Pesticide Residues in French Soils: Occurrence, Risks, and Persistence.

Contamination of the environment by pesticide residues is a growing concern given their widespread presence in the environment and their effects on ecosystems. Only a few studies have addressed the occurrence of pesticides in soils, and their results highlighted the need for further research on the persistence and risks induced by those substances. We monitored 111 pesticide residues (48 fungicides, 36 herbicides, 25 insecticides and/or acaricides, and two safeners) in 47 soils sampled across France under various land uses (arable lands, vineyards, orchards, forests, grasslands, and brownfields). Pesticides were found in 98% of the sites (46 of the 47 sampled), including untreated areas such as organic fields, forests, grasslands, and brownfields, with up to 33 different substances detected in one sample, mostly fungicides and herbicides. The concentrations of herbicides were the highest in soils with glyphosate, and its transformation product, AMPA, contributed 70% of the cumulative herbicides. Risk assessment underlined a moderate to high risk for earthworms in arable soils mostly attributed to insecticides and/or acaricides. Finally, the comparison with pesticide application by farmers underlines the presence of some residues long after their supposed 90% degradation and at concentrations higher than predicted environmental concentrations, leading to questions their real persistence in soils.

A new approach to estimate soil organic carbon content targets in European croplands topsoils.

Adopting land management practices that increase the stock of soil organic carbon (SOC) in croplands is widely promoted as a win-win strategy to enhance soil health and mitigate climate change. In this context, the definition of reference SOC content and stock values is needed to provide reliable targets to farmers, policymakers, and stakeholders. In this study, we used the LUCAS dataset to compare different methods for evaluating reference SOC content and stock values in European croplands topsoils (0-20 cm depth). Methods gave generally similar estimates although being built on very different assumptions. In the absence of an objective criterion to establish which approach is the most suitable to determine SOC reference values, we propose an ensemble modelling approach that consists in extracting the estimates using different relevant methods and retaining the median value among them. Interestingly, this approach led us to select values from the three different approaches with similar frequencies. Using estimated bulk density values, we obtained a first rough estimate of 3.5 Gt C of SOC storage potential in the cropland topsoils that we interpret as a long-term aspirational target that would be reachable only under extreme changes in agricultural practices. The use of additional methods in the ensemble modelling approach and more valid statistical spatial estimates may further refine our approach designed for the estimation of SOC reference values for croplands.

Biogeographical patterns of the soil fungal:bacterial ratio across France.

Soils are one of the major reservoirs of biological diversity on our planet because they host a huge richness of microorganisms. The fungal:bacterial (F:B) ratio targets two major functional groups of organisms in soils and can improve our understanding of their importance and efficiency for soil functioning. To better decipher the variability of this ratio and rank the environmental parameters involved, we used the French Soil Quality Monitoring Network (RMQS)-one of the most extensive and a priori-free soil sampling surveys, based on a systematic 16 km × 16 km grid and including more than 2,100 samples. F:B ratios, measured by quantitative PCR targeting the 18S and 16S rDNA genes, turned out to be heterogenously distributed and spatially structured in geographical patterns across France. These distribution patterns differed from bacterial or fungal densities taken separately, supporting the hypothesis that the F:B ratio is not the mere addition of each density but rather results from the complex interactions of the two functional groups. The F:B ratios were mainly influenced by soil characteristics and land management. Among soil characteristics, the pH and, to a lesser extent, the organic carbon content and the carbon:nitrogen (C:N) ratio were the main drivers. These results improved our understanding of soil microbial communities, and from an operational point of view, they suggested that the F:B ratio should be a useful new bioindicator of soil status. The resulting dataset can be considered as a first step toward building up a robust repository essential to any bioindicator and aimed at guiding and helping decision making. IMPORTANCE In the face of human disturbances, microbial activity can be impacted and, e.g., can result in the release of large amounts of soil carbon into the atmosphere, with global impacts on temperature. Therefore, the development and the regular use of soil bioindicators are essential to (i) improve our knowledge of soil microbial communities and (ii) guide and help decision makers define suitable soil management strategies. Bacterial and fungal communities are key players in soil organic matter turnover, but with distinct physiological and ecological characteristics. The fungal:bacterial ratio targets these two major functional groups by investigating their presence and their equilibrium. The aim of our study is to characterize this ratio at a territorial scale and rank the environmental parameters involved so as to further develop a robust repository essential to the interpretation of any bioindicator of soil quality.

Analyzing the spatial distribution of PCB concentrations in soils using below-quantification limit data.

