Continental-scale drivers of soil microbial extracellular polymeric substances

Continental-scale drivers of soil microbial extracellular polymeric substances | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Continental-scale drivers of soil microbial extracellular polymeric substances Wolfgang Wanek, Ke Shi, Qing Zheng, Baorong Wang, Lisa Noll, Shasha Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6279309/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Extracellular polymeric substances (EPS) are a vital component of microbial residues which contribute to soil organic carbon (SOC). However, despite various conjectures and hypotheses regarding soil EPS controls, empirical research and experimental evidence to validate these theories have remained highly limited. In this study, we addressed this knowledge gap by conducting extensive soil sampling across Europe, encompassing diverse climates and bedrock and land use types, to systematically investigate soil EPS contents and large-scale controls. We found that bedrock and land use significantly influenced the soil EPS concentration, the contribution of EPS-carbon (C) to SOC, as well as the microbial EPS production efficiency. The average soil EPS concentration was 956 ± 55 µg g⁻¹ soil (n = 92 sites), with EPS-C contributing on average 1.6 ± 0.1% to SOC. Soil EPS concentrations were significantly higher on carbonate bedrock than on silicate and sedimentary geologies. In terms of land use, grassland soils had significantly higher EPS concentrations compared to cropland soils but did not differ from woodland soils. Further detailed investigations of proximate soil physicochemical drivers of EPS content across the transect showed slightly different drivers for EPS polysaccharides and EPS proteins. For instance, EPS polysaccharides were affected by bedrock but not by land use, while the pattern was inverse for EPS proteins. Microbial EPS production efficiency, which expresses the EPS-C content per microbial biomass C, was significantly negatively correlated with microbial carbon use efficiency, reflecting the trade-off between C allocation for growth and extracellular production. EPS production efficiency increased under harsh environmental conditions (e.g., low soil moisture content, high drought index), but was unaffected by pH extremes. On a large scale, soil EPS accumulation was promoted by its production efficiency and by soil factors promoting the sorption and stabilization of EPS, such as clay content, exchangeable Ca and Fe oxides. These findings underscore the significant yet overlooked role of EPS as a critical component of the soil-stable C pool, as it influences microbial C allocation and SOC stabilization and should be further studied to better understand soil C cycling. Earth and environmental sciences/Ecology/Ecosystem ecology Earth and environmental sciences/Ecology/Biogeochemistry/Carbon cycle Earth and environmental sciences/Ecology/Biogeography Biofilm global climate change management microbial residues parent rock Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights Contents of soil extracellular polymeric substances (EPS) were measured across Europe. EPS-C contributes 0.3 to 3.9% of SOC across Europe. Bedrock and land use affect EPS concentration and its contribution to SOC. Water deficit and carbon limitation stimulate EPS production efficiency. EPS-C increases with soil properties promoting sorption and stabilization. 1 INTRODUCTION Soil microbial residues are essential elements of the stable carbon (C) pool, and can be classified as cellular and extracellular residues 1 , 2 . Cellular residues are widely quantified using amino sugars as biomarkers. They have been extensively investigated, are reasonably well understood 3 , 4 , and contribute ~ 30%-60% of the total soil organic carbon (SOC) 3 . However, our knowledge of extracellular residues such as microbial extracellular polymeric substances (EPS) and their contribution to SOC remains highly limited 2 . EPS is a natural mixed polymer secreted by microorganisms that supports them to resist environmental stresses and absorb nutrients and other resources 5 , 6 . The primary components of EPS are polysaccharides (exopolysaccharides) and proteins (exoproteins) 5 , 7 , 8 . EPS polysaccharides are composed of neutral sugar, amino sugar, and sugar acid monomers, are highly viscous, and therefore facilitate the attachment of cells to the surfaces of soil particles 9 , 10 , 11 . EPS proteins are comprised of proteinogenic amino acid monomers that perform more complex functions, such as providing structural and enzymatic function and enabling cell-to-cell communications in response to environmental signals 12 , 13 . EPS polysaccharides are important backbone components of biofilms, the strength and structural diversity of which are promoted by EPS proteins. Together EPS polysaccharides and EPS proteins eventually embed microbial cells and consortia to form millimeter-thick biofilms in aquatic systems while the extent of biofilm formation in soils is still debated 9 , 14 , 15 . Independent of soil biofilm occurrence or not, EPS is not only beneficial for the life and survival of soil microorganisms but also benefits the soil structure 16 . For example, the high viscosity and water retention capacity of EPS facilitate soil particle binding and aggregation and improve soil moisture and soil structure 7 . Given the potentially outstanding role of EPS for microbial function and SOC dynamics, it is interesting to note that quantitative measurements of soil EPS contents remain constrained. So far, only about a dozen (~ 10) sites differing in climate and geology have been tested globally, with limited measurements primarily focused on differences in land use 16 , soil types 17 , and plantation ages 2 . Therefore, large-scale patterns and controls of EPS dynamics, including their effects on microbial necromass and SOC stabilization, remain largely unconstrained. The contribution of EPS to SOC depends on (i) the microbial secretion of EPS and (ii) its long-term protection by binding with soil particles 2 , 18 . The secretion of EPS typically occurs under specific environmental conditions and can be approximated by microbial EPS production efficiency. The EPS production efficiency represents the amount of EPS secreted by microorganisms in soils and is calculated as EPS: microbial biomass carbon (MBC) ratio 17 . For instance, in nutrient-rich soils, microorganisms tend to stimulate EPS secretion to store it as an additional C resource 7 . This means that when soil microbial biomass is high and microbial C and nutrient demands are met, microorganisms promote the secretion of EPS after adequate self-synthesis 7 . Moreover, when subjected to environmental stresses such as drought, high salinity, pH extremes, and C or nutrient limitations, microorganisms secrete EPS to retain water 19 , 20 , to provide ion and proton buffering, and to acquire nutrients increasing the metabolite return on investment for the exoenzymes secreted 21 . This indicates that the soil environment and C and nutrient availability are significant triggers for EPS secretion by microorganisms 7 . Finally, when microorganisms reproduce and microbial communities expand, microbial cells can increase their adhesion capacities to the surfaces of soil particles by secreting EPS, which not only strengthens their attachment but also serves as an effective barrier to block invasion from competing microorganisms 6 , 17 . In summary, in soils microbial EPS secretion is contingent on microbial biomass synthesis (e.g., efficacy of microbial C and N use, microbial growth) and environmental stress 7 , which depend on soil pH, water content, and cation exchange capacity (CEC) 19 , 20 and on the supply of soil nutrients (e.g., SOC, N, and phosphorus (P)) 21 , 22 , but also are related to other factors such as population survival strategies 17 . Conversely, soils enhance the physical protection of EPS that is secreted by microorganisms 7 , 16 , where mineral binding of EPS allows for its long-term preservation in the soil and contributes to the SOC pool 2 . Soil mineral composition and particle size distribution therefore can affect the stabilization of EPS. For instance, silt- and clay-rich soils can tightly bind EPS, which reduces the EPS decomposition rate and enhances its preservation in the soil 14 , 23 . Furthermore, soil minerals such as Fe- and Al-oxyhydroxides and exchangeable Ca and Mg interact with EPS functional groups to strongly bind it to soil particles, also causing soil aggregation and improving the stability of soil structures, and thereby stimulating further accumulation of EPS 24 , 25 . In conclusion, both EPS secretion and its protection by soil determine the contribution of EPS to the SOC pool. Various hypotheses have been put forward regarding the controls of EPS secretion and accumulation. Land use change often leads to the simultaneous modification of multiple environmental factors 26 , affecting microbial activity and potentially EPS production. Soils derived from different bedrock types offer varying levels of protection for EPS 27 . However, there is no in-depth experimental data available to support or falsify any of these hypotheses. Therefore large-scale and in-depth investigations into the secretion and accumulation of EPS in soils as influenced by bedrock and land use are crucial for accurately assessing the dynamics and roles of the EPS-C pool. Here, we conducted large-scale soil sampling on a transect that spanned ~ 5,500 km across the entire European continent, quantified EPS in soil samples from 92 different sites 28 , and compiled a very broad range of environmental and biotic parameters (i.e., climate, plant, soil, and microbial factors). Thereby, this study addressed four major research questions: (1) What is the distribution of EPS concentration in soils on the continental scale, and what are its physicochemical and biological drivers? (2) Do the two primary components of EPS, EPS polysaccharides and EPS proteins, follow the same or different drivers, in the latter case causing EPS composition to diverge? (3) What is the contribution of EPS-C to the SOC pool, and what causes potential differences at the continental scale? (4) Finally, how does the microbial EPS production efficiency change on a continental scale, and how do environmental conditions such as C and nutrient availability, pH extremes, and soil water deficit affect this productivity? By exploring these questions, we aim to gain a deeper understanding of the role of EPS across different soil ecosystems and its contribution to the soil C cycle. 2 RESULTS 2.1 Dynamics and drivers of EPS concentrations across different bedrock and land-use types on a continental scale Across the European transect, total soil EPS concentrations (the sum of concentrations of EPS polysaccharides and EPS proteins) were found to range from 149 µg g − 1 to 2495 µg g − 1 soil, with a grand mean of 956 ± 55 µg g − 1 soil (n = 92), which is higher than the previously reported EPS concentrations in soils (mean: 393 µg g⁻¹ soil) (Figs. 1 , 2 ; Supplementary Table S1 ). Bedrock type and land-use type strongly influenced the concentrations of EPS (Figs. 2 A, B; Supplementary Table S2 ). The concentrations of EPS were significantly higher in soils on carbonate than on silicate and sediment bedrock, albeit the latter two did not differ significantly (Fig. 2 A). The EPS concentrations were significantly higher in grasslands than in croplands but both did not differ from woodlands (Fig. 2 B). More detailed analyses of bedrock, land-use types, and environmental factors influencing EPS variables can be found in Supplementary Tables S3-14. To test for major environmental and microbial drivers of soil EPS accumulation at this large scale, we performed extensive correlation analyses. We investigated the relationships between soil total EPS and climate and geographic parameters (e.g., elevation, latitude, longitude, ADI, MAT, MAP, and PET), plant parameters (e.g., FRB, root C, root N, and root C:N ratio), soil physicochemical properties (e.g., texture (sand, silt, and clay), soil minerals (Ca e , Mg e , Fe c , Fe d , Al d , Fe o , and Al o ), soil nutrients (SOC, TN, TP, soil C:N, C:P, and N:P ratios, NH 4 + , NO 3 − , TDN, DOC, and TOP), soil pH and CEC, and soil microbial parameters (e.g., MBC, MBN, MBP, MBC:MBN, MBC:MBP, and MBN:MBP ratios, CUE, NUE, Cgrowth, qGrowth, turnover time), soil enzyme activities (BG, NAG, LAP, VectorL, and VectorA), and microbial community composition (GPB, GNB, fungal PLFA, bacterial PLFA, total PLFA, and B:F and GPB:GNB ratios). The results indicated that soil EPS concentrations were affected by varied factors, but most strongly by soil physicochemical properties and microbial parameters (Fig. 2 C). Soil total EPS concentrations increased with MAP, and more weakly with ADI and elevation, while there were no significant correlations with latitude, longitude, MAT, and PET (Fig. 2 C). Among the plant parameters, EPS concentrations increased with root N and weakly with FRB, and decreased with root C:N (Fig. 2 C). Among the soil physicochemical properties, EPS concentrations were strongly related to soil C and nutrients, showing a strong positive correlation with TN, positive correlations with SOC, TP, TOP, TDN, and weak positive correlations with the NO 3 − , soil N:P ratio, and NH 4 + , while there were no significant correlations with the soil C:N and C:P ratios, pH, and DOC (Fig. 2 C). Soil texture, minerals, and CEC also had strong effects on soil total EPS concentrations. The content of EPS increased with clay and silt content, was positively correlated with soil Fe and Al oxides and exchangeable cations (Ca e , Mg e , Fe c , Fe d , Al d , Fe o , and Al o ) and CEC, and decreased with the soil sand content (Fig. 2 C). Among microbial parameters, EPS concentration increased with microbial biomass (MBC, MBN and MBP), decreased with MBC:MBN, and was positively correlated with soil enzymes and microbial activities (BG, Cgrowth, turnover time, and VectorL) and weakly negative with qGrowth (Fig. 2 C). On the other hand, other microbial parameters that might be linked to microbial EPS formation were not significantly related in this data set, including MBC:MBP and MBN:MBP ratios, microbial CUE and NUE, LAP, NAG, VectorA, and PLFA-based microbial community composition (Fig. 2 C). 2.2 Roles of environmental factors in driving specific EPS components As the primary components of EPS, we investigated whether environmental drivers of EPS polysaccharides and EPS proteins followed congruent or different patterns. Statistical analysis revealed that bedrock and land use had distinct influences on EPS polysaccharides and EPS proteins across the European transect (Fig. 3 ; Supplementary Table S2 ). Specifically, EPS polysaccharides exhibited significant variations in soils derived from different bedrock types (carbonate > sediment / silicate; Fig. 3 A) but showed no significant changes across different land use types (Fig. 3 B). In contrast, EPS proteins demonstrated significant changes associated with land use type (grassland > cropland / woodland; Fig. 3 D), while no significant differences were observed between soils from different bedrock types (Fig. 3 C). PCA and random forest analyses revealed broad similarities in environmental drivers of EPS components, i.e. microbial factors such as MBN (also: MBC) and qGrowth, along with soil properties such as SOC, soil C:N ratio, Ca e , Fe d , and soil pH, regulated both EPS polysaccharides and proteins (Fig. 4 ). Notably, MBN, Ca e , and SOC emerged as the most critical factors that significantly influenced EPS polysaccharides, while microbial biomass (MBN) was the single most important driver of EPS proteins (Fig. 4 ). However, we also found distinct patterns among driving factors between EPS components: climate factors (ADI), plant traits (root C:N ratio), and the soil clay content influenced EPS polysaccharides, while the plant traits (FRB) and microbial factors (Cgrowth and BG) affected EPS proteins (Fig. 4 ). At the same time, both EPS compounds were strongly positively related to soil water holding capacity (particularly EPS polysaccharides), indicating that EPS promotes soil water holding capacity more than bulk SOC or texture do (Fig. 4 ). 2.3 EPS-C and its contribution to SOC compared with MNC Across the European continent, we found EPS-C concentrations in soils that ranged from 0.06 g C kg⁻¹ − 1.07 g C kg⁻¹ soil, with an average of 0.41 ± 0.02 g C kg⁻¹ soil (Figs. 5 A, B). Notably, EPS-C contributed 0.3% − 3.9% to the SOC, with average contributions of 1.6 ± 0.1% (Figs. 5 C, D). The contributions of EPS-C to SOC varied significantly across different bedrock and land use types (Figs. 5 C, D; Supplementary Table S2 ). The contributions of EPS-C to SOC followed the pattern: carbonate / sediment > silicate (Fig. 5 C). In terms of land use, EPS-C contributed 2.0% to SOC in croplands and grasslands, which was significantly higher than the contribution of EPS-C to SOC of 1.2% in woodlands (Fig. 5 D). Furthermore, we found that across the European continent MNC contributed 20.9% to SOC, with BNC contributing 11.5%, and FNC contributing 9.4% (data not shown). The contribution of MNC to SOC was therefore nearly ten times that of EPS-C (Fig. 5 ). In addition, notably strong positive correlations were observed among SOC, MBC, MNC, and EPS-C, underscoring their interconnected roles within soil C dynamics (Figs. 5 , 6 ). 