Current Estimates of Soil Organic Carbon Stocks Are Not Four to Six Times Underestimated. Comment on "Non-Flat Earth Recalibrated for Terrain and Topsoil. Soil Syst. 2018, 2, 64"

In the interesting paper "Non-Flat Earth Recalibrated for Terrain and Topsoil" published in Soil Systems [...

Some considerations on aggregate sample supports for soil inventory and monitoring

Soil monitoring and inventory require a sampling strategy. One component of this strategy is the support of the basic soil observation: the size and shape of the volume of material that is collected and then analysed to return a single soil datum. Many, but not all, soil sampling schemes use aggregate supports in which material from a set of more than one soil cores, arranged in a given configuration, is aggregated and thoroughly mixed prior to analysis. In this paper, it is shown how the spatial statistics of soil information, collected on an aggregate support, can be computed from the covariance function of the soil variable on a core support (treated as point support). This is done via what is called here the discrete regularization of the core-support function. It is shown how discrete regularization can be used to compute the variance of soil sample means and to quantify the consistency of estimates made by sampling then re-sampling a monitoring network, given uncertainty in the precision with which sample sites are relocated. These methods are illustrated using data on soil organic carbon content from a transect in central England. Two aggregate supports, both based on a 20 m 20 m square, are compared with core support. It is shown that both the precision and the consistency of data collected on an aggregate support are better than data on a core support. This has implications for the design of sampling schemes for soil inventory and monitoring

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

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

Spatial patterns of bacteria show that members of higher taxa share ecological characteristics

Affiche, résuméWhether 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 2.520136.5 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

Quantifying and mapping topsoil inorganic carbon concentrations and stocks: approaches tested in France

Soils act as a sink or a source of atmospheric carbon, and great efforts are made to monitor soil organic carbon stocks, but soil inorganic carbon (SIC) stocks are not measured by many national- and continental-scale soil monitoring networks. Topsoil (0–30 cm) SIC concentrations were determined for > 2000 sites on a regular 16-km grid as part of the French, Réseau de Mesures de la Qualité des Sols (RMQS). We used design-based statistical methods to calculate unbiased estimates of the mean SIC concentration and total stocks across France. Model-based methods were used to determine the uncertainty of these estimates and to map the spatial distribution of these quantities. Observations of inorganic carbon were highly skewed and did not conform to standard statistical models. Data were normalized using a nonparametric transformation. The estimates and predictions of inorganic carbon are baselines against which the results of future phases of the network can be compared. We found that the total topsoil inorganic carbon stocks in France amount to 1070 ± 61 Tg, ca. one-third of the corresponding organic carbon stocks. Spatial distribution of SIC was strongly linked to the underlying geology. We tested the reliability of estimating SIC concentrations and stocks from the French Soil Test Database, which contains the results of 280 000 soil analyses requested by farmers between 1990 and 2004. A biased estimate of soil inorganic carbon concentrations resulted, presumably because soil samples were selected according to concerns of farmers rather than by a statistical design

Environmental Assessment of Soil for Monitoring: Volume IIb Survey of National Networks

The ENVASSO Project (Contract 022713) was funded 2006-8, under the European Commission 6th Framework Programme of Research, with the objective of defining and documenting a soil monitoring system appropriate for soil protection at continental level. The ENVASSO Consortium, comprising 37 partners drawn from 25 EU Member States, reviewed almost 300 soil indicators, identified existing soil inventories and monitoring programmes in the Member States, designed and programmed a database management system to capture, store and supply soil profile data, and drafted procedures and protocols appropriate for inclusion in a European soil monitoring network of sites that are geo-referenced and at which a qualified sampling process is or could be conducted. This volume (IIb), a Survey of National Networks, is the second of two reports that together constitute the most comprehensive study to date of the soil inventory and monitoring activities in the European Union. It contains comprehensive fact sheets for each national network, listing the purpose, sampling strategy adopted, analytical methods used and the number of monitoring sites.JRC.H.7-Climate Risk Managemen

Spatial patterns of bacteria show that members of higher taxa share ecological characteristics

Affiche, résuméWhether bacteria display spatial patterns of distribution and at which level of taxonomic organisation 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. Geostatistical modelling was used to analyse the spatial patterns of the taxa distributions. To test whether the spatial distributions of the different taxa were related to soil heterogeneity, we performed exploratory analyses of relationships between abundance of the bacterial taxa and key soil properties. The distributions of the relative abundance of most taxa varied by a factor of 2.5 to 6.5 and displayed strong spatial patterns at the field scale with autocorrelation ranging between 2 to 37 m. 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. Overall, the present study showed spatial patterns of distribution of bacteria both at the meter scale and at high taxonomical levels of organisation. Such spatial patterns allow comprehensive observations and predictions of bacterial occurrence in nature, hence helping in the generation of hypotheses concerning the mechanisms generating and maintaining bacterial diversity. The taxa-specific spatial patterns observed here suggest that, in a given environment, ecological traits are shared at high taxonomic levels within the domain Bacteria. This is a piece of evidence that the 16S rRNA gene tree divisions are not only based on evolutionary theory, but also have an ecological reality