions (cultivated soil, forest soil, coarse fractions) or constant conditions (permanent pasture soil, fine fractions). We attempted to identify a specific band within the profile of the clay fraction by cloning and sequencing it and comparing it with the gene databases. Unexpectedly, the nifH sequences of the dominant bacteria were most similar to sequences of unidentified marine eubacteria.

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Aim The spatial organization of soil microbial communities on large scales and the identification of environmental factors structuring their distribution have been little investigated. The overall objective of this study was to determine the spatial patterning of microbial biomass in soils over a wide extent and to rank the environmental filters most influencing this distribution.Location French territory using the French Soil Quality Monitoring Network. This network covers the entire French territory and soils were sampled at 2150 sites along a systematic grid. MethodsThe soil DNA extracted from all these soils was expressed in terms of soil molecular microbial biomass and related to other soil and land-use data over French territory. ResultsThis study provides the first extensive map of microbial biomass and reveals the heterogeneous and spatially structured distribution of this biomass on the scale of France. The main factors driving biomass distribution are the physicochemical properties of the soil (texture, pH and total organic carbon) as well as land use. Soils from land used for intensive agriculture, especially monoculture and vineyards, exhibited the smallest biomass pools. Interestingly, factors known to influence the large-scale distribution of macroorganisms, such as climatic factors, were not identified as important drivers for microbial communities.Main conclusions Microbial abundance is spatially structured and dependent on local filters such as soil characteristics and land use but is relatively independent of global filters such as climatic factors or the presence of natural barriers. Our study confirms that the biogeography of microorganisms differs fundamentally from the biogeography of 'macroorganisms' and that soil management can have significant large-scale effects.

Assessing soil microbial community structure by the use of molecular techniques requires a satisfactory sampling strategy that takes into account the high microbial diversity and the heterogeneous distribution of microorganisms in the soil matrix. The influence of the sample size of three different soil types (sand, silt and clay soils) on the DNA yield and analysis of bacterial and fungal community structure were investigated. Six sample sizes from 0.125 g to 4 g were evaluated. The genetic community structure was assessed by automated ribosomal intergenic spacer analysis (A-RISA fingerprint). Variations between bacterial (B-ARISA) and fungal (F-ARISA) community structure were quantified by using principal component analysis (PCA). DNA yields were positively correlated with the sample size for the sandy and silty soils, suggesting an influence of the sample size on DNA recovery, whereas no correlation was observed in the clay soil. B-ARISA was shown to be consistent between the different sample sizes for each soil type indicating that the sampling procedure has no influence on the assessment of bacterial community structure. On the contrary for F-ARISA profiles, strong variations were observed between replicates of the smaller samples (<1 g). Principal component analysis analysis revealed that sampling aliquots of soil > or =1 g are required to obtain robust and reproducible fingerprinting analysis of the genetic structure of fungal communities. However, the smallest samples could be adequate for the detection of minor populations masked by dominant ones in larger samples. The sampling strategy should therefore be different according to the objectives: rather large soil samples (> or =1 g) for a global description of the genetic community structure, or a large number of small soil samples for a more complete inventory of microbial diversity.