2.4 EPS production efficiency and its relationship with environmental factors Across the European transect, we observed significant variations in EPS production efficiency between soils derived from different bedrock types and from different land uses (Fig. 7 ; Supplementary Table S2 ). EPS production efficiency declined from sediment / carbonate > silicate (Fig. 7 A), while in terms of land use types, it was ranked as cropland > grassland > woodland (Fig. 7 B). Correlation analysis revealed significantly negative associations between EPS production efficiency and microbial factors (Cgrowth and CUE) but positive associations with VectorL (Fig. 7 C). Soil factors such as clay and silt content positively affected EPS production efficiency while others were strongly negatively associated such as soil C:N ratio and sand content (Fig. 7 C). Further potential drivers such as climate factors (ADI), soil factors (e.g., soil C:P ratio, SMC, and DOC) and microbial factors (qgrowth, MBC and MBP) showed weak negative correlations with the EPS production efficiency (Fig. 7 C) while other soil factors (e.g., Ca e , Fe c , Fe d , and CEC) and microbial factors (e.g., turnover time and MBN: MBP ratio) exhibited weak positive correlations with the EPS production efficiency (Fig. 7 C). Correlations between EPS production efficiency and soil pH or FRB were not significant (Fig. 7 C). To assess direct versus indirect effect pathways between environmental drivers and soil EPS-C content, and to evaluate the importance of these effects via driving EPS production efficiency or EPS stabilization, we performed SEM analysis (Fig. 8 ). Soil physicochemical variables (a composite factor negatively loaded by clay, Ca e , and Fe d , and SOC, Supplementary Fig. S1 ) had a direct negative effect on EPS-C. This aligns with expectations, as higher clay and Ca e contribute to EPS-C accumulation and stabilization at lower PC (soil) values. In contrast, the microbial composite factor (negatively loaded by CUE, Cgrowth, qGrowth, and MBN, Supplementary Fig. S1 ) exerted a direct positive effect on EPS production efficiency and a direct negative effect on EPS-C (Fig. 8 ). This is consistent with the observed trade-off between microbial growth and EPS production efficiency, as low microbial biomass and growth (at high PC (microbe) values) are associated with high EPS-C production efficiency. Moreover, EPS-C scaled positively withEPS production efficiency, showing that EPS production efficiency promotes EPS accumulation. The direct negative effect of microbial factors (low PC (microbe) implying high microbial CUE, biomass and growth) reflects the findings of Fig. 6 , the positive interrelationship between MBC, EPS-C, and SOC, where C rich conditions come with high microbial growth, CUE, MBC, EPS-C and SOC. Additionally, plant factors (here: FRB) directly affected EPS-C (Fig. 8 ). Climate factors (here: ADI), as well as the factor land use type, indirectly affected the EPS-C and EPS production efficiency by impacting plant, soil, and microbial factors (Fig. 8 ). 3 DISCUSSION 3.1 Variations in EPS concentrations and compositions: Roles of bedrock and land use types This study found a high spatial variability in soil EPS concentrations across the European transect (minimum 149 µg g − 1 / maximum 2495 µg g − 1 (16 x the minimum value); Fig. 1 ). EPS polysaccharide concentrations ranged from 79.6–1818 µg g⁻¹ (mean: 624 µg g⁻¹), while those of EPS proteins ranged from 14.7–825 µg g⁻¹ (mean: 332 µg g⁻¹). On average, polysaccharides therefore accounted for 63% of the total soil EPS, with proteins contributing the remaining 37%. Other EPS compounds like lipids and eDNA were not determined here, but commonly comprise only a minor fraction of EPS (< 10%, 46 , 47 . There are only a few reports of soil EPS concentrations available in the literature, which we compiled in a data synthesis effort (Supplementary Table S1 ). Total EPS, EPS polysaccharides, and EPS proteins levels in our study were higher than those previously reported at ~ 10 sites differing in climate and geology: total EPS (mean: 393 µg g⁻¹), EPS polysaccharides (mean: 266 µg g⁻¹), and EPS proteins (mean: 126 µg g⁻¹) 2 , 16 , 17 , 39 , 40 , 48 . This difference may be due to variations in geographical area, soil management practices, microbial communities, climate, and soil organic matter content. Among the three bedrock types, EPS concentrations were significantly higher in carbonate soils than in soils on silicate and sedimentary bedrock. Generally, soil EPS concentrations are governed by the dynamic balance between its generation, stabilization and decomposition (Shi et al., 2024). Interestingly, while carbonate soils exhibited the highest EPS concentrations, EPS production efficiencies followed a different pattern, with soils on sedimentary rocks demonstrating the highest EPS production efficiency. This suggested that high EPS concentrations in carbonate soils may depend on their stabilization processes 49 , whereas the EPS in sedimentary rocks (despite their high EPS production efficiencies) may be rapidly decomposed due to the presence of adapted microbial communities efficiently utilizing EPS 50 . Comparing the three studied bedrock types, carbonate rocks are rich in base cations such as Ca 2+ and Mg 2 + 51 , weather rapidly 52 and have neutral soil pH (Supplementary Table S15) 53 , 54 . This provides a suitable living environment for microorganisms, but also promotes the adsorption of EPS by minerals and thereby aids with its stabilization and accumulation 55 , 56 . Sorption and stabilization controls on EPS concentrations were also evident from positive correlations between EPS concentration and Ca e , clay, silt and Fe d , and these properties were also highest in carbonate soils. Sedimentary rocks are rich in various nutrients (such as NH 4 + ) 57 that may stimulate the microbial secretion of EPS. This, in turn, can stimulate microbial resource utilization efficiency and microbial activities through a positive feedback mechanism 6 . However, greater microbial activities may result in the recycling and degradation of EPS 50 , thus, eventually limiting its accumulation 58 . In the studied soils, those deriving from sedimentary bedrock were intermediate in properties promoting EPS stabilization (e.g. clay, silt, Ca e , Fe d ) but had highest EPS production efficiencies, which may explain the second highest soil EPS concentrations. In contrast, silicate rocks, due to their slower weathering rates and limited nutrient release capacities, may lack certain essential soil microbial elements, thus, limiting microbial activities and the generation of EPS 59 , 60 . Silicate soils had the lowest levels of potentially EPS-binding properties (clay, silt, and, Ca e ), and the lowest EPS production efficiencies which were accompanied by lowest soil pH values eventually reducing microbial activities and decomposition processes. The strong effects of bedrock on EPS concentrations where therefore driven by differences in bedrock chemistry affecting (i) soil pH and nutrient supply modulating microbial EPS production and (ii) soil texture and mineralogy controlling the EPS binding capacity and stabilization. Among the three land-use types, EPS concentrations were highest in grassland soils compared to cropland and woodland soils. Interestingly, as observed with the different types of bedrock, the rankings for EPS production efficiencies and EPS concentrations did not align consistently across land use types. The EPS production efficiencies of croplands and grasslands were similar and higher than those of woodlands. This divergence may have been attributed to large differences in plant belowground C inputs (root system development) accompanied by external nutrient inputs (fertilization) and disturbance (tillage, grazing) in agricultural systems (Supplementary Table S16) 61 . Soil EPS increased with fine root biomass along the European transect, indicating that part of the land use driven patterns in EPS concentration were plant input driven. Plant fine root biomass was similar in grasslands and woodlands, and much greater than in croplands, but root morphologies also differ dramatically 61 . Grass roots are known to be much thinner and have greater specific root length (length per mass of fine roots), globally leading to much greater fine root length and fine root area in grasslands than in forests, where trees produce thicker roots with less fine root length and area 61 . Thinner roots turnover faster and thereby produce larger root necromass inputs in topsoils which adds to greater root exudate inputs in grasslands with their markedly greater root surface area 61 . Root turnover and exudates provide rich C sources for microorganisms 62 that can significantly increase microbial EPS. Despite lower plant root C inputs in croplands due to the reduced root system development in cropping systems, on a microbial biomass basis, EPS production efficiency was highest, likely due to relaxed nutrient constraints of microbial activities through long-term fertilization. However, low plant root C inputs in croplands caused the lowest MBC levels and therefore, even at the highest microbial EPS production efficiencies, the lowest EPS concentrations. In addition, anthropogenic activities such as frequent tillage, disrupt soil structure and aggregates, thereby reducing soil organic matter content 63 , and the use of pesticides, which strongly affect soil microbial communities 64 , negatively impacts the biomass and diversity of microbial communities. These factors contribute to reduced formation and stabilization and accelerated degradation of EPS 65 . There are substantial differences in the composition and function of soil microbial communities under different land use types 69 , which may also lead to differences in EPS formation and decomposition 70 . Nevertheless, these microbial diversity aspects were not effectively investigated in this study and warrant further exploration in the future. In conclusion, it is the interplay between plant inputs and anthropogenic management which cause land use driven differences in EPS concentrations. A more detailed analysis revealed that it was primarily bedrock type that affected the concentration of EPS polysaccharides, while the type of land use influenced the concentration of EPS proteins (Fig. 3 ). This difference reflects the different elemental demands (C, or C plus N) and controls of the biosynthetic pathways of EPS polysaccharides and EPS proteins in microbial ecosystems 6 , and the different interaction mechanisms with organic and mineral surfaces of EPS polysaccharides and EPS proteins promoting their binding, stabilization and accumulation. The phenomenon of differential controls of EPS polysaccharide and EPS protein accumulation is therefore triggered by the selective regulation of microbial metabolic dynamics leading to EPS compound biosynthesis and secretion and differences in their stabilization mechanisms. This differential accumulation mechanisms are thought to have strong repercussions on the properties of EPS like its stickiness and adhesion, mechanical stability (Supplementary Fig. S2 ), ion binding capacity and hydration potential which are related to the ratio of EPS protein: EPS polysaccharides 71 , 72 . However, studies on the EPS protein-to-polysaccharide ratio have primarily been conducted in marine environments, with no research available in the context of soils. 3.2 EPS-C contribution: its role in soil organic carbon storage In this study, we found that the average EPS-C concentration in soils across the European transect was 0.41 g C kg⁻¹ (0.06–1.07 g C kg⁻¹), which contributed on average 1.6% to the SOC (0.3% − 3.9% range). These are the first estimations of EPS-C contents of soils, calculations which were not performed previously due to lack of representative data on the C content of EPS polysaccharides (and EPS proteins). Based on literature data and own measurements of model EPS polysaccharide compounds, we derived an average 39.1% C for EPS polysaccharides. In addition, a representative EPS protein C content was derived from genomic data, here the yeast Saccharomyces cerevisiae , to predict the amino acid compositions of all potentially expressed polypeptides, which produced a protein C content of 50.7% 44 . The contributions of EPS-C to SOC are low (~ 2% of SOC) yet these values clearly represent underestimates as the CER extraction of EPS was previously chosen to extract a relatively pure EPS fraction with little humic interference and causing little lysis of soil microbes 39 , 40 . The gentle CER extraction procedure therefore certainly does not fully extract soil EPS 39 . Compared to harsher, more complete yet more destructive extraction protocols in soils CER only extracts 1/2 to 1/20 of the total EPS polysaccharides and EPS proteins 39 , 40 , 73 . Clearly more comprehensive protocols need to be developed to account for incomplete EPS extraction of soils and to arrive at fully quantitative EPS estimates for soils. Although EPS-C contributes little to the total SOC (not accounting for incomplete extraction), EPS plays a critical role in soil ecosystems 16 . As a direct product of microbial anabolic metabolism, EPS-C represents a highly active soil C pool and promotes SOC storage and stabilization by improving soil structure through aggregation and via promoting the formation of mineral-organic complexes 16 . Furthermore, EPS-C has an amplifying effect on the soil C cycle and soil functions 7 , and plays key roles in soil erosion resistance. One important functional aspect of EPS was shown here to be the key positive driver of soil water holding capacity, compared to weaker associations to SOC or soil texture (in the absence of direct measurements of soil porosity and pore size distribution). Its regulatory impacts on SOC and soil water storage are especially important in view of addressing climate change in agricultural systems 74 . In contrast to extracellular microbial residues, the contribution of cellular residues in the form of MNC to SOC was ~ 10 x that of estimated EPS-C on a continental scale, which is mainly due to differences in C allocation to extracellular versus cellular residue formation mechanisms and differences in their lability or stability 2 . EPS is an active pool that is selectively secreted by microorganisms in response to adverse environmental conditions, being limited in quantity, but otherwise exhibits relatively rapid decomposition 5 , 20 . As the inevitable product of microbial turnover and death, MNC is rather recalcitrant against degradation, and by binding to soil mineral surfaces is preserved in soil for prolonged periods to become an important source of SOC 3 , 75 , 76 . This passivity and stability makes the contribution of MNC to SOC significantly higher than that of EPS-C. Interestingly, we found a strong positive relationship between EPS-C and MNC, which may be due to them being both outputs of microbial anabolic metabolism, active microbes producing EPS and replicating, with microbial cells turning into necromass by different processes 77 . In addition, EPS is produced by microbes as a glue for attachment of their cells and colonies to soil surfaces 46 , which after death cause that necromass fragments are cemented via EPS to soil minerals or organic surfaces which has been shown microscopically 78 , 79 . This also promotes the collinearity between EPS-C and MNC, and with SOC. Overall, EPS therefore likely plays a double ‘sticky’ role, on the one hand promoting cell and necromass attachment to (mineral) surfaces stabilizing SOC via necromass accumulation, and on the other hand by stimulating the aggregation of particulate organic matter and of mineral associated organic matter (including MNC-laden mineral particles) into micro- and macroaggregates, thereby also promoting SOC stabilization on a higher level. In summary, EPS-C is expected to play a crucial role in the soil C cycle. Although its direct contribution to SOC is currently estimated to be limited, it can indirectly increase SOC storage by promoting surface attachment of microbial biomass and necromass, and by stimulating aggregation and soil structure formation 80 . Therefore, management recommendations that encourage microbial EPS secretion through sustainable land-use practices can, in turn, enhance soil C and water storage and improve soil health and soil ecological functions. 