From plants to minerals: depth-dependent controls on microbial carbon use efficiency across the global

From plants to minerals: depth-dependent controls on microbial carbon use efficiency across the global | 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 From plants to minerals: depth-dependent controls on microbial carbon use efficiency across the global Yongxing Cui, Yaqin Guo, Tessa Camenzind, Matthias Rillig This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8625878/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Microbial carbon use efficiency (CUE) represents a key trait linking microbial metabolism to soil carbon (C) cycling. While subsoils store over 50% of total soil C and are supposed to be more vulnerable to global change than topsoils, the patterns and controls of CUE in subsoils remain unclear, limiting predictions of whole-profile soil C dynamics. Here, we estimated CUE in topsoils (n = 814) and subsoils (n = 379) worldwide using an enzyme-based stoichiometric model and identified dominant drivers in each layer. We found that subsoil CUE was significantly higher than topsoil CUE, indicating a greater allocation of assimilated C to microbial biomass relative to respiration in deeper soils. Topsoil CUE was primarily influenced by vegetation-derived C inputs, whereas subsoil CUE was strongly constrained by mineral protection and soil physicochemical conditions, which suggests subsoil CUE may be less sensitive to global change than previously assumed. Global prediction revealed a poleward increase in CUE across layers, highlighting high soil C retention potential at high latitudes. This geographical pattern also implies that high-latitude soil C is vulnerable and may experience accelerated loss under ongoing climate warming. Earth and environmental sciences/Ecology/Microbial ecology Earth and environmental sciences/Biogeochemistry/Carbon cycle Earth and environmental sciences/Ecology/Climate-change ecology Soil carbon cycling Soil profile Spatial variations Microbial metabolism Ecological stoichiometry Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Soils are the largest carbon (C) pool of the terrestrial ecosystems and therefore play critical role in mediating climate change (Paustian et al., 2016; Balesdent et al., 2018). The turnover of soil organic carbon (SOC) is primarily regulated by microorganisms that assimilate C into biomass while releasing CO 2 through respiration (Crowther et al., 2019; Beattie et al., 2024). The efficiency with which microorganisms allocate assimilated C to growth rather than respiration, defined as microbial carbon use efficiency (CUE), is a key determinant of SOC storage (Manzoni et al., 2018; He et al., 2024). Subsoils store more than half of SOC in terrestrial ecosystems and exhibit substantially longer SOC turnover time than topsoils (Rumpel and Kögel-Knabner, 2011; Luo et al., 2019; Wang et al., 2025). Yet, how CUE varies with soil depths and mechanisms governing this variation remain unclear, constraining our ability to predict whole-profile SOC dynamics and their responses to climate change. The prevailing assumptions expect that CUE declines with soil depth, due to sharp decrease in fresh and labile C from plants (Zhang et al., 2023; Guo et al., 2025), forcing microorganisms to invest more energy in extracellular enzymes to access recalcitrant substrates in deeper layers (Fontaine et al., 2007; Huang and Su, 2025). However, empirical evidence is highly inconsistent. While a recent study on continental scale reported declining CUE with depth (Pei et al., 2025), other site-specific studies have shown increasing trends or no significant variation (Spohn et al., 2016; Li et al., 2021). These discrepancies suggest that fundamental controls on CUE may differ between soil layers. Although various factors such as climatic conditions (e.g., temperature), substrate quantity and quality, and soil physicochemical properties, are known to primarily influence microbial metabolism (Manzoni et al., 2012; Zhang et al., 2022; Cui et al., 2024; Shi et al., 2024; Hu et al., 2025), a comparative assessment of drivers behind CUE variations among different layers remains lacking. As a result, identifying the relative importance of these environmental drivers in CUE globally across depths is critical for improving CUE representation in Earth System models (Allison, 2025). In topsoils, microbial activity is tightly coupled to the quantity and quality of fresh C inputs from vegetation (Button et al., 2022; Huang et al., 2023). In contrast, subsoils are substrate-poor environments where C resources are often older, more chemically complex, or physically protected organic matter (Eusterhues