Polychlorinated biphenyls (PCBs) are highly toxic environmental pollutants that can accumulate in soils. We consider the problem of explaining and mapping the spatial distribution of PCBs using a spatial data set of 105 PCB-187 measurements from a region in the north of France. A large proportion of our data (35%) fell below a quantification limit (QL), meaning that their concentrations could not be determined to a sufficient degree of precision. Where a measurement fell below this QL, the inequality information was all that we were presented with. In this work, we demonstrate a full geostatistical analysis-bringing together the various components, including model selection, cross-validation, and mapping-using censored data to represent the uncertainty that results from below-QL observations. We implement a Monte Carlo maximum likelihood approach to estimate the geostatistical model parameters. To select the best set of explanatory variables for explaining and mapping the spatial distribution of PCB-187 concentrations, we apply the Akaike Information Criterion (AIC). The AIC provides a trade-off between the goodness-of-fit of a model and its complexity (i.e., the number of covariates). We then use the best set of explanatory variables to help interpolate the measurements via a Bayesian approach, and produce maps of the predictions. We calculate predictions of the probability of exceeding a concentration threshold, above which the land could be considered as contaminated. The work demonstrates some differences between approaches based on censored data and on imputed data (in which the below-QL data are replaced by a value of half of the QL). Cross-validation results demonstrate better predictions based on the censored data approach, and we should therefore have confidence in the information provided by predictions from this method.

Correction: Mapping and predictive variations of soil bacterial richness across France.

[This corrects the article DOI: 10.1371/journal.pone.0186766.].

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.

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.

Management of soil pH promotes nitrous oxide reduction and thus mitigates soil emissions of this greenhouse gas.

While concerns about human-induced effects on the Earth's climate have mainly concentrated on carbon dioxide (CO2) and methane (CH4), reducing anthropogenic nitrous oxide (N2O) flux, mainly of agricultural origin, also represents an opportunity for substantial mitigation. To develop a solution that induces neither the transfer of nitrogen pollution nor decreases agricultural production, we specifically investigated the last step of the denitrification pathway, the N2O reduction path, in soils. We first observed that this path is mainly driven by soil pH and is progressively inhibited when pH is lower than 6.8. During field experiments, we observed that liming acidic soils to neutrality made N2O reduction more efficient and decreased soil N2O emissions. As we estimated acidic fertilized soils to represent 37% [27-50%] of French soils, we calculated that liming could potentially decrease France's total N2O emissions by 15.7% [8.3-21.2%]. Nevertheless, due to the different possible other impacts of liming, we currently recommend that the deployment of this solution to mitigate N2O emission should be based on local studies that take into account agronomic, environmental and economic aspects.

National estimation of soil organic carbon storage potential for arable soils: A data-driven approach coupled with carbon-landscape zones.

Soil organic carbon (SOC) is important for its contributions to agricultural production, food security, and ecosystem services. Increasing SOC stocks can contribute to mitigate climate change by transferring atmospheric CO2 into long-lived soil carbon pools. The launch of the 4 per 1000 initiative has resulted in an increased interest in developing methods to quantity the additional SOC that can be stored in soil under different management options. In this work, we have made a first attempt to estimate SOC storage potential of arable soils using a data-driven approach based on the French National Soil Monitoring Network. The data-driven approach was used to determine the maximum SOC stocks of arable soils for France. We first defined different carbon-landscape zones (CLZs) using clustering analysis. We then computed estimates of the highest possible values using percentile of 0.8, 0.85, 0.9 and 0.95 of the measured SOC stocks within these CLZs. The SOC storage potential was calculated as the difference between the maximum SOC stocks and current SOC stocks for topsoil and subsoil. The percentile used to determine highest possible SOC had a large influence on the estimates of French national SOC storage potential. When the percentile increased from 0.8 to 0.95, the national SOC storage potential increased by two to three-fold, from 336 to 1020 Mt for topsoil and from 165 to 433 Mt for subsoil, suggesting a high sensitivity of this approach to the selected percentile. Nevertheless, we argue that this approach can offer advantages from an operational point of view, as it enables to set targets of SOC storage taking into account both policy makers' and farmers' considerations about their feasibility. Robustness of the estimates should be further assessed using complementary approaches such as mechanistic modelling.

Characterization of Environmental Health Inequalities Due to Polyaromatic Hydrocarbon Exposure in France.