3.3 Environmental drivers of EPS production efficiency and accumulation Microbial EPS production efficiency is calculated by standardizing EPS-C concentrations to microbial biomass C 48 . This EPS production efficiency may be used to assess the potential of microorganisms to secrete EPS under various environmental conditions 48 . As mentioned above, the accumulation of EPS in soils is contingent on the dynamic balance between its generation, stabilization and degradation. Distinct bedrock and land-use types have shaped diverse ecological environments and strongly influenced the production and accumulation of EPS. A more detailed statistical analysis using SEM revealed that climate, plant, soil, and microbial factors drive the synthesis (EPS production efficiency) and the accumulation of EPS from multiple perspectives. Firstly, environmental conditions are key factors that determine whether and to what extent microorganisms secrete EPS 7 , 8 . Microorganisms produce EPS under harsh environmental conditions, which helps them adapt and survive 9 , 81 . For example, microorganisms increase EPS secretion to retain water, to maintain their water balance, and to protect cells from drought damage 82 , 83 . Although this has not yet been demonstrated for soil microbial communities, we demonstrate this adaptive phenomenon on a large scale i.e. reflected by increasing EPS production efficiency with increasing aridity (indicated by a decrease in the aridity index, ADI) and with worsening drought conditions. This same trend was apparent by the negative correlation between EPS production efficiency and soil moisture content across Europe. Soil microbial communities therefore invest into EPS secretion to effectively retain soil moisture under conditions of increasing water deficit, based on the excellent water retention properties of EPS. Importantly, soils with high EPS content likewise were characterized by high soil water retention capacity across this transect. Environmental pH extremes, particularly high acidity were also documented to induce EPS production in microbes, where the EPS matrix buffers excessive proton concentrations via their cation-exchange capacity 7 , 84 , 85 . In this study, we found a curvilinear relationship between EPS production efficiency and soil pH (Supplementary Fig. S3 ; ranged from 3.6 to 8.9), where EPS production efficiency peaked at neutral pH and not at lowest pH values. This means that pH extremes are not main controlling factors of EPS production efficiency in soils in the studied pH range. Given the limiting C resources available to microbes in soils, microbes may also need to prioritize C allocation to specific processes, which may lead to physiological trade-offs in microbial C allocation 86 , 87 . For instance under conditions of high growth and high CUE, microbes may prioritize C allocation to biosynthesis for growth processes, eventually reducing the C flow to EPS formation and secretion. The strategy adopted by soil microorganisms to secrete EPS is therefore likely strongly influenced by their physiological state and by C trade-offs governing (extra)cellular C allocation. In this study, EPS production efficiency scaled negatively with microbial growth (Cgrowth), and therefore also negatively with microbial CUE and with microbial biomass. This provides the first and strong hint for a microbial trade-off in C allocation between EPS secretion and growth processes and CUE, based on in vivo process measurements. EPS production efficiency was highest under stressful conditions, where microbial growth, microbial biomass and CUE were low. The production of EPS represents an energetically costly investment that microbes prioritize for maintaining survival under harsh conditions 7 , 22 . This trade-off also allows to test the EPS production relationship to resource availability. For instance, we found that EPS production efficiency was high when microbial growth was low due to microbial C limitation as indicated by high enzyme vector length and low DOC. Different from aquatic systems, EPS production is obviously not stimulated as an intermittent extracellular store of C and N by secreting EPS polysaccharides and EPS proteins 8 , under ample C and nutrient supply in soils. Instead, C and nutrient limitation promote EPS production in aquatic and soil microbial communities, though the mechanistic underpinning remains to be resolved in soils. In aquatic biofims resource limitation has been called for maximizing the microbial return on investment for exoenzymes 6 , 7 . Finally, microbial community composition may affect EPS production 22 and degradation 17 , as it is known that different taxa show very large differences in their genomic potential for EPS secretion and EPS degradation. Diverse microbial populations may indeed adopt different EPS-related strategies in response to environmental stresses and to optimize the use of resources 88 , 89 . Certain microbial populations may be more inclined to secrete EPS 90 as they possess more efficient EPS biosynthesis and secretion capabilities. Key microbes for EPS secretion and EPS degradation in soils can only be identified by deep shot-gun metagenomic sequencing and by sequencing of key functional genes involved in EPS production and decomposition processes. However, in this study, we primarily relied on PLFA analysis to broadly fingerprint the soil microbial communities for large groups of microbes such as fungi, and gram-positive and gram-negative bacteria, with which we did not find a link between microbial community structure and EPS content or EPS production efficiency. Whether freshly produced EPS is retained to accumulate in soils or degraded to be microbially consumed is an important factor that influences its accumulation and soil function. Soils with high clay and silt contents are more conducive to the long-term accumulation and stabilisation of EPS 23 , whereas in soils with a higher sand content, EPS may be more prone to recycling and be lost due to their weaker binding forces 91 . The presence of metal ions (e.g., Ca²⁺) and Fe oxy(hydr)oxides in the soil can assist in the accumulation of EPS through enforcing adsorption and binding 92 , which reduces its degradation, while promoting its accumulation 93 . The prevalence of this abiotic sorptive control of EPS was highlighted by SEM analysis, where soil factors dominantly explained EPS-C accumulation. In conclusion, the driving mechanisms behind differences in EPS production efficiency and EPS accumulation involve the dynamic interplay between the environment, resource availability, microbial metabolic strategies, and soil properties. It is essential to improve EPS quantification in soils, addressing potential underestimations by current methods. Further research should focus on understanding the dynamics of EPS formation, stabilization, and turnover using isotope tracer methods. Additionally, linking EPS content and dynamics to its functional implications in soils—including aggregate formation, carbon cycling, water retention, and microbial stress amelioration—will be critical for advancing our knowledge of soil microbial ecology and ecosystem resilience. 4 MATERIALS AND METHODS 4.1 Site description To investigate the distribution of EPS and its drivers on a continental scale, 92 sites were sampled across the European continent (36º24 '45' N- 71º01 '56' N, 8º31 '56' W- 29º34 '60' E). Sampling sites extended from the Mediterranean (Southern Spain) to the subarctic (Northern Norway), and from the Atlantic coast (Western Portugal) to the dry continental steppes (Eastern Romania). The sampled soils were distinct in terms of their bedrock and land use types. Geographical and climate data (including latitude, longitude, elevation, mean annual temperature (MAT), mean annual precipitation (MAP), potential evapotranspiration (PET)), and bedrock and land use types were described earlier in detail by Noll et al. 28 . In brief, MAT of the sampling sites ranged from − 3 to 18 ºC, while MAP ranged from 415 to ~ 1400 mm yr - 1 . An aridity index (ADI) was calculated as MAP divided by (MAT + 33) according to Quan et al. 29 . Bedrock types were aggregated into three large groups for statistical analyses: carbonate (e.g., dolomite, limestone, and marl), sediment (e.g., flysch, molasse, till, and fluvial sand), and silicate (e.g., plutonic, igneous, and metamorphic formations). Where possible, samples were collected from all three land use types (shrubland/forest, grassland, and cropland) in close vicinity, the three sites then lying within 1–2 km distance in such a land use cluster with the same geology and climate. In the following, we used “woodland” encompassing subarctic tundra, open woodlands, and forests. 4.2 Sampling During the peak of the growing season (May-August 2017), bulk samples of mineral topsoil (0–15 cm depth) were extracted using a soil corer (Ø 5 cm). Each soil sample was composited from five replicates. In total, soils from 92 sites were sampled, with 23 site clusters (69 soils) including woodland, grassland, and cropland soils 28 . The soil samples were homogenized to 2 mm by sieving, and separate aliquots were air-dried or stored under moist conditions at 4°C. Root samples were collected from soil while sieving to determine root biomass. They were then washed and dried in a drying oven at 60°C. 4.3 Soil physicochemical and microbial characterisation Soil texture (sand, silt, and clay content), CEC, and exchangeable Ca 2+ (Ca e ) and Mg 2+ (Mg e ) were determined by the Austrian Agency for Health and Food Safety (AGES) according to European and international standards (ÖNORM). Fe oxyhydroxides and Al oxyhydroxides were determined in acid ammonium oxalate (Fe o and Al o ) and in Na-dithionite extracts (Fe d and Al d ) 30 at the Institute of Soil Research (IBF, University of Natural Resources and Life Sciences, Vienna, Austria). Oxalate-extractable Fe (Fe o ) and Al (Al o ) referred to amorphous Fe and Al oxyhydroxides and Fe bound within organometallic complexes. Fe d minus Fe o represented Fe bound in crystalline oxyhydroxides (Fe c ). The soil water holding capacity (WHC), soil moisture content (SMC), soil pH, SOC, soil total nitrogen (TN), soil total phosphorus (TP), soil organic phosphorus (TOP), dissolved organic carbon (DOC), total dissolved nitrogen (TDN), soil ammonium (NH 4 + ), and soil nitrate (NO 3 − ) were measured as described by Noll et al. 28 . Subsequently, the soil C:N:P ratios were calculated according to the concentrations of SOC, TN, and TP. Fine root biomass (FRB) was obtained for roots < 2 mm diameter. An elemental analyzer was used to determine the root C and N content, and root P concentrations were determined by dry ashing, acid extraction and colorimetric P determination. Root C:N:P ratios were calculated accordingly. Soil microbial community composition was analyzed by phospholipid fatty acid (PLFA) analyses according to Kaiser et al. 31 and Hu et al. 32 , and PLFAs divided into gram-positive bacteria (GPB), gram-negative bacteria (GNB), fungi, and general bacteria. Total PLFA biomass, bacteria to fungi (B:F) ratios, and gram-positive bacteria to gram-negative bacteria (GPB:GNB) ratios were calculated. Microbial biomass C (MBC), microbial biomass nitrogen (MBN), and microbial biomass phosphorus (MBP) were determined via chloroform fumigation extraction 33 , from which the MBC:MBN:MBP ratios were calculated. Microbial N use efficiency (NUE) was measured as described by Zhang et al. 34 , and microbial C use efficiency (CUE) was quantified following Zheng et al. 35 . Underlying processes of microbial growth (Cgrowth), growth normalized to MBC (qGrowth), respiration, and microbial biomass turnover time were also estimated as described by Zheng et al. 35 . Soil enzyme activities, including β-glucosidase (BG), N-acetyl-β-glucosaminidase (NAG), leucine aminopeptidase (LAP), acid phosphatase (AP), exoglucanase-cellobiosidase (CEL), β-xylosidase (BX), and phenoloxidase (POX) were measured as described by Marx et al. 36 and Zheng et al. 35 , and enzymatic vector lengths (VectorL) and vector angles (VectorA) calculated following Moorhead et al. 37 . Soil microbial necromass, including fungal necromass C (FNC), bacterial necromass C (BNC), and total microbial necromass C (MNC), was determined using amino sugar biomarkers as described by Salas et al. 38 . 4.4 Soil EPS analysis Soil EPS was extracted using cation exchange resins (CER) 39 . Soil EPS binds to negatively charged soil surfaces, such as clay minerals and organic matter, through electrostatic interactions involving Ca²⁺ bridging. CER, in its sodium form, exchanges their Na + ions with these divalent cations, weakening Ca 2+ bridging and releasing EPS from the soil matrix. This technique is highly efficient for the extraction of EPS and induces negligible cell lysis, yielding highly pure EPS fractions 39 , 40 , 41 . Prior to EPS quantification, a preliminary experiment was devised to test EPS extraction efficiency with different CER:soil weights across bedrock types, considering the expected larger exchangeable Ca 2+ levels in carbonate soils and the wide range of SOC concentration variations due to the extensive sampling range. Specifically, three carbonate soil samples and three non-carbonate soil samples with high, medium, and low SOC contents were selected. For each sample, 0.5 g, 1 g, 2 g, and 3 g air-dried soil were weighed, and then 10 g of wet CER (soaked in phosphate-buffered saline solution (PBS)) was added for extraction, to demonstrate that the CER method and dosage used are suitable for different types of soil samples with varying SOC contents. This experiment showed that using 1 g of air-dried soil with 10 g of wet CER provided a good balance for determining soil EPS, as 3 g was more effective for polysaccharides and 0.5 g or 1 g for proteins, regardless of bedrock type or SOC level (Supplementary Fig. S4 ). PBS was prepared, the pH adjusted to 7.4 and the solution cooled to 4°C in advance. CER (Amberlite™ IR120 Ion Exchange Resin, Na + form, 15–50 mesh, Sigma Aldrich) was thoroughly hydrated and washed using PBS, exchanging the PBS solution by decanting and adding new PBS for 5–6 times. Aliquots of air-dried soils (1 g) were weighed into 50 ml centrifuge tubes, and amended with 25 ml 10 mM CaCl₂ solution. The suspension was then shaken gently for 30 min, and centrifuged at 4°C at 4000 g for 30 min, after which the supernatant was carefully poured off to remove interfering extractable non-EPS microbial products such as free sugars and amino acids. Subsequently, EPS was extracted by addition of 25 ml PBS and 10 g wet CER, and the mixture was shaken vigorously for 2 h prior to centrifugation at 4°C at 4000 g for 30 min. The supernatant was then filtered through 0.45 µm filters (VWR® Syringe Filters, Nylon, 25 mm, Avantor), and the obtained solution was stored at 4°C and used for the determination of EPS polysaccharides and EPS proteins within 4 d. EPS polysaccharides were measured by acid hydrolysis and anthrone reaction using glucose as a standard 42 . The color reagent was produced by dissolving 0.2 g anthrone in 100 ml concentrated H 2 SO 4 (previously cooled to 0°C). For colorimetric assays, 200 µl of sample, standard or blank was added to 10 ml glass tubes, followed by the addition of 1 ml anthrone reagent. The mixture was then mixed well and heated to 100°C in a water bath for 10 min. After rapid cooling to room temperature, the absorbance was measured at a wavelength of 625 nm. EPS proteins were determined by a modified Lowry method 43 , based on Cu-binding to polypeptides and the Folin-Ciocalteu reaction, but adopted to deal with the humic compound interferences in soil extracts. Bovine serum protein was used as the standard. Samples, standards, and blanks (50 µl) were pipetted into microtiter plates, mixed with 100 µl copper-containing or copper-free NaK tartrate/Na 2 CO 3 -NaOH buffer for 10 min, supplemented with 100 µl of 10-fold diluted Folin-Ciocalteu reagent and after 30 min protein absorbance was measured at a wavelength of 750 nm. Total EPS was calculated as the sum of EPS polysaccharides and EPS proteins. EPS-C was then estimated, where the C content of EPS polysaccharides was determined as an average of 39.1% based on elemental analysis of several exopolysaccharide standards, including hyaluronic acid (37.5%), xylan (40.5%), pectin (38.3%), and arabinogalactan (40.2%), while the C content of EPS proteins was estimated to be 50.7% 44 . The EPS-C:MBC ratio was calculated to represent the EPS production efficiency, whereas the EPS-C:SOC ratio was computed to represent the contribution of EPS-C to SOC. 4.5 Statistical analyses Statistical analyses were performed and visualized using R version 4.1.2 45 . One-way ANOVA analysis was applied to examine the impacts of bedrock and of land use on the target EPS variables, i.e., total soil EPS contents, EPS-polysaccharide and EPS-protein contents, EPS-protein:EPS-polysaccharide ratios, EPS-C contents, the contributions of EPS-C to SOC, and the EPS production efficiency. Subsequently, we used linear regression models to analyze the relationships between EPS variables (total soil EPS content, EPS polysaccharide content, EPS protein content, and EPS production efficiency) and environmental factors. Three models were applied: (1) a simple linear regression to assess the effect of environmental factors on EPS variables, (2) a multiple linear regression with an interaction term to examine the combined effects of environmental factors and land use type, and (3) another multiple linear regression to evaluate the interaction between environmental factors and bedrock type. All analyses were conducted in R using the lm function. Spearman’s correlation analysis and principal component analysis (PCA) were employed to examine the impacts of environmental factors (including climatic, plant, soil and microbial factors) on the target EPS variables, PCA being performed with the R package stats . Random forest models were used to rank the importance of controlling factors of EPS polysaccharides, EPS proteins, EPS-C, the contribution of EPS to SOC and EPS production efficiency (R package randomForest ) (Supplementary Fig. S5 ). Using general linear models we examined the relationships between EPS-C, MNC, MBC and SOC (R lm function). The normality of model residuals was assessed using the Shapiro-Wilk test. To assess the direct and indirect effect pathways between environmental drivers and soil EPS-C content, and to determine whether these effects operate through EPS production efficiency or EPS stabilization, we performed structural equation modeling (SEM) analysis (R package lavaan SEM ). Before constructing the SEM, we first examined