A trait-based ecological perspective on the soil microbial antibiotic-related genetic machinery.

Antibiotic resistance crisis dictates the need for resistance monitoring and the search for new antibiotics. The development of monitoring protocols is hindered by the great diversity of resistance factors, while the "streetlight effect" denies the possibility of discovering novel drugs based on existing databases. In this study, we address these challenges using high-throughput environmental screening viewed from a trait-based ecological perspective. Through an in-depth analysis of the metagenomes of 658 topsoil samples spanning Europe, we explored the distribution of 241 prokaryotic and fungal genes responsible for producing metabolites with antibiotic properties and 485 antibiotic resistance genes. We analyzed the diversity of these gene collections at different levels and modeled the distribution of each gene across environmental gradients. Our analyses revealed several nonparallel distribution patterns of the genes encoding sequential steps of enzymatic pathways synthesizing large antibiotic groups, pointing to gaps in existing databases and suggesting potential for discovering new analogues of known antibiotics. We show that agricultural activity caused a continental-scale homogenization of microbial antibiotic-related machinery, emphasizing the importance of maintaining indigenous ecosystems within the landscape mosaic. Based on the relationships between the proportion of the genes in the metagenomes with the main predictors (soil pH, land cover type, climate temperature and humidity), we illustrate how the properties of chemical structures dictate the distribution of the genes responsible for their synthesis across environments. With this understanding, we propose general principles to facilitate the discovery of antibiotics, including principally new ones, establish abundance baselines for antibiotic resistance genes, and predict their dissemination.

An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques.

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model's output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model's predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model's output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.

Benchmarking soil organic carbon (SOC) concentration provides more robust soil health assessment than the SOC/clay ratio at European scale.

Increasing soil organic carbon (SOC) confers benefits to soil health, biodiversity, underpins carbon sequestration and ameliorates land degradation. One recommendation is to increase SOC such that the SOC to clay ratio (SOC/clay) exceeds 1/13, yet normalising SOC levels based on clay alone gives misleading indications of soil structure and the potential to store additional carbon. Building on work by Poeplau & Don (2023) to benchmark observed against predicted SOC, we advance an alternative indicator: the ratio between observed and "typical" SOC (O/T SOC) for pan-European application. Here, "typical" SOC is the average concentration in different pedo-climate zones, PCZs (which, unlike existing SOC indicators, incorporate land cover and climate, alongside soil texture) across Europe, determined from mineral (<20 % organic matter) topsoils (0-20 cm) sampled during 2009-2018 in LUCAS, Europe's largest soil monitoring scheme (n = 19,855). Regression tree modelling derived 12 PCZs, with typical SOC values ranging 5.99-39.65 g kg-1. New index classes for comparison with SOC/clay grades were established from the quartiles of each PCZ's O/T SOC distribution; these were termed: "Low" (below the 25th percentile), "Intermediate" (between the 25th and 50th percentiles), "High" (between the 50th and 75th percentiles), and "Very high" (above the 75th percentile). Compared with SOC/clay, O/T SOC was less sensitive to clay content, land cover, and climate, less geographically skewed, and better reflected differences in soil porosity and SOC stock, supporting 2 EU Soil Health Mission objectives (consolidating SOC stocks; improving soil structure for crops and biota). These patterns held for 2 independent datasets, and O/T SOC grades were sensitive enough to reflect land management differences across several long-term field experiments. O/T SOC used in conjunction with several other physical, chemical and biological soil health indicators can help support the EU Soil Monitoring Law and achieve several United Nations Sustainable Development Goals.

Into the unknown: The role of post-fire soil erosion in the carbon cycle.

Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires and highlights the need for more field data to further validate this initial assessment.

A unifying modelling of multiple land degradation pathways in Europe.

Land degradation is a complex socio-environmental threat, which generally occurs as multiple concurrent pathways that remain largely unexplored in Europe. Here we present an unprecedented analysis of land multi-degradation in 40 continental countries, using twelve dataset-based processes that were modelled as land degradation convergence and combination pathways in Europe's agricultural (and arable) environments. Using a Land Multi-degradation Index, we find that up to 27%, 35% and 22% of continental agricultural (~2 million km2) and arable (~1.1 million km2) lands are currently threatened by one, two, and three drivers of degradation, while 10-11% of pan-European agricultural/arable landscapes are cumulatively affected by four and at least five concurrent processes. We also explore the complex pattern of spatially interacting processes, emphasizing the major combinations of land degradation pathways across continental and national boundaries. Our results will enable policymakers to develop knowledge-based strategies for land degradation mitigation and other critical European sustainable development goals.