Reducing environmental health inequalities has become a major focus of public health efforts in France, as evidenced by the French action plans for health and the environment. To evaluate environmental inequalities, routine monitoring networks provide a valuable source of data on environmental contamination, which can be used in integrated assessments, to identify overexposed populations and prioritize actions. However, available databases generally do not meet sufficient spatial representativeness to characterize population exposure, as they are usually not assembled for this specific purpose. The aim of this study was to develop geoprocessing procedures and statistical methods to build spatial environmental variables (water, air, soil, and food pollutant concentrations) at a fine resolution, and provide appropriate input for the exposure modelling. Those methods were designed to combine in situ monitoring data with correlated auxiliary information (for example, atmospheric emissions, population, and altitude), in order to better represent the variability of the environmental compartment quality. The MODUL'ERS multimedia exposure model developed by INERIS (French Institute for industrial Environment and Risks) was then used to assess the transfer of substances from the environment to humans, through inhalation and ingestion pathway characterization. We applied the methodology to a carcinogenic Polycyclic Aromatic Hydrocarbon substance, benzo[a]pyrene(B[a]P), to map spatialized exposure indicators, at the national scale. The largest environmental contribution corresponded to the ingestion pathway. Data processing algorithms and calculation of exposure will be integrated into the French coordinated integrated environment and health platform PLAINE (PLteforme intégrée d'Analyse des INégalités Environnementales) which has been developed to map and analyze environmental health inequalities.

Fine resolution map of top- and subsoil carbon sequestration potential in France.

Although soils have a high potential to offset CO2 emissions through its conversion into soil organic carbon (SOC) with long turnover time, it is widely accepted that there is an upper limit of soil stable C storage, which is referred to SOC saturation. In this study we estimate SOC saturation in French topsoil (0-30cm) and subsoil (30-50cm), using the Hassink equation and calculate the additional SOC sequestration potential (SOCsp) by the difference between SOC saturation and fine fraction C on an unbiased sampling set of sites covering whole mainland France. We then map with fine resolution the geographical distribution of SOCsp over the French territory using a regression Kriging approach with environmental covariates. Results show that the controlling factors of SOCsp differ from topsoil and subsoil. The main controlling factor of SOCsp in topsoils is land use. Nearly half of forest topsoils are over-saturated with a SOCsp close to 0 (mean and standard error at 0.19±0.12) whereas cropland, vineyard and orchard soils are largely unsaturated with degrees of C saturation deficit at 36.45±0.68% and 57.10±1.64%, respectively. The determinant of C sequestration potential in subsoils is related to parent material. There is a large additional SOCsp in subsoil for all land uses with degrees of C saturation deficit between 48.52±4.83% and 68.68±0.42%. Overall the SOCsp for French soils appears to be very large (1008Mt C for topsoil and 1360Mt C for subsoil) when compared to previous total SOC stocks estimates of about 3.5Gt in French topsoil. Our results also show that overall, 176Mt C exceed C saturation in French topsoil and might thus be very sensitive to land use change.

Biogeography of soil bacteria and archaea across France.

Over the last two decades, a considerable effort has been made to decipher the biogeography of soil microbial communities as a whole, from small to broad scales. In contrast, few studies have focused on the taxonomic groups constituting these communities; thus, our knowledge of their ecological attributes and the drivers determining their composition and distribution is limited. We applied a pyrosequencing approach targeting 16S ribosomal RNA (rRNA) genes in soil DNA to a set of 2173 soil samples from France to reach a comprehensive understanding of the spatial distribution of bacteria and archaea and to identify the ecological processes and environmental drivers involved. Taxonomic assignment of the soil 16S rRNA sequences indicated the presence of 32 bacterial phyla or subphyla and 3 archaeal phyla. Twenty of these 35 phyla were cosmopolitan and abundant, with heterogeneous spatial distributions structured in patches ranging from a 43- to 260-km radius. The hierarchy of the main environmental drivers of phyla distribution was soil pH > land management > soil texture > soil nutrients > climate. At a lower taxonomic level, 47 dominant genera belonging to 12 phyla aggregated 62.1% of the sequences. We also showed that the phylum-level distribution can be determined largely by the distribution of the dominant genus or, alternatively, reflect the combined distribution of all of the phylum members. Together, our study demonstrated that soil bacteria and archaea present highly diverse biogeographical patterns on a nationwide scale and that studies based on intensive and systematic sampling on a wide spatial scale provide a promising contribution for elucidating soil biodiversity determinism.

Spatial analysis of trace elements in a moss bio-monitoring data over France by accounting for source, protocol and environmental parameters.

Air pollution in trace elements (TE) remains a concern for public health in Europe. For this reasons, networks of air pollution concentrations or exposure are deployed, including a moss bio-monitoring programme in Europe. Spatial determinants of TE concentrations in mosses remain unclear. In this study, the French dataset of TE in mosses is analyzed by spatial autoregressive model to account for spatial structure of the data and several variables proven or suspected to affect TE concentrations in mosses. Such variables include source (atmospheric deposition and soil concentrations), protocol (sampling month, collector, and moss species), and environment (forest type and canopy density, distance to the coast or the highway, and elevation). Modeled atmospheric deposition was only available for Cd and Pb and was one of the main explanatory variables of the concentrations in mosses. Predicted soil content was also an important explanatory variable except for Cr, Ni, and Zn. However, the moss species was the main factor for all the studied TE. The other environmental variables affected differently the TE. In particular, the forest type and canopy density were important in most cases. These results stress the need for further research on the effect of the moss species on the capture and retention of TE, as well as for accounting for several variables and the spatial structure of the data in statistical analyses.