collinearity among all environmental variables and removed highly correlated factors to reduce multicollinearity. After this selection process, the retained variables included ADI, FRB, MBN, Cgrowth, qGrowth, CUE, SOC, clay content, soil C/N ratio, Ca e , and Fe d . The environmental variables were grouped into four categories: climate, plant, microbial, and soil. Among them, SOC, soil clay content, soil C/N ratio, Ca e , and Fe d were combined into a soil composite variable, while MBN, Cgrowth, gGrowth, and CUE were combined into a microbial composite variable using PCA. Instead of using only the first principal component (PC1) or the second principal component (PC2) alone, soil and microbial variables were constructed by combining the PC1 and PC2 based on their explained variance, and this was necessary because SEM models using only PC1 or PC2 did not yield satisfactory results (p < 0.05) (Supplementary Fig. S6 ). Using this categorization, we developed an SEM model to explore how these four environmental components, along with bedrock type and land use, may directly or indirectly influence EPS-C content and EPS production efficiency. Model fit was evaluated using multiple fit indices, including the Chi-square test (χ²), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and Akaike Information Criterion (AIC). In the best-fitting model, we calculated direct and indirect pathway coefficients to determine the key drivers of EPS production efficiency and EPS-C variation across climatic, geological, and land-use gradients. Declarations CONFLICT OF INTEREST The authors declare that they have no conflict of interest. AUTHOR CONTRIBUTIONS K.S. drafted the manuscript, conducted laboratory work, and analyzed and interpreted the data. Q.Z., B.W., L.N., S.Z., and Y.H. performed laboratory work, contributed to data analysis, and assisted in manuscript editing. H.R. reviewed and revised the manuscript. W.W. designed the study, interpreted the data, and contributed to manuscript editing. All authors made significant contributions to the manuscript drafts and approved the final version for publication. ACKNOWLEDGMENTS This research was funded in whole or in part by the Austrian Science Fund (FWF) [grant DOI: 10.55776/P28037 ]. For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. This study was further supported by the National Key Research and Development Program of China (No. 2023YFD2200404 and No. 2021YFD2200402/3), and the program of China Scholarship Council (No. 202308320320). DATA AVAILABILITY The data supporting the findings of this study are available from the corresponding author upon reasonable request. References Buckeridge KM, Creamer C, Whitaker J (2022) Deconstructing the microbial necromass continuum to inform soil carbon sequestration. 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Supplementary Files SI.docx Supplementary Fig. S1-S6; Table S1-S2; Table S15-16 TableS3.xlsx Table S3 TableS4.xlsx Table S4 TableS5.xlsx Table S5 TableS6.xlsx Table S6 TableS7.xlsx Table S7 TableS8.xlsx Table S8 TableS9.xlsx Table S9 TableS10.xlsx Table S10 TableS11.xlsx Table S11 TableS12.xlsx Table S12 TableS13.xlsx Table S13 TableS14.xlsx Table S14 Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6279309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":439574251,"identity":"6e9a4595-8e67-48ff-91c1-6579168f96c7","order_by":0,"name":"Wolfgang Wanek","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2178-8258","institution":"University of Vienna","correspondingAuthor":true,"prefix":"","firstName":"Wolfgang","middleName":"","lastName":"Wanek","suffix":""},{"id":439574252,"identity":"20b131b0-f787-4311-bdf6-6eb8d569924c","order_by":1,"name":"Ke Shi","email":"","orcid":"https://orcid.org/0009-0004-5244-2887","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Shi","suffix":""},{"id":439574253,"identity":"4bd4e566-a3d8-42a1-8684-eb92202974fc","order_by":2,"name":"Qing Zheng","email":"","orcid":"","institution":"University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zheng","suffix":""},{"id":439574254,"identity":"59992702-0302-4919-93cd-15ba70f176f0","order_by":3,"name":"Baorong Wang","email":"","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Baorong","middleName":"","lastName":"Wang","suffix":""},{"id":439574255,"identity":"7557df5c-d5dc-4633-a9fe-bfdfb63ea9da","order_by":4,"name":"Lisa Noll","email":"","orcid":"https://orcid.org/0000-0003-3711-1444","institution":"University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Noll","suffix":""},{"id":439574256,"identity":"9cbd3dda-d426-412e-a0cd-414e252551fa","order_by":5,"name":"Shasha Zhang","email":"","orcid":"","institution":"University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"Shasha","middleName":"","lastName":"Zhang","suffix":""},{"id":439574257,"identity":"0f8caae8-ce65-4079-ae5c-56071ba3c0e2","order_by":6,"name":"Yuntao Hu","email":"","orcid":"","institution":"University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"Yuntao","middleName":"","lastName":"Hu","suffix":""},{"id":439574258,"identity":"9723e215-8852-4c27-85fb-1364a8ef6cc4","order_by":7,"name":"Honghua Ruan","email":"","orcid":"https://orcid.org/0000-0002-6075-474X","institution":"Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Honghua","middleName":"","lastName":"Ruan","suffix":""}],"badges":[],"createdAt":"2025-03-21 16:40:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6279309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6279309/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-70068-0","type":"published","date":"2026-03-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80199753,"identity":"bf610e80-d520-4198-80be-47c2ddd22f91","added_by":"auto","created_at":"2025-04-09 06:32:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of EPS polysaccharides and EPS proteins across the European transect and with data derived from previous studies. \u003c/strong\u003eData were collected from six previous studies that used the cation exchange resin (CER) method to extract and quantify soil EPS, totaling 21 sites × land use combinations. The numbers in the figure indicate frequency. Specific data are listed in the supplementary Table S1. EPS, extracellular polymeric substances.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/15db516cff5261435f28b5cf.png"},{"id":80200719,"identity":"7d799dd8-bd33-42d6-925b-b761ba00292a","added_by":"auto","created_at":"2025-04-09 06:40:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plots of total EPS concentrations in soils from different bedrock types (A) and land use types (B), along with a heatmap (C) showing the correlations of total EPS, EPS polysacchararides, EPS proteins and their ratio with climate, plant, soil, and microbial factors. \u003c/strong\u003eSome variables with \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 are not displayed in the heatmap for simplicity. The numbers in the heatmap represent correlation coefficients (\u003cem\u003er\u003c/em\u003e), with those for variables with \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 not displayed. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. EPS, extracellular polymeric substances; EPS PN:PS, EPS protein to EPS polysaccharide ratio; ADI, aridity index; MAP, mean annual precipitation; FRB, fine root biomass; SOC, soil organic carbon; TN, total nitrogen; TP, total phosphorus; Ca\u003csub\u003ee\u003c/sub\u003e, exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e; Mg\u003csub\u003ee\u003c/sub\u003e, exchangeable Mg\u003csup\u003e2+\u003c/sup\u003e; Fe\u003csub\u003ec\u003c/sub\u003e, crystalline Fe oxyhydroxides;\u003cstrong\u003e Fe\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sub\u003e,\u003cstrong\u003e \u003c/strong\u003eFe oxyhydroxides extracted with Na-dithionite; \u003cstrong\u003eAl\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sub\u003e, Al oxyhydroxides extracted with Na-dithionite; \u003cstrong\u003eFe\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eo\u003c/strong\u003e\u003c/sub\u003e, Fe oxyhydroxides extracted with acid ammonium oxalate; \u003cstrong\u003eAl\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eo\u003c/strong\u003e\u003c/sub\u003e, Al oxyhydroxides extracted with acid ammonium oxalate; NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, ammonium; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, nitrate; TDN, total dissolved nitrogen; TOP, total organic phosphorus;\u0026nbsp; CEC, cation exchange capacity; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; MBP, microbial biomass phosphorus; CUE, microbial carbon use efficiency; NUE, microbial nitrogen use efficiency; Cgrowth, microbial growth; qGrowth, microbial growth normalized to microbial biomass carbon; BG, β-glucosidase; NAG, N-acetyl-β-glucosaminidase; LAP, leucine aminopeptidase; VectorL, enzyme vector length; GPB, gram-positive bacteria; GNB, gram-negative bacteria; Bacteria, bacterial PLFA biomass; B:F, bacteria to fungi ratio.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/a60680e3f0ed86a7c561e657.png"},{"id":80200718,"identity":"940be106-05a9-44dc-ad83-909c5bdef46e","added_by":"auto","created_at":"2025-04-09 06:40:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariations in EPS polysaccharides and EPS proteins across different bedrock and land use types. \u003c/strong\u003e* \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. EPS, extracellular polymeric substances.\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/5fa3bb91e52441ab56806290.png"},{"id":80201135,"identity":"97b28a5a-282d-42a3-8921-ad363574ed7a","added_by":"auto","created_at":"2025-04-09 06:48:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA analysis and importance ranking of environmental factors driving EPS polysaccharides and EPS proteins via random forest analysis.\u003c/strong\u003e * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. EPS, extracellular polymeric substances; ADI, aridity index; FRB, fine root biomass; SOC, soil organic carbon; TP, total phosphorus; WHC, water holding capacity; Ca\u003csub\u003ee\u003c/sub\u003e, exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e; \u003cstrong\u003eFe\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sub\u003e,\u003cstrong\u003e \u003c/strong\u003eFe oxyhydroxides extracted with Na-dithionite; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; Cgrowth, microbial growth; qGrowth, growth normalized to microbial biomass carbon; BG, β-glucosidase; VectorL, enzyme vector length; B:F, bacteria to fungi ratio.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/7e7b203a225271598e2b6b37.png"},{"id":80199755,"identity":"8f78114f-1fe3-4873-b1c2-94ecbf961e69","added_by":"auto","created_at":"2025-04-09 06:32:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariations in EPS-C and its contribution to soil organic carbon across different bedrock and land use types. \u003c/strong\u003e* \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. EPS, extracellular polymeric substances; SOC, soil organic carbon.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/45cd7ef2149a6d65ff19c9a6.png"},{"id":80200725,"identity":"b9468bd5-8a4f-4e40-a943-1039accce422","added_by":"auto","created_at":"2025-04-09 06:40:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":86855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear relationships between microbial necromass carbon and extracellular polymeric substance carbon, soil organic carbon, and microbial biomass carbon.\u003c/strong\u003e EPS, extracellular polymeric substances; SOC, soil organic carbon; MBC, microbial biomass carbon, MNC, microbial necromass carbon.\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/4d10d0cc49860d38851e2cbb.png"},{"id":80199788,"identity":"58c2cd57-1a5f-427b-b5de-10ce3acda99c","added_by":"auto","created_at":"2025-04-09 06:33:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":116705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plots of EPS production efficiencies under different bedrock types (A) and land use types (B), along with a heatmap showing the correlations of EPS production efficiency with environmental factors (C). \u003c/strong\u003eSome variables with \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 are not displayed in the heatmap for simplicity. The numbers in the heatmap represent correlation coefficients (r), with those for variables with \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 not displayed. \u0026nbsp;* \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001. EPS, extracellular polymeric substances; MBC, microbial biomass carbon; ADI, aridity index; MAP, mean annual precipitation; FRB, fine root biomass; SOC, soil organic carbon; Ca\u003csub\u003ee\u003c/sub\u003e, exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e; Fe\u003csub\u003ec\u003c/sub\u003e, crystalline Fe oxyhydroxides;\u003cstrong\u003e \u003c/strong\u003eFe\u003csub\u003ed\u003c/sub\u003e, Fe oxyhydroxides extracted with Na-dithionite; DOC, dissolved organic carbon; CEC, cation exchange capacity; SMC, soil moisture content; MBP, microbial biomass phosphorus; CUE, microbial carbon use efficiency; Cgrowth, microbial growth; qGrowth, microbial growth normalized to microbial biomass carbon; VectorL, enzyme vector length.\u003c/p\u003e","description":"","filename":"F7.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/f343e3dc6653c8a79b780531.png"},{"id":80200720,"identity":"8b72e726-258b-4d0a-8334-5a2ff933a963","added_by":"auto","created_at":"2025-04-09 06:40:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":150126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural equation modeling (SEM) results illustrating the effects of bedrock type, land use type, and climate on plant, soil, and microbial factors and thereby on EPS-C and EPS production efficiencies.\u003c/strong\u003e Red lines indicate negative effects and blue lines signify positive effects. Numbers on the paths represent path coefficients (standardized regression coefficients), indicating the strength and direction of the relationships between variables. Bar chart showing the factor loadings of soil and microbial factors on PC1 and PC2. EPS, extracellular polymeric substances; ADI, aridity index; MBN, microbial biomass nitrogen; Cgrowth, microbial growth; qGrowth, microbial growth normalized to microbial biomass carbon; CUE, microbial carbon use efficiency; SOC, soil organic carbon; Ca\u003csub\u003ee\u003c/sub\u003e, exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e; Fe\u003csub\u003ed\u003c/sub\u003e,\u003cstrong\u003e \u003c/strong\u003eFe oxyhydroxides extracted with Na-dithionite.\u003c/p\u003e","description":"","filename":"F8.png","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/7ca0aeb2fd922b61f5b01b70.png"},{"id":106582972,"identity":"9bd8be6e-387f-4e6d-bb16-e9459d76d93c","added_by":"auto","created_at":"2026-04-10 07:06:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2155620,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/1cd18f92-cafd-41ce-80f7-d64dc25affd5.pdf"},{"id":80202259,"identity":"4704dd33-289c-4380-bd1e-83d25ced6727","added_by":"auto","created_at":"2025-04-09 06:56:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":660223,"visible":true,"origin":"","legend":"Supplementary Fig. S1-S6; Table S1-S2; Table S15-16","description":"","filename":"SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/46a66b2bda5190f2f3749e3a.docx"},{"id":80199752,"identity":"a48cdd8d-6df9-4b82-8b6a-6daa74bb1174","added_by":"auto","created_at":"2025-04-09 06:32:59","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12945,"visible":true,"origin":"","legend":"Table S3","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/7e919dffedfd28a12757b8db.xlsx"},{"id":80199759,"identity":"e3dba1ed-b931-4a3a-bf98-155ac795c70a","added_by":"auto","created_at":"2025-04-09 06:32:59","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12592,"visible":true,"origin":"","legend":"Table S4","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/c17b860284812e8aefae4912.xlsx"},{"id":80200722,"identity":"6a352f1d-6a86-4a36-933a-393908154a7b","added_by":"auto","created_at":"2025-04-09 06:40:59","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12524,"visible":true,"origin":"","legend":"Table 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S9","description":"","filename":"TableS9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/d5ac66efad230b65392fbf44.xlsx"},{"id":80199760,"identity":"11752e01-e176-40e1-b826-5675aac04fd3","added_by":"auto","created_at":"2025-04-09 06:32:59","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":17560,"visible":true,"origin":"","legend":"Table S10","description":"","filename":"TableS10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/431cda3d9f08cf85f0d53c8e.xlsx"},{"id":80200736,"identity":"913c2665-0884-405c-b10a-f0a46e929b1c","added_by":"auto","created_at":"2025-04-09 06:41:00","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":17348,"visible":true,"origin":"","legend":"Table S11","description":"","filename":"TableS11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/cb66eb245a425feb13f24ddc.xlsx"},{"id":80199785,"identity":"7159acc5-ffee-4724-83b8-7b7b2bbe1e70","added_by":"auto","created_at":"2025-04-09 06:33:00","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":17302,"visible":true,"origin":"","legend":"\u003cp\u003eTable S12\u003c/p\u003e","description":"","filename":"TableS12.