An integrated modeling approach for estimating monthly global rainfall erosivity.

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001-2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission's Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha-1 h-1 month-1 in June-August over the Northern Hemisphere and ~ 700 MJ mm ha-1 h-1 month-1 in December-February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December-February and June-August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10-30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Modeling arsenic in European topsoils with a coupled semiparametric (GAMLSS-RF) model for censored data.

Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Organization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection.

Interaction effects of pH and land cover on soil microbial diversity are climate-dependent.

Factors regulating the diversity and composition of soil microbial communities include soil properties, land cover and climate. How these factors interact at large scale remains poorly investigated. Here, we used an extensive dataset including 715 locations from 24 European countries to investigate the interactive effects of climatic region, land cover and pH on soil bacteria and fungi. We found that differences in microbial diversity and community composition between land cover types depended on the climatic region. In Atlantic, Boreal and Continental regions, microbial richness was higher in croplands and grasslands than woodlands while richness in Mediterranean areas did not vary significantly among land cover types. These differences were further related to soil pH, as a driver of bacterial and fungal richness in most climatic regions, but the interaction of pH with land cover depended on the region. Microbial community composition differed the most between croplands and woodlands in all regions, mainly due to differences in pH. In the Mediterranean region, bacterial communities in woodlands and grasslands were the most similar, whereas in other regions, grassland and cropland-associated bacteria showed more similarity. Overall, we showed that key factors interact in shaping soil microbial communities in a climate-dependent way at large scale.

How the EU Soil Observatory contributes to a stronger soil erosion community.

New policy developments have emerged in relation to soil conservation after 2020. The Common Agricultural Policy (CAP) 2023-2027, the proposal for a Soil Monitoring Law and the mission 'A Soil Deal for Europe' have shaped a new policy framework at EU level, which requires updated assessments on soil erosion and land degradation. The EU Soil Observatory (EUSO) successfully organised a scientific workshop on 'Soil erosion for the EU' in June 2022. The event has seen the participation of more than 330 people from 63 countries, addressing important topics such as (i) management practices, (ii) large scale modelling, (iii) the importance of sediments in nutrient cycle, (vi) the role of landslides and (v) laying the foundations for early career scientists. As a follow up, among the 120 abstracts submitted in the workshop, we received fifteen manuscripts, out of which nine were selected for publication in the present special issue. In this editorial, we summarize the major challenges that the soil erosion research community faces in relation to supporting the increasing role of soils in the EU Green Deal.

Pervasive soil phosphorus losses in terrestrial ecosystems in China.

Future phosphorus (P) shortages could seriously affect terrestrial productivity and food security. We investigated the changes in topsoil available P (AP) and total P (TP) in China's forests, grasslands, paddy fields, and upland croplands during the 1980s-2010s based on substantial repeated soil P measurements (63,220 samples in the 1980s, 2000s, and 2010s) and machine learning techniques. Between the 1980s and 2010s, total soil AP stock increased with a small but significant rate of 0.13 kg P ha-1 year-1 , but total soil TP stock declined substantially (4.5 kg P ha-1 year-1 ) in the four ecosystems. We quantified the P budgets of soil-plant systems by harmonizing P fluxes from various sources for this period. Matching trends of soil contents over the decades with P budgets and fluxes, we found that the P-surplus in cultivated soils (especially in upland croplands) might be overestimated due to the great soil TP pool compared to fertilization and the substantial soil P losses through plant uptake and water erosion that offset the P additions. Our findings of P-deficit in China raise the alarm on the sustainability of future biomass production (especially in forests), highlight the urgency of P recycling in croplands, and emphasize the critical role of country-level basic data in guiding sound policies to tackle the global P crises.

Quantitative analysis of the compliance of EU Sewage Sludge Directive by using the heavy metal concentrations from LUCAS topsoil database.