et al., 2003; Hicks Pries et al., 2017; Zosso et al., 2023). As a result, soil physicochemical properties such as clay content, aggregate structure, and pH play a central role in regulating the accessibility and transformation of organic matter through organo-mineral interactions and physical protection in deeper soil layers (Wordell-Dietrich et al., 2017; Luo et al., 2019; Qin et al., 2019; Kang et al., 2021). Supporting this view, a recent deep-soil manipulation experiment found that CUE in subsoils, but not in topsoils, increased following glucose addition and aggregate disruption, suggesting co-limitation of microbial growth by substrate availability and physical inaccessibility in subsoils (Pei et al., 2025). Together, these findings imply that while C inputs primarily drive topsoil CUE, C accessibility and mineral protection likely exert dominant control on subsoil CUE. Here, we hypothesize that (1) CUE is systematically higher in topsoils than in subsoils due to greater C availability; (2) vegetation production, the primary sources of fresh C inputs, exerts the dominant control on CUE in topsoils, whereas physicochemical properties, particularly mineral associations that regulate C accessibility, are the primary drivers of CUE in subsoils. To test these hypotheses, we compiled a global dataset from natural ecosystems spanning a wide range of climatic zones and biomes (Fig. 1a). We estimated community-level CUE using a culture-independent stoichiometric model, which captures the in-situ adaptation of microbial communities to their resource environment (Sinsabaugh et al., 2013; Schimel et al., 2022). We then examined relationships between CUE and 21 potential drivers related to topography, climate, vegetation, and soil properties, to identify the dominant controls of CUE in topsoils and subsoils. Finally, we integrated these factors to generate global maps of CUE (0.25˚ x 0.25˚ resolution) for both layers. 2. Results 2.1 ． Patterns of CUE across sampling sites Across all sites CUE was significantly higher in subsoils than in topsoils ( P < 0.001, linear mixed effects model; Fig. 1b), though the effect size was modest (0.02). The estimated marginal means were 0.37 (95% confidence interval (CI): 0.34-0.41) for subsoils and 0.35 (95% CI: 0.31-0.38) for topsoils. Across soil layers (0-30, 30-60, and 60-100 cm), CUE in the 30-60 cm and 60-100 cm layers was significantly higher than in the 0-30 cm layer, with no statistical significance between the two deeper layers (Fig. S1a). Therefore, the 30-60 and 60-100 cm layers were pooled and designated as subsoils for subsequent analyses. CUE displayed contrasting latitudinal patterns between topsoils and subsoils (Fig. 1c and Fig. S1b). In tropical regions, CUE was significantly higher in topsoils than in subsoils, whereas in cold regions the opposite pattern was observed, with subsoil CUE significantly exceeding that of topsoil ( P < 0.001; Fig. 1e). In temperate and arid regions, no significant differences between topsoils and subsoils were detected ( P = 0.49 and P = 0.76, respectively; Fig. 1e). 2.2 ． Environmental drivers of CUE in different layers To identify the key drivers of CUE, we employed Random Forest analysis on a comprehensive set of predictors representing topographic, climatic, vegetation, and soil properties (Fig. S2). When data from topsoils and subsoils were combined, the analysis revealed that soil depth was a significant predictor of CUE (Fig. S3). In topsoils, bulk density emerged as the single most important predictor, whereas in subsoils, clay content was the most important predictor (Fig. 2a-b). Beyond these primary drivers, soil pH was identified as the second most important predictor in both layers (Fig. 2a-b). While these factors were dominant, the analysis highlighted the complex nature of CUE, with multiple predictors showing statistical significance in both topsoils and subsoils. To further elucidate the causal relationships among predictor groups, path analysis was conducted to quantify the direct and indirect effects of topography, climate, seasonality, soil texture, vegetation, and soil properties on CUE based on a priori model (Fig. S4). In topsoils, vegetation-related metrics–including gross primary productivity (GPP), Shannon_EVI and belowground biomass (BGB)–exerted the strongest positive effect on CUE (Fig. 2c, d and Table S1). In contrast, CUE in subsoils was primarily constrained by soil physicochemical properties, particularly pH, bulk density, and moisture, which showed pronounced negative effects (Fig. 2e, f and Table S2). 