No abstract

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.

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.

The results of this study provide the first baseline for predicting Db from soil properties for soils across the Amazon basin. Bulk density values are needed to convert nutrient content and organic carbon (OC) content to weight of nutrient and OC per unit area; unfortunately, common field methods to measure Db are limited with regard to reliable, complete, and uniform soil data. Much effort has been made in finding alternative solutions to predict Db from soil properties. We hypothesized that Db could be reliably estimated by multiple regression of OC, soil textural properties, and some chemical properties. Using the data of 323 soil horizons from the Brazilian Amazon basin, a stepwise multiple regression (SMR) procedure was developed to predict Db from other soil properties. Multiple regression relationships were obtained for all the data, which were also partitioned by layer and then by main soil order: Latossolos (Oxisols, 62 horizons) and Podzólicos (Alfisols and Ultisols, 212 horizons). The SMR on all the data showed that clay content is the best predictor of Db, accounting for 37% of the variation. Adding OC content increased the explained variance up to nearly 50%. Predictions of the models were improved when the data were partitioned by order and by horizon type. In the case of Latossolos (Oxisols), the use of OC and clay content as predictors increased the percentage of explained variation, reaching 71% using all layers and 79% for A horizons. The results of this study will provide a basis for estimating OC stocks in the Amazon basin.