In the European Union (EU), a common understanding of the potential harmful effect of sewage sludge (SS) on the environment is regulated by the Sewage Sludge Directive 86/278/EEC (SSD). Limit values (LVs) for concentrations of heavy metals in soil are listed in Impact Assessment of this directive, and they were transposed by EU member states using different criteria. Member states adopted either single limit values or based on soil factors such as pH and texture to define the maximum limit values for concentrations of heavy metals in soils. Our work presents the first quantitative analysis of the SSD at the European level by using the Land Use and Coverage Area Frame Survey (LUCAS) 2009 topsoil database. The reference values at the European level were arranged taking into account the upper value (EU_UL) and the lower value (EU_LL) for each heavy metal (arsenic, cadmium, copper, chromium, mercury, nickel, lead, and zinc) as well as taking into account the pH of the soil (cadmium, copper, mercury, nickel, lead, and zinc) as introduced in the SSD Annex IA. Single and integrated contamination rate indices were developed to identify those agricultural soils that exceeded the reference values for each heavy metal. In total, 10%, 36%, and 19% of the LUCAS 2009 topsoil samples exceeded the limit values. Additionally, 12% and 16% of agricultural soils exceeded the concentration of at least one single heavy metal when European LVs were fixed following the soil pH in Strategy II compared to those national ones in Strategy I. Generally, all member states apply similar or stricter limit values than those laid down in the SSD. Our work indicates that choosing LVs quantitatively affects further actions such as monitoring and remediation of contaminated soils. The actual soil parameters, such as heavy metal concentrations and soil pH values from the LUCAS 2009 topsoil database, could be used by SSD-involved policy stakeholders not only to lay down the LVs for concentrations of heavy metal in soils but also for monitoring the SSD compliance grade by using the LUCAS surveys over time (past and upcoming LUCAS datasets).

Soil organic carbon stocks in European croplands and grasslands: How much have we lost in the past decade?

The EU Soil Strategy 2030 aims to increase soil organic carbon (SOC) in agricultural land to enhance soil health and support biodiversity as well as to offset greenhouse gas emissions through soil carbon sequestration. Therefore, the quantification of current SOC stocks and the spatial identification of the main drivers of SOC changes is paramount in the preparation of agricultural policies aimed at enhancing the resilience of agricultural systems in the EU. In this context, changes of SOC stocks (Δ SOCs) for the EU + UK between 2009 and 2018 were estimated by fitting a quantile generalized additive model (qGAM) on data obtained from the revisited points of the Land Use/Land Cover Area Frame Survey (LUCAS) performed in 2009, 2015 and 2018. The analysis of the partial effects derived from the fitted qGAM model shows that land use and land use change observed in the 2009, 2015 and 2018 LUCAS campaigns (i.e. continuous grassland [GGG] or cropland [CCC], conversion grassland to cropland (GGC or GCC) and vice versa [CGG or CCG]) was one of the main drivers of SOC changes. The CCC was the factor that contributed to the lowest negative change on Δ SOC with an estimated partial effect of -0.04 ± 0.01 g C kg-1 year-1 , while the GGG the highest positive change with an estimated partial effect of 0.49 ± 0.02 g C kg-1 year-1 . This confirms the C sequestration potential of converting cropland to grassland. However, it is important to consider that local soil and environmental conditions may either diminish or enhance the grassland's positive effect on soil C storage. In the EU + UK, the estimated current (2018) topsoil (0-20 cm) SOC stock in agricultural land below 1000 m a.s.l was 9.3 Gt, with a Δ SOC of -0.75% in the period 2009-2018. The highest estimated SOC losses were concentrated in central-northern countries, while marginal losses were observed in the southeast.

Cadmium in topsoils of the European Union - An analysis based on LUCAS topsoil database.