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/0a1bd56db8d62b552a4e4f44.xlsx"},{"id":80199783,"identity":"697cde55-abfb-4ab5-9a81-f491b8e69899","added_by":"auto","created_at":"2025-04-09 06:33:00","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":17498,"visible":true,"origin":"","legend":"Table S13","description":"","filename":"TableS13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/a48345efe15e91d7c8ebaee5.xlsx"},{"id":80200744,"identity":"24dc1855-e386-4354-8cf5-5f490fe33df8","added_by":"auto","created_at":"2025-04-09 06:41:01","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":17427,"visible":true,"origin":"","legend":"Table S14","description":"","filename":"TableS14.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6279309/v1/53cbab8245c99043f9d9a9b2.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Continental-scale drivers of soil microbial extracellular polymeric substances","fulltext":[{"header":"Highlights","content":"\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eContents of soil extracellular polymeric substances (EPS) were measured across Europe.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEPS-C contributes 0.3 to 3.9% of SOC across Europe.\u003c/li\u003e\n \u003cli\u003eBedrock and land use affect EPS concentration and its contribution to SOC.\u003c/li\u003e\n \u003cli\u003eWater deficit and carbon limitation stimulate EPS production efficiency.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEPS-C increases with soil properties promoting sorption and stabilization.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 INTRODUCTION","content":"\u003cp\u003eSoil microbial residues are essential elements of the stable carbon (C) pool, and can be classified as cellular and extracellular residues \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Cellular residues are widely quantified using amino sugars as biomarkers. They have been extensively investigated, are reasonably well understood \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and contribute\u0026thinsp;~\u0026thinsp;30%-60% of the total soil organic carbon (SOC) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, our knowledge of extracellular residues such as microbial extracellular polymeric substances (EPS) and their contribution to SOC remains highly limited \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. EPS is a natural mixed polymer secreted by microorganisms that supports them to resist environmental stresses and absorb nutrients and other resources \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The primary components of EPS are polysaccharides (exopolysaccharides) and proteins (exoproteins) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. EPS polysaccharides are composed of neutral sugar, amino sugar, and sugar acid monomers, are highly viscous, and therefore facilitate the attachment of cells to the surfaces of soil particles \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. EPS proteins are comprised of proteinogenic amino acid monomers that perform more complex functions, such as providing structural and enzymatic function and enabling cell-to-cell communications in response to environmental signals \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. EPS polysaccharides are important backbone components of biofilms, the strength and structural diversity of which are promoted by EPS proteins. Together EPS polysaccharides and EPS proteins eventually embed microbial cells and consortia to form millimeter-thick biofilms in aquatic systems while the extent of biofilm formation in soils is still debated \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Independent of soil biofilm occurrence or not, EPS is not only beneficial for the life and survival of soil microorganisms but also benefits the soil structure \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For example, the high viscosity and water retention capacity of EPS facilitate soil particle binding and aggregation and improve soil moisture and soil structure \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the potentially outstanding role of EPS for microbial function and SOC dynamics, it is interesting to note that quantitative measurements of soil EPS contents remain constrained. So far, only about a dozen (~\u0026thinsp;10) sites differing in climate and geology have been tested globally, with limited measurements primarily focused on differences in land use \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, soil types \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and plantation ages \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Therefore, large-scale patterns and controls of EPS dynamics, including their effects on microbial necromass and SOC stabilization, remain largely unconstrained.\u003c/p\u003e \u003cp\u003eThe contribution of EPS to SOC depends on (i) the microbial secretion of EPS and (ii) its long-term protection by binding with soil particles \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The secretion of EPS typically occurs under specific environmental conditions and can be approximated by microbial EPS production efficiency. The EPS production efficiency represents the amount of EPS secreted by microorganisms in soils and is calculated as EPS: microbial biomass carbon (MBC) ratio \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For instance, in nutrient-rich soils, microorganisms tend to stimulate EPS secretion to store it as an additional C resource \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This means that when soil microbial biomass is high and microbial C and nutrient demands are met, microorganisms promote the secretion of EPS after adequate self-synthesis \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Moreover, when subjected to environmental stresses such as drought, high salinity, pH extremes, and C or nutrient limitations, microorganisms secrete EPS to retain water \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, to provide ion and proton buffering, and to acquire nutrients increasing the metabolite return on investment for the exoenzymes secreted \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This indicates that the soil environment and C and nutrient availability are significant triggers for EPS secretion by microorganisms \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Finally, when microorganisms reproduce and microbial communities expand, microbial cells can increase their adhesion capacities to the surfaces of soil particles by secreting EPS, which not only strengthens their attachment but also serves as an effective barrier to block invasion from competing microorganisms \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In summary, in soils microbial EPS secretion is contingent on microbial biomass synthesis (e.g., efficacy of microbial C and N use, microbial growth) and environmental stress \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, which depend on soil pH, water content, and cation exchange capacity (CEC) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and on the supply of soil nutrients (e.g., SOC, N, and phosphorus (P)) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, but also are related to other factors such as population survival strategies \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConversely, soils enhance the physical protection of EPS that is secreted by microorganisms \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, where mineral binding of EPS allows for its long-term preservation in the soil and contributes to the SOC pool \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Soil mineral composition and particle size distribution therefore can affect the stabilization of EPS. For instance, silt- and clay-rich soils can tightly bind EPS, which reduces the EPS decomposition rate and enhances its preservation in the soil \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Furthermore, soil minerals such as Fe- and Al-oxyhydroxides and exchangeable Ca and Mg interact with EPS functional groups to strongly bind it to soil particles, also causing soil aggregation and improving the stability of soil structures, and thereby stimulating further accumulation of EPS \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In conclusion, both EPS secretion and its protection by soil determine the contribution of EPS to the SOC pool.\u003c/p\u003e \u003cp\u003eVarious hypotheses have been put forward regarding the controls of EPS secretion and accumulation. Land use change often leads to the simultaneous modification of multiple environmental factors \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, affecting microbial activity and potentially EPS production. Soils derived from different bedrock types offer varying levels of protection for EPS \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, there is no in-depth experimental data available to support or falsify any of these hypotheses. Therefore large-scale and in-depth investigations into the secretion and accumulation of EPS in soils as influenced by bedrock and land use are crucial for accurately assessing the dynamics and roles of the EPS-C pool.\u003c/p\u003e \u003cp\u003eHere, we conducted large-scale soil sampling on a transect that spanned\u0026thinsp;~\u0026thinsp;5,500 km across the entire European continent, quantified EPS in soil samples from 92 different sites \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and compiled a very broad range of environmental and biotic parameters (i.e., climate, plant, soil, and microbial factors). Thereby, this study addressed four major research questions: (1) What is the distribution of EPS concentration in soils on the continental scale, and what are its physicochemical and biological drivers? (2) Do the two primary components of EPS, EPS polysaccharides and EPS proteins, follow the same or different drivers, in the latter case causing EPS composition to diverge? (3) What is the contribution of EPS-C to the SOC pool, and what causes potential differences at the continental scale? (4) Finally, how does the microbial EPS production efficiency change on a continental scale, and how do environmental conditions such as C and nutrient availability, pH extremes, and soil water deficit affect this productivity? By exploring these questions, we aim to gain a deeper understanding of the role of EPS across different soil ecosystems and its contribution to the soil C cycle.\u003c/p\u003e"},{"header":"2 RESULTS","content":"\u003cp\u003e \u003cb\u003e2.1 Dynamics and drivers of EPS concentrations across different bedrock and land-use types on a continental scale\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAcross the European transect, total soil EPS concentrations (the sum of concentrations of EPS polysaccharides and EPS proteins) were found to range from 149 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 2495 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil, with a grand mean of 956\u0026thinsp;\u0026plusmn;\u0026thinsp;55 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil (n\u0026thinsp;=\u0026thinsp;92), which is higher than the previously reported EPS concentrations in soils (mean: 393 \u0026micro;g g⁻\u0026sup1; soil) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Bedrock type and land-use type strongly influenced the concentrations of EPS (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The concentrations of EPS were significantly higher in soils on carbonate than on silicate and sediment bedrock, albeit the latter two did not differ significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe EPS concentrations were significantly higher in grasslands than in croplands but both did not differ from woodlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). More detailed analyses of bedrock, land-use types, and environmental factors influencing EPS variables can be found in Supplementary Tables S3-14. To test for major environmental and microbial drivers of soil EPS accumulation at this large scale, we performed extensive correlation analyses. We investigated the relationships between soil total EPS and climate and geographic parameters (e.g., elevation, latitude, longitude, ADI, MAT, MAP, and PET), plant parameters (e.g., FRB, root C, root N, and root C:N ratio), soil physicochemical properties (e.g., texture (sand, silt, and clay), soil minerals (Ca\u003csub\u003ee\u003c/sub\u003e, Mg\u003csub\u003ee\u003c/sub\u003e, Fe\u003csub\u003ec\u003c/sub\u003e, Fe\u003csub\u003ed\u003c/sub\u003e, Al\u003csub\u003ed\u003c/sub\u003e, Fe\u003csub\u003eo\u003c/sub\u003e, and Al\u003csub\u003eo\u003c/sub\u003e), soil nutrients (SOC, TN, TP, soil C:N, C:P, and N:P ratios, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, TDN, DOC, and TOP), soil pH and CEC, and soil microbial parameters (e.g., MBC, MBN, MBP, MBC:MBN, MBC:MBP, and MBN:MBP ratios, CUE, NUE, Cgrowth, qGrowth, turnover time), soil enzyme activities (BG, NAG, LAP, VectorL, and VectorA), and microbial community composition (GPB, GNB, fungal PLFA, bacterial PLFA, total PLFA, and B:F and GPB:GNB ratios). The results indicated that soil EPS concentrations were affected by varied factors, but most strongly by soil physicochemical properties and microbial parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eSoil total EPS concentrations increased with MAP, and more weakly with ADI and elevation, while there were no significant correlations with latitude, longitude, MAT, and PET (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Among the plant parameters, EPS concentrations increased with root N and weakly with FRB, and decreased with root C:N (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Among the soil physicochemical properties, EPS concentrations were strongly related to soil C and nutrients, showing a strong positive correlation with TN, positive correlations with SOC, TP, TOP, TDN, and weak positive correlations with the NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, soil N:P ratio, and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, while there were no significant correlations with the soil C:N and C:P ratios, pH, and DOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Soil texture, minerals, and CEC also had strong effects on soil total EPS concentrations. The content of EPS increased with clay and silt content, was positively correlated with soil Fe and Al oxides and exchangeable cations (Ca\u003csub\u003ee\u003c/sub\u003e, Mg\u003csub\u003ee\u003c/sub\u003e, Fe\u003csub\u003ec\u003c/sub\u003e, Fe\u003csub\u003ed\u003c/sub\u003e, Al\u003csub\u003ed\u003c/sub\u003e, Fe\u003csub\u003eo\u003c/sub\u003e, and Al\u003csub\u003eo\u003c/sub\u003e) and CEC, and decreased with the soil sand content (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Among microbial parameters, EPS concentration increased with microbial biomass (MBC, MBN and MBP), decreased with MBC:MBN, and was positively correlated with soil enzymes and microbial activities (BG, Cgrowth, turnover time, and VectorL) and weakly negative with qGrowth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). On the other hand, other microbial parameters that might be linked to microbial EPS formation were not significantly related in this data set, including MBC:MBP and MBN:MBP ratios, microbial CUE and NUE, LAP, NAG, VectorA, and PLFA-based microbial community composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Roles of environmental factors in driving specific EPS components\u003c/h2\u003e \u003cp\u003eAs the primary components of EPS, we investigated whether environmental drivers of EPS polysaccharides and EPS proteins followed congruent or different patterns. Statistical analysis revealed that bedrock and land use had distinct influences on EPS polysaccharides and EPS proteins across the European transect (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Specifically, EPS polysaccharides exhibited significant variations in soils derived from different bedrock types (carbonate\u0026thinsp;\u0026gt;\u0026thinsp;sediment / silicate; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) but showed no significant changes across different land use types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In contrast, EPS proteins demonstrated significant changes associated with land use type (grassland\u0026thinsp;\u0026gt;\u0026thinsp;cropland / woodland; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), while no significant differences were observed between soils from different bedrock types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003ePCA and random forest analyses revealed broad similarities in environmental drivers of EPS components, i.e. microbial factors such as MBN (also: MBC) and qGrowth, along with soil properties such as SOC, soil C:N ratio, Ca\u003csub\u003ee\u003c/sub\u003e, Fe\u003csub\u003ed\u003c/sub\u003e, and soil pH, regulated both EPS polysaccharides and proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, MBN, Ca\u003csub\u003ee\u003c/sub\u003e, and SOC emerged as the most critical factors that significantly influenced EPS polysaccharides, while microbial biomass (MBN) was the single most important driver of EPS proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, we also found distinct patterns among driving factors between EPS components: climate factors (ADI), plant traits (root C:N ratio), and the soil clay content influenced EPS polysaccharides, while the plant traits (FRB) and microbial factors (Cgrowth and BG) affected EPS proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). At the same time, both EPS compounds were strongly positively related to soil water holding