2.3 ． Global prediction of CUE in topsoils and subsoils To extrapolate our site-level findings to the global scale, we generated spatially explicit maps of CUE at 0.25˚ x 0.25˚ resolution using the XGBoost algorithm (Figs. 3 and 4). Globally, mean CUE was 0.35 (95% CI: 0.26-0.43) in topsoils and slightly higher at 0.37 (95% CI: 0.28-0.45) in subsoils (Figs. 3 and 4). In both layers, CUE increased from the equatorial to high-latitude regions (Fig. 3b, d), consistent with patterns across climate zones (Fig. 4b). Across biomes, CUE in subsoils generally exceeded topsoils, and grasslands exhibited higher CUE than forests (Figs. 4d and S5). 3. Discussion Higher CUE in subsoils than in topsoils indicates that deep-soil microbial communities allocate a greater fraction of assimilated C to biomass relative to respiration. This finding challenges the conventional view that decomposition of low-quality substrates requires higher energy, therefore leading to lower metabolic efficiency (Bosatta and Ågren, 1999; Mganga et al., 2022). Instead, our findings align with ecological theory that microorganisms in resource-limited environments employ efficiency-oriented metabolic strategies to sustain long-term survival (Roller and Schmidt, 2015). This interpretation is supported by global syntheses, which showed that microbial communities utilizing complex substrates (e.g., cellulose, plant residues, necromass) generally exhibit higher CUE than those metabolizing labile glucose (Qiao et al., 2019). Similarly, microbial communities with genes for degrading recalcitrant C (e.g., lipids and lignin) have higher CUE when supplied with low-quality C sources (Ren et al., 2024). Collectively, these findings consistently suggest that subsoil microbial communities may possess evolutionary adaptations that enable them to thrive in low-energy, resource-constrained environments. Furthermore, clay content was identified as the strongest predictor of CUE in subsoils (Fig. 2), suggesting the critical role of mineral protection of SOC in meditating CUE. Mineral protection is a well-known mechanism governing the fate of organic substrates in deeper horizons, where organo-mineral associations, sorption, and physical occlusion constrain substrate accessibility and microbial metabolism (Eusterhues et al., 2003; Dungait et al., 2012; Lehmann and Kleber, 2015; Xiao et al., 2024). Soil physiochemical properties, such as soil pH and moisture as identified by our path modeling (Fig. 2), could further shape the capacity of minerals to protect organic matter and regulate microbial activity (Six et al., 2002; Malik et al., 2018; Luo et al., 2019; Luo et al., 2021). Together, our findings indicate that subsoil CUE is primarily regulated by mineral constraints and soil physicochemical conditions. In topsoils, however, CUE is fundamentally linked to vegetation productivity, which provide fresh C and foster the development of soil structure as supported by both path analysis and Random Forest model (Fig. 2). Notably, bulk density, the most important predictor of CUE in topsoils identified by Random Forest model not only reflects soil structure (Heuscher et al., 2005; Niedźwiecka et al., 2025), but also can serve as a robust proxy of organic matter content and substrate accessibility (Fu et al., 2021; Guo et al., 2025). Indeed, the organic carbon in topsoils is primarily from plant C inputs, with higher vegetation productivity generally leading to higher SOC content and lower bulk density (Cotrufo et al., 2013; Sinsabaugh et al., 2017; Cui et al., 2023; Huang et al., 2023). As a result, topsoil CUE is regulated by biologically mediated soil structure and vegetation-derived C inputs. Global prediction illustrated spatial patterns of CUE across soil layers. The global mean CUE was consistently higher in subsoils than in topsoils, suggesting greater microbial C retention in deeper horizons that potentially contributes to long-term SOC sequestration (Tao et al., 2023; Yang et al., 2025). Our models suggested a poleward increase in CUE across soil layers, likely reflecting temperature constraints on microbial metabolism, whereby higher respiration costs in warm tropics reduce efficiency relative to cooler temperate and boreal zones (Sinsabaugh et al., 2013; Wang et al., 2021; Cui et al., 2024). This pattern implies that CUE in high-latitude soils may be more vulnerable than in low latitudes due to the amplified warming occurring at high latitudes (Post et al., 2019; Rantanen et al., 2022). Additionally, we found that mean annual temperature was not a primary driver of CUE in either layer (Fig. 2). Although it correlated significantly with CUE, the direction of the relationship differed between soil layers and was generally weak (Fig. S6). Despite temperature-dependent functions being widely used to parameterize CUE (Wieder et al., 2015; Qiao et al., 2019; Ye et al., 2019), our findings emphasize the necessity of considering the distinction between topsoil and subsoil processes, when simplify CUE as a function of temperature. Despite providing an important global investigation of CUE estimates, our study includes several limitations. First, our estimates of CUE relied on the stoichiometric modeling that assumes microorganisms regulate extracellular enzyme activities to balance the elemental (C, N, or P) composition of their substrates (Sinsabaugh and Follstad Shah, 2012). While this approach leverages widely available data, enzyme-based metrics may not fully capture transient microbial nutrient limitations due to potential temporal lags in enzyme production and turnover (Cui et al., 2025). Integrating this method with isotope tracer incubation experiments and genomic data can provide a more comprehensive understanding of global CUE dynamics (Geyer et al., 2019; Saifuddin et al., 2019). Second, though our dataset spans multiple climate zones, it is geographically biased towards the northern hemisphere (Fig. S7). Expanding data coverage in the southern hemisphere is necessary to reduce prediction uncertainties in global maps (Meyer and Pebesma, 2022). Finally, given that subsoils play a pivotal role in long-term C sequestration yet remain understudied, targeted investigation of subsoil microbial processes will be essential for refining global C cycle models. In conclusion, our study reveals that CUE was higher in subsoils than in topsoils, and was driven by distinct environmental factors across soil layers. While topsoil CUE was most strongly influenced by vegetation-derived C inputs, subsoil CUE was constrained by mineral protection and soil physicochemical conditions that govern C accessibility. Recognizing these depth-dependent mechanisms underscores the importance of viewing subsoils as distinct, microbially active compartments rather than extensions of topsoils. Incorporating vertical heterogeneity in microbial functioning is essential for advancing predictions of long-term soil C storage under changing climate. 4. Methods 4.1. Data collection We conducted a systematic literature search using the keywords “soil extracellular enzymes OR soil enzymes” in the Web of Science (https://www.webofscience.com) and the Google scholar Integrated Database (https://scholar.google.com) ranging from 1980 to 2024. Although the terms are broad, they ensure comprehensive retrieval of studies reporting soil enzyme data across diverse ecosystems, which we then refine through well-defined inclusion criteria to capture studies relevant for CUE estimation in both topsoils and subsoils. The following criteria needed to be met: 1) Soil physicochemical characteristics and extracellular enzyme activity should be measured simultaneously, and the activities of these ecoenzymes must be measured fluorometrically using a 200 μM solution of substrate labeled with 4-methylumbelliferone or 7-amino-4-methylcoumarin. The following C, N, and P-acquiring enzymes should be included. Namely, β-1,4-glucosidase (BG), β-1, 4-N-acetylglucosaminidase (NAG) and / or L-leucine aminopeptidase (LAP), and acid or alkaline phosphatase (AP). Soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) must be available. Microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and microbial biomass phosphorus (MBP) were obtained from the same studies when reported. In total, 64.7% of MBC data, 63.6% of MBN data, and 57.4% of MBP data were retrieved, representing the proportion of all collected data that met the required quality criteria. These parameters were essential components to calculate CUE based on a biogeochemical equilibrium model. 2) Studies with natural or field conditions were included and greenhouse or laboratory experiments were excluded. 3) Wetland ecosystems were excluded because the biogeochemical cycles in wetlands are very different from those in terrestrial soils (Bahram et al., 2022). 4) Samples from litter layers were excluded (Takele et al., 2025). In total, our database comprised 1193 data points from 103 sites (Fig. S7). Data were extracted from tables or figures of selected papers using GetData Graph Digitizer v.2.25. In addition, we collected the following parameters: location (latitude and longitude), elevation, mean annual temperature (MAT), mean annual precipitation (MAP), and various soil properties including soil texture, pH, soil moisture, bulk density (BD), and cation exchange capacity (CEC). For data not directly recorded in the study, we supplemented information from relevant databases (Table S3). To comprehensively identify potential environmental drivers, we also