Cadmium (Cd) is a naturally occurring element that can accumulate in the soil through the application of fertilisers containing cadmium and as a waste of industrial processes. Cadmium inputs in the soil have increased significantly (+50 %) during the 20th century as a result of the application of fertilisers and sewage sludge, and also due to local contamination (e.g. waste dumping, mining) and industrial emissions (e.g. zinc smelters). Using the 21,682 soil samples from the LUCAS soil survey, we aim to estimate the spatial distribution of the concentration of Cd in the European Union (EU) and UK topsoil. Out of the total, 72.6 % of the samples have Cd values <0.07 mg kg-1, 21.6 % in the range 0.07-1 mg kg-1 and the remaining 5.5 % higher than the threshold of 1 mg kg-1, which is generally considered the limit for risk assessment. The mean Cd value in the EU topsoils is 0.20 mg kg-1, slightly higher in grasslands (0.24 mg kg-1) compared to croplands (0.17 mg kg-1). Applying an ensemble of machine learning models supported by a variety of environmental descriptors, we created maps of Cd distribution at a resolution of 100 m. The ensemble approach included five models and increased the prediction accuracy to R2 of 0.45 (an increase of 0.1 compared to best single model performance). The approach used resulted in a high predictive power for the general Cd distribution, while also identifying hotspots of Cd contamination. Natural factors influencing Cd levels include soil properties (pH, clay), topography, soil erosion, and leaching. As anthropogenic factors, we identified phosphorus inputs to agricultural lands as the most important for Cd levels. The application of the EU Fertiliser Directive should further limit Cd inputs and potentially the Cd content in soils.

Including land management in a European carbon model with lateral transfer to the oceans.

The use of cover crops (CCs) is a promising cropland management practice with multiple benefits, notably in reducing soil erosion and increasing soil organic carbon (SOC) storage. However, the current ability to represent these factors in land surface models remains limited to small scales or simplified and lumped approaches due to the lack of a sediment-carbon erosion displacement scheme. This precludes a thorough understanding of the consequences of introducing a CC into agricultural systems. In this work, this problem was addressed in two steps with the spatially distributed CE-DYNAM model. First, the historical effect of soil erosion, transport, and deposition on the soil carbon budget at a continental scale in Europe was characterized since the early industrial era, using reconstructed climate and land use forcings. Then, the impact of two distinct policy-oriented scenarios for the introduction of CCs were evaluated, covering the European cropping systems where surface erosion rates or nitrate susceptibility are critical. The evaluation focused on the increase in SOC storage and the export of particulate organic carbon (POC) to the oceans, compiling a continental-scale carbon budget. The results indicated that Europe exported 1.95 TgC/year of POC to the oceans in the last decade, and that CCs can contribute to reducing this amount while increasing SOC storage. Compared to the simulation without CCs, the additional rate of SOC storage induced by CCs peaked after 10 years of their adoption, followed by a decrease, and the cumulative POC export reduction stabilized after around 13 years. The findings indicate that the impacts of CCs on SOC and reduced POC export are persistent regardless of their spatial allocation adopted in the scenarios. Together, the results highlight the importance of taking the temporal aspect of CC adoption into account and indicate that CCs alone are not sufficient to meet the targets of the 4‰ initiative. Despite some known model limitations, which include the lack of feedback of erosion on the net primary productivity and the representation of carbon fluxes with an emulator, the current work constitutes the first approach to successfully couple a distributed routing scheme of eroded carbon to a land carbon model emulator at a reasonably high resolution and continental scale. SHORT ABSTRACT: A spatially distributed model coupling erosion, transport, and deposition to the carbon cycle was developed. Then, it was used to simulate the impact of cover crops on both erosion and carbon, to show that cover crops can simultaneously increase organic carbon storage and reduce particulate organic carbon export to the oceans. The results seemed persistent regardless of the spatial distribution of cover crops.

Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution.

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk. In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

EUSEDcollab: a network of data from European catchments to monitor net soil erosion by water.

As a network of researchers we release an open-access database (EUSEDcollab) of water discharge and suspended sediment yield time series records collected in small to medium sized catchments in Europe. EUSEDcollab is compiled to overcome the scarcity of open-access data at relevant spatial scales for studies on runoff, soil loss by water erosion and sediment delivery. Multi-source measurement data from numerous researchers and institutions were harmonised into a common time series and metadata structure. Data reuse is facilitated through accompanying metadata descriptors providing background technical information for each monitoring station setup. Across ten European countries, EUSEDcollab covers over 1600 catchment years of data from 245 catchments at event (11 catchments), daily (22 catchments) and monthly (212 catchments) temporal resolution, and is unique in its focus on small to medium catchment drainage areas (median = 43 km2, min = 0.04 km2, max = 817 km2) with applicability for soil erosion research. We release this database with the aim of uniting people, knowledge and data through the European Union Soil Observatory (EUSO).

Ecosystem type drives soil eukaryotic diversity and composition in Europe.