Mapping and predictive variations of soil bacterial richness across France.

Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition.

Correction: Mapping and predictive variations of soil bacterial richness across France.

[This corrects the article DOI: 10.1371/journal.pone.0186766.].

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives.

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

Multivariate spatial analyses of the distribution and origin of trace and major elements in soils surrounding a secondary lead smelter.

Major and trace elements in soils originate from natural processes and different anthropogenic activities which are difficult to discriminate. On a 17-ha impacted site in northern France, two industrial sources of soil contamination were xidentified: a former iron foundry and a current secondary lead smelter. To discriminate and map natural and anthropogenic sources of major and trace elements on this site, the rarely applied MULTISPATI-principal component analysis (PCA) method was used. Using a 20-m × 20-m grid, 247 topsoil horizons were sampled and analysed with a field-portable X-ray fluorescence analyser for screening soil contamination. The study site was heavily contaminated with Pb and, to a lesser degree, with Sn. Summary statistics and enrichment factors allowed the differentiation of the main lithogenic or anthropogenic origin of the elements. The MULTISPATI-PCA method, which explained 73.9 % of the variability with the three first factors, evidenced strong spatial structures. Those spatial structures were attributed to different natural and artificial processes in the study area. The first axis can be interpreted as a lithogenic effect. Axes 2 and 3 reflect the two different contamination sources. Pb, Sn and S originated from the secondary lead smelter while Fe and Ca were mainly derived from the old iron foundry activity and the old railway built with foundry sand. This study demonstrated that the MULTISPATI-PCA method can be successfully used to investigate multicontaminated sites to discriminate the various sources of contamination.

Occurrence of Stenotrophomonas maltophilia in agricultural soils and antibiotic resistance properties.

The occurrence of Stenotrophomonas maltophilia was monitored in organic amendments and agricultural soils from various sites in France and Tunisia. S. maltophilia was detected in horse and bovine manures, and its abundance ranged from 0.294 (±0.509) × 10(3) to 880 (±33.4) × 10(3) CFU (g drywt)(-1) of sample. S. maltophilia was recovered from most tested soil samples (104/124). Its abundance varied from 0.33 (±0.52) to 414 (±50) × 10(3) CFU (g drywt)(-1) of soil and was not related to soil characteristics. Antibiotic resistance properties of a set of environmental strains were compared to a clinical set, and revealed a high diversity of antibiotic resistance profiles, given both the numbers of resistance and the phenotypes. Manure strains showed resistance phenotypes, with most of the strains resisting between 7 and 9 antibiotics. While French soil strains were sensitive to most antibiotics tested, some Tunisian strains displayed resistance phenotypes close to those of clinical French strains. Screening for metal resistance among 66 soil strains showed a positive relationship between antibiotic and metal resistance. However, the prevalence of antibiotic resistance phenotypes in the studied sites was not related to the metal content in soil samples.

Modeling climate change effects on the potential production of French plains forests at the sub-regional level.

We modeled the effects of climate change and two forest management scenarios on wood production and forest carbon balance in French forests using process-based models of forest growth. We combined data from the national forest inventory and soil network survey, which were aggregated over a 50 x 50-km grid, i.e., the spatial resolution of the climate scenario data. We predicted and analyzed the climate impact on potential forest production over the period 1960-2100. All models predicted a slight increase in potential forest yield until 2030-2050, followed by a plateau or a decline around 2070-2100, with overall, a greater increase in yield in northern France than in the south. Gross and net primary productivities were more negatively affected by soil water and atmospheric water vapor saturation deficits in western France because of a more pronounced shift in seasonal rainfall from summer to winter. The rotation-averaged values of carbon flux and production for different forest management options were estimated during four years (1980, 2015, 2045 and 2080). Predictions were made using a two-dimensional matrix covering the range of local soil and climate conditions. The changes in ecosystem fluxes and forest production were explained by the counterbalancing effect of rising CO2 concentration and increasing water deficit. The effect of climate change decreased with rotation length from short rotations with high production rates and low standing biomasses to long rotations with low productivities and greater standing biomasses. Climate effects on productivity, both negative and positive, were greatest on high fertility sites. Forest productivity in northern France was enhanced by climate change, increasingly from west to east, whereas in the southwestern Atlantic region, productivity was reduced by climate change to an increasing degree from west to east.