capacity (particularly EPS polysaccharides), indicating that EPS promotes soil water holding capacity more than bulk SOC or texture do (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 EPS-C and its contribution to SOC compared with MNC\u003c/h2\u003e \u003cp\u003eAcross the European continent, we found EPS-C concentrations in soils that ranged from 0.06 g C kg⁻\u0026sup1; \u0026minus;\u0026thinsp;1.07 g C kg⁻\u0026sup1; soil, with an average of 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 g C kg⁻\u0026sup1; soil (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). Notably, EPS-C contributed 0.3% \u0026minus;\u0026thinsp;3.9% to the SOC, with average contributions of 1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). The contributions of EPS-C to SOC varied significantly across different bedrock and land use types (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The contributions of EPS-C to SOC followed the pattern: carbonate / sediment\u0026thinsp;\u0026gt;\u0026thinsp;silicate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In terms of land use, EPS-C contributed 2.0% to SOC in croplands and grasslands, which was significantly higher than the contribution of EPS-C to SOC of 1.2% in woodlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFurthermore, we found that across the European continent MNC contributed 20.9% to SOC, with BNC contributing 11.5%, and FNC contributing 9.4% (data not shown). The contribution of MNC to SOC was therefore nearly ten times that of EPS-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, notably strong positive correlations were observed among SOC, MBC, MNC, and EPS-C, underscoring their interconnected roles within soil C dynamics (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 EPS production efficiency and its relationship with environmental factors\u003c/h2\u003e \u003cp\u003eAcross the European transect, we observed significant variations in EPS production efficiency between soils derived from different bedrock types and from different land uses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). EPS production efficiency declined from sediment / carbonate\u0026thinsp;\u0026gt;\u0026thinsp;silicate (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), while in terms of land use types, it was ranked as cropland\u0026thinsp;\u0026gt;\u0026thinsp;grassland\u0026thinsp;\u0026gt;\u0026thinsp;woodland (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed significantly negative associations between EPS production efficiency and microbial factors (Cgrowth and CUE) but positive associations with VectorL (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Soil factors such as clay and silt content positively affected EPS production efficiency while others were strongly negatively associated such as soil C:N ratio and sand content (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Further potential drivers such as climate factors (ADI), soil factors (e.g., soil C:P ratio, SMC, and DOC) and microbial factors (qgrowth, MBC and MBP) showed weak negative correlations with the EPS production efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) while other soil factors (e.g., Ca\u003csub\u003ee\u003c/sub\u003e, Fe\u003csub\u003ec\u003c/sub\u003e, Fe\u003csub\u003ed\u003c/sub\u003e, and CEC) and microbial factors (e.g., turnover time and MBN: MBP ratio) exhibited weak positive correlations with the EPS production efficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Correlations between EPS production efficiency and soil pH or FRB were not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo assess direct versus indirect effect pathways between environmental drivers and soil EPS-C content, and to evaluate the importance of these effects via driving EPS production efficiency or EPS stabilization, we performed SEM analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Soil physicochemical variables (a composite factor negatively loaded by clay, Ca\u003csub\u003ee\u003c/sub\u003e, and Fe\u003csub\u003ed\u003c/sub\u003e, and SOC, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) had a direct negative effect on EPS-C. This aligns with expectations, as higher clay and Ca\u003csub\u003ee\u003c/sub\u003e contribute to EPS-C accumulation and stabilization at lower PC (soil) values. In contrast, the microbial composite factor (negatively loaded by CUE, Cgrowth, qGrowth, and MBN, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) exerted a direct positive effect on EPS production efficiency and a direct negative effect on EPS-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This is consistent with the observed trade-off between microbial growth and EPS production efficiency, as low microbial biomass and growth (at high PC (microbe) values) are associated with high EPS-C production efficiency. Moreover, EPS-C scaled positively withEPS production efficiency, showing that EPS production efficiency promotes EPS accumulation. The direct negative effect of microbial factors (low PC (microbe) implying high microbial CUE, biomass and growth) reflects the findings of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the positive interrelationship between MBC, EPS-C, and SOC, where C rich conditions come with high microbial growth, CUE, MBC, EPS-C and SOC. Additionally, plant factors (here: FRB) directly affected EPS-C (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Climate factors (here: ADI), as well as the factor land use type, indirectly affected the EPS-C and EPS production efficiency by impacting plant, soil, and microbial factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 DISCUSSION","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1 Variations in EPS concentrations and compositions: Roles of bedrock and land use types\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThis study found a high spatial variability in soil EPS concentrations across the European transect (minimum 149 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e / maximum 2495 \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (16 x the minimum value); Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). EPS polysaccharide concentrations ranged from 79.6\u0026ndash;1818 \u0026micro;g g⁻\u0026sup1; (mean: 624 \u0026micro;g g⁻\u0026sup1;), while those of EPS proteins ranged from 14.7\u0026ndash;825 \u0026micro;g g⁻\u0026sup1; (mean: 332 \u0026micro;g g⁻\u0026sup1;). On average, polysaccharides therefore accounted for 63% of the total soil EPS, with proteins contributing the remaining 37%. Other EPS compounds like lipids and eDNA were not determined here, but commonly comprise only a minor fraction of EPS (\u0026lt;\u0026thinsp;10%, \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. There are only a few reports of soil EPS concentrations available in the literature, which we compiled in a data synthesis effort (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Total EPS, EPS polysaccharides, and EPS proteins levels in our study were higher than those previously reported at ~\u0026thinsp;10 sites differing in climate and geology: total EPS (mean: 393 \u0026micro;g g⁻\u0026sup1;), EPS polysaccharides (mean: 266 \u0026micro;g g⁻\u0026sup1;), and EPS proteins (mean: 126 \u0026micro;g g⁻\u0026sup1;) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. This difference may be due to variations in geographical area, soil management practices, microbial communities, climate, and soil organic matter content.\u003c/p\u003e \u003cp\u003eAmong the three bedrock types, EPS concentrations were significantly higher in carbonate soils than in soils on silicate and sedimentary bedrock. Generally, soil EPS concentrations are governed by the dynamic balance between its generation, stabilization and decomposition (Shi et al., 2024). Interestingly, while carbonate soils exhibited the highest EPS concentrations, EPS production efficiencies followed a different pattern, with soils on sedimentary rocks demonstrating the highest EPS production efficiency. This suggested that high EPS concentrations in carbonate soils may depend on their stabilization processes \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, whereas the EPS in sedimentary rocks (despite their high EPS production efficiencies) may be rapidly decomposed due to the presence of adapted microbial communities efficiently utilizing EPS \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Comparing the three studied bedrock types, carbonate rocks are rich in base cations such as Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2\u0026thinsp;+\u0026thinsp;51\u003c/sup\u003e, weather rapidly \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e and have neutral soil pH (Supplementary Table S15) \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. This provides a suitable living environment for microorganisms, but also promotes the adsorption of EPS by minerals and thereby aids with its stabilization and accumulation \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Sorption and stabilization controls on EPS concentrations were also evident from positive correlations between EPS concentration and Ca\u003csub\u003ee\u003c/sub\u003e, clay, silt and Fe\u003csub\u003ed\u003c/sub\u003e, and these properties were also highest in carbonate soils. Sedimentary rocks are rich in various nutrients (such as NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e that may stimulate the microbial secretion of EPS. This, in turn, can stimulate microbial resource utilization efficiency and microbial activities through a positive feedback mechanism \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, greater microbial activities may result in the recycling and degradation of EPS \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, thus, eventually limiting its accumulation \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. In the studied soils, those deriving from sedimentary bedrock were intermediate in properties promoting EPS stabilization (e.g. clay, silt, Ca\u003csub\u003ee\u003c/sub\u003e, Fe\u003csub\u003ed\u003c/sub\u003e) but had highest EPS production efficiencies, which may explain the second highest soil EPS concentrations. In contrast, silicate rocks, due to their slower weathering rates and limited nutrient release capacities, may lack certain essential soil microbial elements, thus, limiting microbial activities and the generation of EPS \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Silicate soils had the lowest levels of potentially EPS-binding properties (clay, silt, and, Ca\u003csub\u003ee\u003c/sub\u003e), and the lowest EPS production efficiencies which were accompanied by lowest soil pH values eventually reducing microbial activities and decomposition processes. The strong effects of bedrock on EPS concentrations where therefore driven by differences in bedrock chemistry affecting (i) soil pH and nutrient supply modulating microbial EPS production and (ii) soil texture and mineralogy controlling the EPS binding capacity and stabilization.\u003c/p\u003e \u003cp\u003eAmong the three land-use types, EPS concentrations were highest in grassland soils compared to cropland and woodland soils. Interestingly, as observed with the different types of bedrock, the rankings for EPS production efficiencies and EPS concentrations did not align consistently across land use types. The EPS production efficiencies of croplands and grasslands were similar and higher than those of woodlands. This divergence may have been attributed to large differences in plant belowground C inputs (root system development) accompanied by external nutrient inputs (fertilization) and disturbance (tillage, grazing) in agricultural systems (Supplementary Table S16) \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Soil EPS increased with fine root biomass along the European transect, indicating that part of the land use driven patterns in EPS concentration were plant input driven. Plant fine root biomass was similar in grasslands and woodlands, and much greater than in croplands, but root morphologies also differ dramatically \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Grass roots are known to be much thinner and have greater specific root length (length per mass of fine roots), globally leading to much greater fine root length and fine root area in grasslands than in forests, where trees produce thicker roots with less fine root length and area \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Thinner roots turnover faster and thereby produce larger root necromass inputs in topsoils which adds to greater root exudate inputs in grasslands with their markedly greater root surface area \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Root turnover and exudates provide rich C sources for microorganisms \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e that can significantly increase microbial EPS. Despite lower plant root C inputs in croplands due to the reduced root system development in cropping systems, on a microbial biomass basis, EPS production efficiency was highest, likely due to relaxed nutrient constraints of microbial activities through long-term fertilization. However, low plant root C inputs in croplands caused the lowest MBC levels and therefore, even at the highest microbial EPS production efficiencies, the lowest EPS concentrations. In addition, anthropogenic activities such as frequent tillage, disrupt soil structure and aggregates, thereby reducing soil organic matter content \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, and the use of pesticides, which strongly affect soil microbial communities \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, negatively impacts the biomass and diversity of microbial communities. These factors contribute to reduced formation and stabilization and accelerated degradation of EPS \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. There are substantial differences in the composition and function of soil microbial communities under different land use types \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, which may also lead to differences in EPS formation and decomposition \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Nevertheless, these microbial diversity aspects were not effectively investigated in this study and warrant further exploration in the future. In conclusion, it is the interplay between plant inputs and anthropogenic management which cause land use driven differences in EPS concentrations.\u003c/p\u003e \u003cp\u003eA more detailed analysis revealed that it was primarily bedrock type that affected the concentration of EPS polysaccharides, while the type of land use influenced the concentration of EPS proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This difference reflects the different elemental demands (C, or C plus N) and controls of the biosynthetic pathways of EPS polysaccharides and EPS proteins in microbial ecosystems \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and the different interaction mechanisms with organic and mineral surfaces of EPS polysaccharides and EPS proteins promoting their binding, stabilization and accumulation. The phenomenon of differential controls of EPS polysaccharide and EPS protein accumulation is therefore triggered by the selective regulation of microbial metabolic dynamics leading to EPS compound biosynthesis and secretion and differences in their stabilization mechanisms. This differential accumulation mechanisms are thought to have strong repercussions on the properties of EPS like its stickiness and adhesion, mechanical stability (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), ion binding capacity and hydration potential which are related to the ratio of EPS protein: EPS polysaccharides \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. However, studies on the EPS protein-to-polysaccharide ratio have primarily been conducted in marine environments, with no research available in the context of soils.