Nicolas Saby

Mapping field‐scale spatial patterns of size and activity of the denitrifier community

There is ample evidence that microbial processes can exhibit large variations in activity on a field scale. However, very little is known about the spatial distribution of the microbial communities mediating these processes. Here we used geostatistical modelling to explore spatial patterns of size and activity of the denitrifying community, a functional guild involved in N-cycling, in a grassland field subjected to different cattle grazing regimes. We observed a non-random distribution pattern of the size of the denitrifier community estimated by quantification of the denitrification genes copy numbers with a macro-scale spatial dependence (6-16 m) and mapped the distribution of this functional guild in the field. The spatial patterns of soil properties, which were strongly affected by presence of cattle, imposed significant control on potential denitrification activity, potential N(2)O production and relative abundance of some denitrification genes but not on the size of the denitrifier community. Absolute abundance of most denitrification genes was not correlated with the distribution patterns of potential denitrification activity or potential N(2)O production. However, the relative abundance of bacteria possessing the nosZ gene encoding the N(2)O reductase in the total bacterial community was a strong predictor of the N(2)O/(N(2) + N(2)O) ratio, which provides evidence for a relationship between bacterial community composition based on the relative abundance of denitrifiers in the total bacterial community and ecosystem processes. More generally, the presented geostatistical approach allows integrated mapping of microbial communities, and hence can facilitate our understanding of relationships between the ecology of microbial communities and microbial processes along environmental gradients.

Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale

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 km2 region of France, using a 16 × 16 km2 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 physico-chemical 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 patterns of bacterial taxa in nature reflect ecological traits of deep branches of the 16S rRNA bacterial tree

Whether bacteria display spatial patterns of distribution and at which level of taxonomic organization such patterns can be observed are central questions in microbial ecology. Here we investigated how the total and relative abundances of eight bacterial taxa at the phylum or class level were spatially distributed in a pasture by using quantitative PCR and geostatistical modelling. The distributions of the relative abundance of most taxa varied by a factor of 2.5-6.5 and displayed strong spatial patterns at the field scale. These spatial patterns were taxon-specific and correlated to soil properties, which indicates that members of a bacterial clade defined at high taxonomical levels shared specific ecological traits in the pasture. Ecologically meaningful assemblages of bacteria at the phylum or class level in the environment provides evidence that deep branching patterns of the 16S rRNA bacterial tree are actually mirrored in nature.

Turnover of soil bacterial diversity driven by wide-scale environmental heterogeneity

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 ~5.3 × 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.

Biogeography of soil microbial communities: a review and a description of the ongoing french national initiative

Microbial biogeography is the study of the distribution of microbial diversity on large scales of space and time. This science aims at understanding biodiversity regulation and its link with ecosystem biological functioning, goods and services such as maintenance of productivity, of soil and atmospheric quality, and of soil health. Although the initial concept dates from the early 20th century (Beijerinck (1913) De infusies en de ontdekking der backterien, in: Jaarboek van de Knoniklijke Akademie van Wetenschappen, Muller, Amsterdam), only recently have an increasing number of studies have investigated the biogeographical patterns of soil microbial diversity. A such delay is due to the constraints of the microbial models, the need to develop relevant molecular and bioinformatic tools to assess microbial diversity, and the non-availability of an adequate sampling strategy. Consequently, the conclusions from microbial ecology studies have rarely been generally applicable and even the fundamental power-laws differ because the taxa-area relationship and the influence of global and distal parameters on the spatial distribution of microbial communities have not been examined. In this article we define and discuss the scientific, technical and operational limits and outcomes resulting from soil microbial biogeography together with the technical and logistical feasibility. The main results are that microbial communities are not stochastically distributed on a wide scale and that biogeographical patterns are more influenced by local parameters such as soil type and land use than by distal ones, e.g. climate and geomorphology, contrary to plants and animals. We then present the European soil biological survey network, focusing on the French national initiative and the „ECOMIC-RMQS” project. The objective of the ECOMIC-RMQS project is to characterise the density and diversity of bacterial communities in all soils in the RMQS library in order to assess, for the first time, not only microbial biogeography across the whole of France but also the impact of land use on soil biodiversity (Réseau de Mesures de la Qualité des Sols = French Soil Quality Monitoring Network, 2200 soils covering all the French territory with a systematic grid of sampling). The scientific, technical and logistical outputs are examined with a view to the future prospects needed to develop this scientific domain and its applications in sustainable land use.