Soil eukaryotes play a crucial role in maintaining ecosystem functions and services, yet the factors driving their diversity and distribution remain poorly understood. While many studies focus on some eukaryotic groups (mostly fungi), they are limited in their spatial scale. Here, we analyzed an unprecedented amount of observational data of soil eukaryomes at continental scale (787 sites across Europe) to gain further insights into the impact of a wide range of environmental conditions (climatic and edaphic) on their community composition and structure. We found that the diversity of fungi, protists, rotifers, tardigrades, nematodes, arthropods, and annelids was predominantly shaped by ecosystem type (annual and permanent croplands, managed and unmanaged grasslands, coniferous and broadleaved woodlands), and higher diversity of fungi, protists, nematodes, arthropods, and annelids was observed in croplands than in less intensively managed systems, such as coniferous and broadleaved woodlands. Also in croplands, we found more specialized eukaryotes, while the composition between croplands was more homogeneous compared to the composition of other ecosystems. The observed high proportion of overlapping taxa between ecosystems also indicates that DNA has accumulated from previous land uses, hence mimicking the land transformations occurring in Europe in the last decades. This strong ecosystem-type influence was linked to soil properties, and particularly, soil pH was driving the richness of fungi, rotifers, and annelids, while plant-available phosphorus drove the richness of protists, tardigrades, and nematodes. Furthermore, the soil organic carbon to total nitrogen ratio crucially explained the richness of fungi, protists, nematodes, and arthropods, possibly linked to decades of agricultural inputs. Our results highlighted the importance of long-term environmental variables rather than variables measured at the time of the sampling in shaping soil eukaryotic communities, which reinforces the need to include those variables in addition to ecosystem type in future monitoring programs and conservation efforts.

Spatial assessment of topsoil zinc concentrations in Europe.

Zinc (Zn) is essential to sustain crop production and human health, while it can be toxic when present in excess. In this manuscript, we applied a machine learning model on 21,682 soil samples from the Land Use and Coverage Area frame Survey (LUCAS) topsoil database of 2009/2012 to assess the spatial distribution in Europe of topsoil Zn concentrations measured by aqua regia extraction, and to identify the influence of natural drivers and anthropogenic sources on topsoil Zn concentrations. As a result, a map was produced showing topsoil Zn concentrations in Europe at a resolution of 250 m. The mean predicted Zn concentration in Europe was 41 mg kg-1, with a root mean squared error of around 40 mg kg-1 calculated for independent soil samples. We identified clay content as the most important factor explaining the overall distribution of soil Zn in Europe, with lower Zn concentrations in coarser soils. Next to texture, low Zn concentrations were found in soils with low pH (e.g. Podzols), as well as in soils with pH above 8 (i.e., Calcisols). The presence of deposits and mining activities mainly explained the occurrence of relatively high Zn concentrations above 167 mg kg-1 (the one percentile highest concentrations) within 10 km from these sites. In addition, the relatively higher Zn levels found in grasslands in regions with high livestock density may point to manure as a significant source of Zn in these soils. The map developed in this study can be used as a reference to assess the eco-toxicological risks associated with soil Zn concentrations in Europe and areas with Zn deficiency. In addition, it can provide a baseline for future policies in the context of pollution, soil health, human health, and crop nutrition.

Patterns in soil microbial diversity across Europe.

Factors driving microbial community composition and diversity are well established but the relationship with microbial functioning is poorly understood, especially at large scales. We analysed microbial biodiversity metrics and distribution of potential functional groups along a gradient of increasing land-use perturbation, detecting over 79,000 bacterial and 25,000 fungal OTUs in 715 sites across 24 European countries. We found the lowest bacterial and fungal diversity in less-disturbed environments (woodlands) compared to grasslands and highly-disturbed environments (croplands). Highly-disturbed environments contain significantly more bacterial chemoheterotrophs, harbour a higher proportion of fungal plant pathogens and saprotrophs, and have less beneficial fungal plant symbionts compared to woodlands and extensively-managed grasslands. Spatial patterns of microbial communities and predicted functions are best explained when interactions among the major determinants (vegetation cover, climate, soil properties) are considered. We propose guidelines for environmental policy actions and argue that taxonomical and functional diversity should be considered simultaneously for monitoring purposes.