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 EPS-C contribution: its role in soil organic carbon storage\u003c/h2\u003e \u003cp\u003eIn this study, we found that the average EPS-C concentration in soils across the European transect was 0.41 g C kg⁻\u0026sup1; (0.06\u0026ndash;1.07 g C kg⁻\u0026sup1;), which contributed on average 1.6% to the SOC (0.3% \u0026minus;\u0026thinsp;3.9% range). These are the first estimations of EPS-C contents of soils, calculations which were not performed previously due to lack of representative data on the C content of EPS polysaccharides (and EPS proteins). Based on literature data and own measurements of model EPS polysaccharide compounds, we derived an average 39.1% C for EPS polysaccharides. In addition, a representative EPS protein C content was derived from genomic data, here the yeast \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, to predict the amino acid compositions of all potentially expressed polypeptides, which produced a protein C content of 50.7% \u003csup\u003e44\u003c/sup\u003e. The contributions of EPS-C to SOC are low (~\u0026thinsp;2% of SOC) yet these values clearly represent underestimates as the CER extraction of EPS was previously chosen to extract a relatively pure EPS fraction with little humic interference and causing little lysis of soil microbes \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The gentle CER extraction procedure therefore certainly does not fully extract soil EPS \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Compared to harsher, more complete yet more destructive extraction protocols in soils CER only extracts 1/2 to 1/20 of the total EPS polysaccharides and EPS proteins \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Clearly more comprehensive protocols need to be developed to account for incomplete EPS extraction of soils and to arrive at fully quantitative EPS estimates for soils. Although EPS-C contributes little to the total SOC (not accounting for incomplete extraction), EPS plays a critical role in soil ecosystems \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. As a direct product of microbial anabolic metabolism, EPS-C represents a highly active soil C pool and promotes SOC storage and stabilization by improving soil structure through aggregation and via promoting the formation of mineral-organic complexes \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Furthermore, EPS-C has an amplifying effect on the soil C cycle and soil functions \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and plays key roles in soil erosion resistance. One important functional aspect of EPS was shown here to be the key positive driver of soil water holding capacity, compared to weaker associations to SOC or soil texture (in the absence of direct measurements of soil porosity and pore size distribution). Its regulatory impacts on SOC and soil water storage are especially important in view of addressing climate change in agricultural systems \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast to extracellular microbial residues, the contribution of cellular residues in the form of MNC to SOC was ~\u0026thinsp;10 x that of estimated EPS-C on a continental scale, which is mainly due to differences in C allocation to extracellular versus cellular residue formation mechanisms and differences in their lability or stability \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. EPS is an active pool that is selectively secreted by microorganisms in response to adverse environmental conditions, being limited in quantity, but otherwise exhibits relatively rapid decomposition \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. As the inevitable product of microbial turnover and death, MNC is rather recalcitrant against degradation, and by binding to soil mineral surfaces is preserved in soil for prolonged periods to become an important source of SOC \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. This passivity and stability makes the contribution of MNC to SOC significantly higher than that of EPS-C. Interestingly, we found a strong positive relationship between EPS-C and MNC, which may be due to them being both outputs of microbial anabolic metabolism, active microbes producing EPS and replicating, with microbial cells turning into necromass by different processes \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. In addition, EPS is produced by microbes as a glue for attachment of their cells and colonies to soil surfaces \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, which after death cause that necromass fragments are cemented via EPS to soil minerals or organic surfaces which has been shown microscopically \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. This also promotes the collinearity between EPS-C and MNC, and with SOC. Overall, EPS therefore likely plays a double \u0026lsquo;sticky\u0026rsquo; role, on the one hand promoting cell and necromass attachment to (mineral) surfaces stabilizing SOC via necromass accumulation, and on the other hand by stimulating the aggregation of particulate organic matter and of mineral associated organic matter (including MNC-laden mineral particles) into micro- and macroaggregates, thereby also promoting SOC stabilization on a higher level.\u003c/p\u003e \u003cp\u003eIn summary, EPS-C is expected to play a crucial role in the soil C cycle. Although its direct contribution to SOC is currently estimated to be limited, it can indirectly increase SOC storage by promoting surface attachment of microbial biomass and necromass, and by stimulating aggregation and soil structure formation \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Therefore, management recommendations that encourage microbial EPS secretion through sustainable land-use practices can, in turn, enhance soil C and water storage and improve soil health and soil ecological functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Environmental drivers of EPS production efficiency and accumulation\u003c/h2\u003e \u003cp\u003eMicrobial EPS production efficiency is calculated by standardizing EPS-C concentrations to microbial biomass C \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. This EPS production efficiency may be used to assess the potential of microorganisms to secrete EPS under various environmental conditions \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. As mentioned above, the accumulation of EPS in soils is contingent on the dynamic balance between its generation, stabilization and degradation. Distinct bedrock and land-use types have shaped diverse ecological environments and strongly influenced the production and accumulation of EPS. A more detailed statistical analysis using SEM revealed that climate, plant, soil, and microbial factors drive the synthesis (EPS production efficiency) and the accumulation of EPS from multiple perspectives.\u003c/p\u003e \u003cp\u003eFirstly, environmental conditions are key factors that determine whether and to what extent microorganisms secrete EPS \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Microorganisms produce EPS under harsh environmental conditions, which helps them adapt and survive \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. For example, microorganisms increase EPS secretion to retain water, to maintain their water balance, and to protect cells from drought damage \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Although this has not yet been demonstrated for soil microbial communities, we demonstrate this adaptive phenomenon on a large scale i.e. reflected by increasing EPS production efficiency with increasing aridity (indicated by a decrease in the aridity index, ADI) and with worsening drought conditions. This same trend was apparent by the negative correlation between EPS production efficiency and soil moisture content across Europe. Soil microbial communities therefore invest into EPS secretion to effectively retain soil moisture under conditions of increasing water deficit, based on the excellent water retention properties of EPS. Importantly, soils with high EPS content likewise were characterized by high soil water retention capacity across this transect.\u003c/p\u003e \u003cp\u003eEnvironmental pH extremes, particularly high acidity were also documented to induce EPS production in microbes, where the EPS matrix buffers excessive proton concentrations via their cation-exchange capacity \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. In this study, we found a curvilinear relationship between EPS production efficiency and soil pH (Supplementary Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e; ranged from 3.6 to 8.9), where EPS production efficiency peaked at neutral pH and not at lowest pH values. This means that pH extremes are not main controlling factors of EPS production efficiency in soils in the studied pH range.\u003c/p\u003e \u003cp\u003eGiven the limiting C resources available to microbes in soils, microbes may also need to prioritize C allocation to specific processes, which may lead to physiological trade-offs in microbial C allocation \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. For instance under conditions of high growth and high CUE, microbes may prioritize C allocation to biosynthesis for growth processes, eventually reducing the C flow to EPS formation and secretion. The strategy adopted by soil microorganisms to secrete EPS is therefore likely strongly influenced by their physiological state and by C trade-offs governing (extra)cellular C allocation. In this study, EPS production efficiency scaled negatively with microbial growth (Cgrowth), and therefore also negatively with microbial CUE and with microbial biomass. This provides the first and strong hint for a microbial trade-off in C allocation between EPS secretion and growth processes and CUE, based on \u003cem\u003ein vivo\u003c/em\u003e process measurements. EPS production efficiency was highest under stressful conditions, where microbial growth, microbial biomass and CUE were low. The production of EPS represents an energetically costly investment that microbes prioritize for maintaining survival under harsh conditions \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This trade-off also allows to test the EPS production relationship to resource availability. For instance, we found that EPS production efficiency was high when microbial growth was low due to microbial C limitation as indicated by high enzyme vector length and low DOC. Different from aquatic systems, EPS production is obviously not stimulated as an intermittent extracellular store of C and N by secreting EPS polysaccharides and EPS proteins \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, under ample C and nutrient supply in soils. Instead, C and nutrient limitation promote EPS production in aquatic and soil microbial communities, though the mechanistic underpinning remains to be resolved in soils. In aquatic biofims resource limitation has been called for maximizing the microbial return on investment for exoenzymes \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, microbial community composition may affect EPS production \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and degradation \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, as it is known that different taxa show very large differences in their genomic potential for EPS secretion and EPS degradation. Diverse microbial populations may indeed adopt different EPS-related strategies in response to environmental stresses and to optimize the use of resources \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Certain microbial populations may be more inclined to secrete EPS \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e as they possess more efficient EPS biosynthesis and secretion capabilities. Key microbes for EPS secretion and EPS degradation in soils can only be identified by deep shot-gun metagenomic sequencing and by sequencing of key functional genes involved in EPS production and decomposition processes. However, in this study, we primarily relied on PLFA analysis to broadly fingerprint the soil microbial communities for large groups of microbes such as fungi, and gram-positive and gram-negative bacteria, with which we did not find a link between microbial community structure and EPS content or EPS production efficiency.\u003c/p\u003e \u003cp\u003eWhether freshly produced EPS is retained to accumulate in soils or degraded to be microbially consumed is an important factor that influences its accumulation and soil function. Soils with high clay and silt contents are more conducive to the long-term accumulation and stabilisation of EPS \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, whereas in soils with a higher sand content, EPS may be more prone to recycling and be lost due to their weaker binding forces \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. The presence of metal ions (e.g., Ca\u0026sup2;⁺) and Fe oxy(hydr)oxides in the soil can assist in the accumulation of EPS through enforcing adsorption and binding \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, which reduces its degradation, while promoting its accumulation \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. The prevalence of this abiotic sorptive control of EPS was highlighted by SEM analysis, where soil factors dominantly explained EPS-C accumulation.\u003c/p\u003e \u003cp\u003eIn conclusion, the driving mechanisms behind differences in EPS production efficiency and EPS accumulation involve the dynamic interplay between the environment, resource availability, microbial metabolic strategies, and soil properties. It is essential to improve EPS quantification in soils, addressing potential underestimations by current methods. Further research should focus on understanding the dynamics of EPS formation, stabilization, and turnover using isotope tracer methods. Additionally, linking EPS content and dynamics to its functional implications in soils\u0026mdash;including aggregate formation, carbon cycling, water retention, and microbial stress amelioration\u0026mdash;will be critical for advancing our knowledge of soil microbial ecology and ecosystem resilience.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Site description\u003c/h2\u003e \u003cp\u003eTo investigate the distribution of EPS and its drivers on a continental scale, 92 sites were sampled across the European continent (36\u0026ordm;24 '45' N- 71\u0026ordm;01 '56' N, 8\u0026ordm;31 '56' W- 29\u0026ordm;34 '60' E). Sampling sites extended from the Mediterranean (Southern Spain) to the subarctic (Northern Norway), and from the Atlantic coast (Western Portugal) to the dry continental steppes (Eastern Romania). The sampled soils were distinct in terms of their bedrock and land use types. Geographical and climate data (including latitude, longitude, elevation, mean annual temperature (MAT), mean annual precipitation (MAP), potential evapotranspiration (PET)), and bedrock and land use types were described earlier in detail by Noll et al. \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In brief, MAT of the sampling sites ranged from \u0026minus;\u0026thinsp;3 to 18 \u0026ordm;C, while MAP ranged from 415 to ~\u0026thinsp;1400 mm yr\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. An aridity index (ADI) was calculated as MAP divided by (MAT\u0026thinsp;+\u0026thinsp;33) according to Quan et al. \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Bedrock types were aggregated into three large groups for statistical analyses: carbonate (e.g., dolomite, limestone, and marl), sediment (e.g., flysch, molasse, till, and fluvial sand), and silicate (e.g., plutonic, igneous, and metamorphic formations). Where possible, samples were collected from all three land use types (shrubland/forest, grassland, and cropland) in close vicinity, the three sites then lying within 1\u0026ndash;2 km distance in such a land use cluster with the same geology and climate. In the following, we used \u0026ldquo;woodland\u0026rdquo; encompassing subarctic tundra, open woodlands, and forests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Sampling\u003c/h2\u003e \u003cp\u003eDuring the peak of the growing season (May-August 2017), bulk samples of mineral topsoil (0\u0026ndash;15 cm depth) were extracted using a soil corer (\u0026Oslash; 5 cm). Each soil sample was composited from five replicates. In total, soils from 92 sites were sampled, with 23 site clusters (69 soils) including woodland, grassland, and cropland soils \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The soil samples were homogenized to 2 mm by sieving, and separate aliquots were air-dried or stored under moist conditions at 4\u0026deg;C. Root samples were collected from soil while sieving to determine root biomass. They were then washed and dried in a drying oven at 60\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Soil physicochemical and microbial characterisation\u003c/h2\u003e \u003cp\u003eSoil texture (sand, silt, and clay content), CEC, and exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e (Ca\u003csub\u003ee\u003c/sub\u003e) and Mg\u003csup\u003e2+\u003c/sup\u003e (Mg\u003csub\u003ee\u003c/sub\u003e) were determined by the Austrian Agency for Health and Food Safety (AGES) according to European and international standards (\u0026Ouml;NORM). Fe oxyhydroxides and Al oxyhydroxides were determined in acid ammonium oxalate (Fe\u003csub\u003eo\u003c/sub\u003e and Al\u003csub\u003eo\u003c/sub\u003e) and in Na-dithionite extracts (Fe\u003csub\u003ed\u003c/sub\u003e and Al\u003csub\u003ed\u003c/sub\u003e) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e at the Institute of Soil Research (IBF, University of Natural Resources and Life Sciences, Vienna, Austria). Oxalate-extractable Fe (Fe\u003csub\u003eo\u003c/sub\u003e) and Al (Al\u003csub\u003eo\u003c/sub\u003e) referred to amorphous Fe and Al oxyhydroxides and Fe bound within organometallic complexes. Fe\u003csub\u003ed\u003c/sub\u003e minus Fe\u003csub\u003eo\u003c/sub\u003e represented Fe bound in crystalline oxyhydroxides (Fe\u003csub\u003ec\u003c/sub\u003e). The soil water holding capacity (WHC), soil moisture content (SMC), soil pH, SOC, soil total nitrogen (TN), soil total phosphorus (TP), soil organic phosphorus (TOP), dissolved organic carbon (DOC), total dissolved nitrogen (TDN), soil ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), and soil nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) were measured as described by Noll et al. \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Subsequently, the soil C:N:P ratios were calculated according to the concentrations of SOC, TN, and TP.