Multivariate analysis of the spatial patterns of 8 trace elements using the French soil monitoring network data

Geostatistical and spatially constrained multivariate analysis methods (MULTISPATI-PCA) have been applied at the scale of France to differentiate the influence of natural background from the pollution due to human activities on the content of 8 trace elements in the topsoil. The results of MULTISPATI-PCA evidence strong spatial structures attributed to different natural and artificial processes. The first axis can be interpreted as an axis of global richness in trace elements. Axis 2 reflects geochemical anomalies in Tl and Pb. Axis 3 exhibits on one hand natural pedogeogenic anomalies and on the other hand, it shows high values attributable to anthropogenic contamination. Finally, axis 4 is driven by anthropogenic copper contamination. At the French territory scale, we show that the main factors controlling trace elements distribution in the topsoil are soil texture, variations in parent material geology and weathering, and various anthropogenic sources.

Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale

Abstract Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes.

Generic Issues on Broad-Scale Soil Monitoring Schemes： A Review

Numerous scientific challenges arise when designing a soil monitoring network (SMN), especially when assessing large areas and several properties that are driven by numerous controlling factors of various origins and scales. Different broad approaches to the establishment of SMNs are distinguished. It is essential to establish an adequate sampling protocol that can be applied rigorously at each sampling location and time. We make recommendations regarding the within-site sampling of soil. Different statistical methods should be associated with the different types of sampling design. We review new statistical methods that account for different sources of uncertainty. Except for those parameters for which a consensus exists, the question of testing method harmonisation remains a very difficult issue. The establishment of benchmark sites devoted to harmonisation and inter-calibration is advocated as a technical solution. However, to our present knowledge, no study has addressed crucial scientific issues such as how many calibration sites are necessary and how to locate them.

Which persistent organic pollutants can we map in soil using a large spacing systematic soil monitoring design? A case study in Northern France.

Persistent organic pollutants (POPs) impact upon human and animal health and the wider environment. It is important to determine where POPs are found and the spatial pattern of POP variation. The concentrations of 90 molecules which are members of four families of POPs and two families of herbicides were measured within a region of Northern France as part of the French National Soil Monitoring Network (RMQS: Réseau de Mesures de la Qualité des Sols). We also gather information on five covariates (elevation, soil organic carbon content, road density, land cover and population density) which might influence POP concentrations. The study region contains 105 RMQS observation sites arranged on a regular square grid with spacing of 16 km. The observations include hot-spots at sites of POP application, smaller concentrations where POPs have been dispersed and observations less than the limit of quantification (LOQ) where the soil has not been impacted by POPs. Fifty nine of the molecules were detected at less than 50 sites and hence the data were unsuitable for spatial analyses. We represent the variation of the remaining 31 molecules by various linear mixed models which can include fixed effects (i.e. linear relationships between the molecule concentrations and covariates) and spatially correlated random effects. The best model for each molecule is selected by the Akaike Information Criterion. For nine of the molecules, spatial correlation is evident and hence they can potentially be mapped. For four of these molecules, the spatial correlation cannot be wholly explained by fixed effects. It appears that these molecules have been transported away from their application sites and are now dispersed across the study region with the largest concentrations found in a heavily populated depression. More complicated statistical models and sampling designs are required to explain the distribution of the less dispersed molecules.

Mapping and determinism of soil microbial community distribution across an agricultural landscape.

Despite the relevance of landscape, regarding the spatial patterning of microbial communities and the relative influence of environmental parameters versus human activities, few investigations have been conducted at this scale. Here, we used a systematic grid to characterize the distribution of soil microbial communities at 278 sites across a monitored agricultural landscape of 13 km². Molecular microbial biomass was estimated by soil DNA recovery and bacterial diversity by 16S rRNA gene pyrosequencing. Geostatistics provided the first maps of microbial community at this scale and revealed a heterogeneous but spatially structured distribution of microbial biomass and diversity with patches of several hundreds of meters. Variance partitioning revealed that both microbial abundance and bacterial diversity distribution were highly dependent of soil properties and land use (total variance explained ranged between 55% and 78%). Microbial biomass and bacterial richness distributions were mainly explained by soil pH and texture whereas bacterial evenness distribution was mainly related to land management. Bacterial diversity (richness, evenness, and Shannon index) was positively influenced by cropping intensity and especially by soil tillage, resulting in spots of low microbial diversity in soils under forest management. Spatial descriptors also explained a small but significant portion of the microbial distribution suggesting that landscape configuration also shapes microbial biomass and bacterial diversity.