\u003c/p\u003e \u003cp\u003eFine root biomass (FRB) was obtained for roots\u0026thinsp;\u0026lt;\u0026thinsp;2 mm diameter. An elemental analyzer was used to determine the root C and N content, and root P concentrations were determined by dry ashing, acid extraction and colorimetric P determination. Root C:N:P ratios were calculated accordingly. Soil microbial community composition was analyzed by phospholipid fatty acid (PLFA) analyses according to Kaiser et al. \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and Hu et al. \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and PLFAs divided into gram-positive bacteria (GPB), gram-negative bacteria (GNB), fungi, and general bacteria. Total PLFA biomass, bacteria to fungi (B:F) ratios, and gram-positive bacteria to gram-negative bacteria (GPB:GNB) ratios were calculated. Microbial biomass C (MBC), microbial biomass nitrogen (MBN), and microbial biomass phosphorus (MBP) were determined via chloroform fumigation extraction \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, from which the MBC:MBN:MBP ratios were calculated. Microbial N use efficiency (NUE) was measured as described by Zhang et al. \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and microbial C use efficiency (CUE) was quantified following Zheng et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Underlying processes of microbial growth (Cgrowth), growth normalized to MBC (qGrowth), respiration, and microbial biomass turnover time were also estimated as described by Zheng et al. \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Soil enzyme activities, including β-glucosidase (BG), N-acetyl-β-glucosaminidase (NAG), leucine aminopeptidase (LAP), acid phosphatase (AP), exoglucanase-cellobiosidase (CEL), β-xylosidase (BX), and phenoloxidase (POX) were measured as described by Marx et al. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and Zheng et al. \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and enzymatic vector lengths (VectorL) and vector angles (VectorA) calculated following Moorhead et al. \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Soil microbial necromass, including fungal necromass C (FNC), bacterial necromass C (BNC), and total microbial necromass C (MNC), was determined using amino sugar biomarkers as described by Salas et al. \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Soil EPS analysis\u003c/h2\u003e \u003cp\u003eSoil EPS was extracted using cation exchange resins (CER) \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Soil EPS binds to negatively charged soil surfaces, such as clay minerals and organic matter, through electrostatic interactions involving Ca\u0026sup2;⁺ bridging. CER, in its sodium form, exchanges their Na\u003csup\u003e+\u003c/sup\u003e ions with these divalent cations, weakening Ca\u003csup\u003e2+\u003c/sup\u003e bridging and releasing EPS from the soil matrix. This technique is highly efficient for the extraction of EPS and induces negligible cell lysis, yielding highly pure EPS fractions \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Prior to EPS quantification, a preliminary experiment was devised to test EPS extraction efficiency with different CER:soil weights across bedrock types, considering the expected larger exchangeable Ca\u003csup\u003e2+\u003c/sup\u003e levels in carbonate soils and the wide range of SOC concentration variations due to the extensive sampling range. Specifically, three carbonate soil samples and three non-carbonate soil samples with high, medium, and low SOC contents were selected. For each sample, 0.5 g, 1 g, 2 g, and 3 g air-dried soil were weighed, and then 10 g of wet CER (soaked in phosphate-buffered saline solution (PBS)) was added for extraction, to demonstrate that the CER method and dosage used are suitable for different types of soil samples with varying SOC contents. This experiment showed that using 1 g of air-dried soil with 10 g of wet CER provided a good balance for determining soil EPS, as 3 g was more effective for polysaccharides and 0.5 g or 1 g for proteins, regardless of bedrock type or SOC level (Supplementary Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). PBS was prepared, the pH adjusted to 7.4 and the solution cooled to 4\u0026deg;C in advance. CER (Amberlite\u0026trade; IR120 Ion Exchange Resin, Na\u003csup\u003e+\u003c/sup\u003e form, 15\u0026ndash;50 mesh, Sigma Aldrich) was thoroughly hydrated and washed using PBS, exchanging the PBS solution by decanting and adding new PBS for 5\u0026ndash;6 times. Aliquots of air-dried soils (1 g) were weighed into 50 ml centrifuge tubes, and amended with 25 ml 10 mM CaCl₂ solution. The suspension was then shaken gently for 30 min, and centrifuged at 4\u0026deg;C at 4000 g for 30 min, after which the supernatant was carefully poured off to remove interfering extractable non-EPS microbial products such as free sugars and amino acids. Subsequently, EPS was extracted by addition of 25 ml PBS and 10 g wet CER, and the mixture was shaken vigorously for 2 h prior to centrifugation at 4\u0026deg;C at 4000 g for 30 min. The supernatant was then filtered through 0.45 \u0026micro;m filters (VWR\u0026reg; Syringe Filters, Nylon, 25 mm, Avantor), and the obtained solution was stored at 4\u0026deg;C and used for the determination of EPS polysaccharides and EPS proteins within 4 d.\u003c/p\u003e \u003cp\u003eEPS polysaccharides were measured by acid hydrolysis and anthrone reaction using glucose as a standard \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The color reagent was produced by dissolving 0.2 g anthrone in 100 ml concentrated H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e (previously cooled to 0\u0026deg;C). For colorimetric assays, 200 \u0026micro;l of sample, standard or blank was added to 10 ml glass tubes, followed by the addition of 1 ml anthrone reagent. The mixture was then mixed well and heated to 100\u0026deg;C in a water bath for 10 min. After rapid cooling to room temperature, the absorbance was measured at a wavelength of 625 nm.\u003c/p\u003e \u003cp\u003eEPS proteins were determined by a modified Lowry method \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, based on Cu-binding to polypeptides and the Folin-Ciocalteu reaction, but adopted to deal with the humic compound interferences in soil extracts. Bovine serum protein was used as the standard. Samples, standards, and blanks (50 \u0026micro;l) were pipetted into microtiter plates, mixed with 100 \u0026micro;l copper-containing or copper-free NaK tartrate/Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e-NaOH buffer for 10 min, supplemented with 100 \u0026micro;l of 10-fold diluted Folin-Ciocalteu reagent and after 30 min protein absorbance was measured at a wavelength of 750 nm.\u003c/p\u003e \u003cp\u003eTotal EPS was calculated as the sum of EPS polysaccharides and EPS proteins. EPS-C was then estimated, where the C content of EPS polysaccharides was determined as an average of 39.1% based on elemental analysis of several exopolysaccharide standards, including hyaluronic acid (37.5%), xylan (40.5%), pectin (38.3%), and arabinogalactan (40.2%), while the C content of EPS proteins was estimated to be 50.7% \u003csup\u003e44\u003c/sup\u003e. The EPS-C:MBC ratio was calculated to represent the EPS production efficiency, whereas the EPS-C:SOC ratio was computed to represent the contribution of EPS-C to SOC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Statistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed and visualized using R version 4.1.2 \u003csup\u003e45\u003c/sup\u003e. One-way ANOVA analysis was applied to examine the impacts of bedrock and of land use on the target EPS variables, i.e., total soil EPS contents, EPS-polysaccharide and EPS-protein contents, EPS-protein:EPS-polysaccharide ratios, EPS-C contents, the contributions of EPS-C to SOC, and the EPS production efficiency. Subsequently, we used linear regression models to analyze the relationships between EPS variables (total soil EPS content, EPS polysaccharide content, EPS protein content, and EPS production efficiency) and environmental factors. Three models were applied: (1) a simple linear regression to assess the effect of environmental factors on EPS variables, (2) a multiple linear regression with an interaction term to examine the combined effects of environmental factors and land use type, and (3) another multiple linear regression to evaluate the interaction between environmental factors and bedrock type. All analyses were conducted in R using the \u003cem\u003elm\u003c/em\u003e function. Spearman\u0026rsquo;s correlation analysis and principal component analysis (PCA) were employed to examine the impacts of environmental factors (including climatic, plant, soil and microbial factors) on the target EPS variables, PCA being performed with the R package \u003cem\u003estats\u003c/em\u003e. Random forest models were used to rank the importance of controlling factors of EPS polysaccharides, EPS proteins, EPS-C, the contribution of EPS to SOC and EPS production efficiency (R package \u003cem\u003erandomForest\u003c/em\u003e) (Supplementary Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Using general linear models we examined the relationships between EPS-C, MNC, MBC and SOC (R \u003cem\u003elm\u003c/em\u003e function). The normality of model residuals was assessed using the Shapiro-Wilk test. To assess the direct and indirect effect pathways between environmental drivers and soil EPS-C content, and to determine whether these effects operate through EPS production efficiency or EPS stabilization, we performed structural equation modeling (SEM) analysis (R package \u003cem\u003elavaan SEM\u003c/em\u003e). Before constructing the SEM, we first examined collinearity among all environmental variables and removed highly correlated factors to reduce multicollinearity. After this selection process, the retained variables included ADI, FRB, MBN, Cgrowth, qGrowth, CUE, SOC, clay content, soil C/N ratio, Ca\u003csub\u003ee\u003c/sub\u003e, and Fe\u003csub\u003ed\u003c/sub\u003e. The environmental variables were grouped into four categories: climate, plant, microbial, and soil. Among them, SOC, soil clay content, soil C/N ratio, Ca\u003csub\u003ee\u003c/sub\u003e, and Fe\u003csub\u003ed\u003c/sub\u003e were combined into a soil composite variable, while MBN, Cgrowth, gGrowth, and CUE were combined into a microbial composite variable using PCA. Instead of using only the first principal component (PC1) or the second principal component (PC2) alone, soil and microbial variables were constructed by combining the PC1 and PC2 based on their explained variance, and this was necessary because SEM models using only PC1 or PC2 did not yield satisfactory results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Using this categorization, we developed an SEM model to explore how these four environmental components, along with bedrock type and land use, may directly or indirectly influence EPS-C content and EPS production efficiency. Model fit was evaluated using multiple fit indices, including the Chi-square test (χ\u0026sup2;), Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and Akaike Information Criterion (AIC). In the best-fitting model, we calculated direct and indirect pathway coefficients to determine the key drivers of EPS production efficiency and EPS-C variation across climatic, geological, and land-use gradients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e \u003cp\u003eK.S. drafted the manuscript, conducted laboratory work, and analyzed and interpreted the data. Q.Z., B.W., L.N., S.Z., and Y.H. performed laboratory work, contributed to data analysis, and assisted in manuscript editing. H.R. reviewed and revised the manuscript. W.W. designed the study, interpreted the data, and contributed to manuscript editing. All authors made significant contributions to the manuscript drafts and approved the final version for publication.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eThis research was funded in whole or in part by the Austrian Science Fund (FWF) [grant DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.55776/P28037\u003c/span\u003e\u003cspan address=\"10.55776/P28037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. For open access purposes, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. This study was further supported by the National Key Research and Development Program of China (No. 2023YFD2200404 and No. 2021YFD2200402/3), and the program of China Scholarship Council (No. 202308320320).\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBuckeridge KM, Creamer C, Whitaker J (2022) Deconstructing the microbial necromass continuum to inform soil carbon sequestration. Funct Ecol 36:1396\u0026ndash;1410\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi K et al (2024) Accumulation of soil microbial extracellular and cellular residues during forest rewilding: Implications for soil carbon stabilization in older plantations. 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J Basic Microbiol 47:103\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberson E, Chenu C, Firestone M (1993) Microstructural changes in bacterial exopolysaccharides during desiccation\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberson EB, Firestone MK (1992) Relationship between desiccation and exopolysaccharide production in a soil Pseudomonas sp. Appl Environ Microbiol 58:1284\u0026ndash;1291\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y et al (2022) Bacterial extracellular polymeric substances: Impact on soil microbial community composition and their potential role in heavy metal-contaminated soil. Ecotoxicol Environ Saf 240:113701\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha I, Datta S, Biswas D (2020) Exploring the role of bacterial extracellular polymeric substances for sustainable development in agriculture. 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Microb biofilms, 163\u0026ndash;199\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biofilm, global climate change, management, microbial residues, parent rock","lastPublishedDoi":"10.21203/rs.3.rs-6279309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6279309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExtracellular polymeric substances (EPS) are a vital component of microbial residues which contribute to soil organic carbon (SOC). However, despite various conjectures and hypotheses regarding soil EPS controls, empirical research and experimental evidence to validate these theories have remained highly limited. In this study, we addressed this knowledge gap by conducting extensive soil sampling across Europe, encompassing diverse climates and bedrock and land use types, to systematically investigate soil EPS contents and large-scale controls. We found that bedrock and land use significantly influenced the soil EPS concentration, the contribution of EPS-carbon (C) to SOC, as well as the microbial EPS production efficiency. The average soil EPS concentration was 956\u0026thinsp;\u0026plusmn;\u0026thinsp;55 \u0026micro;g g⁻\u0026sup1; soil (n\u0026thinsp;=\u0026thinsp;92 sites), with EPS-C contributing on average 1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% to SOC. Soil EPS concentrations were significantly higher on carbonate bedrock than on silicate and sedimentary geologies. In terms of land use, grassland soils had significantly higher EPS concentrations compared to cropland soils but did not differ from woodland soils. Further detailed investigations of proximate soil physicochemical drivers of EPS content across the transect showed slightly different drivers for EPS polysaccharides and EPS proteins. For instance, EPS polysaccharides were affected by bedrock but not by land use, while the pattern was inverse for EPS proteins. Microbial EPS production efficiency, which expresses the EPS-C content per microbial biomass C, was significantly negatively correlated with microbial carbon use efficiency, reflecting the trade-off between C allocation for growth and extracellular production. EPS production efficiency increased under harsh environmental conditions (e.g., low soil moisture content, high drought index), but was unaffected by pH extremes. On a large scale, soil EPS accumulation was promoted by its production efficiency and by soil factors promoting the sorption and stabilization of EPS, such as clay content, exchangeable Ca and Fe oxides. These findings underscore the significant yet overlooked role of EPS as a critical component of the soil-stable C pool, as it influences microbial C allocation and SOC stabilization and should be further studied to better understand soil C cycling.\u003c/p\u003e","manuscriptTitle":"Continental-scale drivers of soil microbial extracellular polymeric substances","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-09 06:32:54","doi":"10.21203/rs.3.rs-6279309/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"56e52d5f-c737-47ea-9623-a3ba793871f8","owner":[],"postedDate":"April 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46796391,"name":"Earth and environmental sciences/Ecology/Ecosystem ecology"},{"id":46796392,"name":"Earth and environmental sciences/Ecology/Biogeochemistry/Carbon cycle"},{"id":46796393,"name":"Earth and environmental sciences/Ecology/Biogeography"}],"tags":[],"updatedAt":"2026-04-10T07:06:52+00:00","versionOfRecord":{"articleIdentity":"rs-6279309","link":"https://doi.org/10.1038/s41467-026-70068-0","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-03-02 05:00:00","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-04-09 06:32:54","video":"","vorDoi":"10.1038/s41467-026-70068-0","vorDoiUrl":"https://doi.org/10.1038/s41467-026-70068-0","workflowStages":[]},"version":"v1","identity":"rs-6279309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6279309","identity":"rs-6279309","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}