retrieved other variables from multiple databases at relatively fine spatial resolutions (Table S3). These include topography (slope and elevation), climate (MAT, MAP, aridity index (AI), temperature seasonality (TS), precipitation seasonality (PS)), and vegetation (gross primary production (GPP), aboveground biomass (AGB), below ground biomass (BGB), root depth, leaf area index (LAI), Shannon_EVI). Due to inconsistencies in soil layer depths across profiles, we classified the depth intervals into 0-30 cm, 30-60 cm, and 60-100 cm, following previous studies (Luo et al., 2019; Tautges et al., 2019). Due to non-significant difference of CUE between 30-60 cm and 60-100 cm (Fig. S1), we further defined the 0-30 cm and 30-100 cm as Topsoil and Subsoil (Balesdent et al., 2018), respectively. All topographic, climatic, vegetation variables were applied uniformly across depths, whereas soil properties were assigned specifically to each depth interval (See explanation in Fig. S4). 4.2. Calculation of CUE CUE was derived using an updated biogeochemical equilibrium model, based on the following equations: (1) (2) (3) Where S C:N and S C:P are scalar values representing the extent to which the allocation of extracellular enzyme activity (EEA) offsets the imbalance between the elemental composition of available resources and that of microbial biomass (Sinsabaugh et al., 2016). In this study, EEA C:N was calculated as BG/(NAG+LAP) and EEA C:P was defined as BG/AP. The molar ratios of SOC:TN and SOC:TP were used to estimate L C:N and L C:P , respectively, while B C:N and B C:P were calculated as molar ratios of MBC:MBN and MBC:MBP. For selected articles lacking direct measurements of microbial C, N, and P, mean molar B C:N = 8.6 and B C:P = 60 were used in the CUE calculations (Cleveland and Liptzin, 2007). In addition, K C:N and K C:P were each set to 0.5, representing half-saturation constants for CUE based on C, N, P availability (Sinsabaugh and Follstad Shah, 2012). The maximum value of CUE (CUE max ) was set at 0.6, which is the upper limit for microbial growth efficiency based on thermodynamic constraints (Gommers et al., 1988). 4.3. Development of machine-learning models 80% of samples from our compiled global-scale in situ databases were selected as the training set and the remaining 20% as the test set to diagnose the generalizability of machine-learning models as in (Li et al., 2025). Three machine-learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), were trained to explore the best set of hyperparameters and predictive accuracy using a ‘grid search’ procedure on the entire training set (Xu et al., 2024). Specifically, the ‘holdout’ method was performed, in which the model was trained with 70% of the samples in the training set and validated on the remaining 30%. Package the ‘mlr3verse’ in R software was utilized to train machine-learning models (Lang et al., 2025). The root mean square error (RMSE) and the coefficient of determination ( R 2 ) values produced on the test set were used to evaluate the predictive performance of machine-learning models. 4.4. Global prediction of CUE in top- and subsoils To determine the spatial variation of CUE in topsoils and subsoils, the site-level CUE was treated as the dependent variable and 21 selected factors were considered as the candidate independent variables. To prevent the overfitting of machine-learning models, the final predictor variables were selected using the recursive feature elimination method (Darst et al., 2018). This approach can effectively reduce the number of predictor variables while maintaining the high predictive power of machine-learning models. Results of the recursive feature elimination method were shown in Fig. S9 and S10. All three machine-learning methods were evaluated (Fig. S11 and S12). Among them, the XGBoost model outperformed the other two methods in both layers, with an R 2 and RMSE of 0.94 and 0.04 in topsoils, while an R 2 and RMSE of 0.96 and 0.03 in subsoils. We combined the training and test set to retrain the final XGBoost model using the same set of hyperparameters derived from the XGBoost model development, to map global CUE with a 0.25˚ x 0.25˚ resolution. Among the list of 21 candidate predictor variables, GPP and root depth were excluded from training the final XGBoost model in topsoils, while only 8 factors remained in subsoils, namely MAT, TS, Elevation, pH, CEC, Clay, Bedrock, and LAI (Table S4 and S5). To quantify prediction uncertainty, we first repeated the XGBoost model development 50 times to generate a set of hyperparameters. Using the 50 sets of hyperparameters, we then trained 50 XGBoost models on the full dataset. The final value was obtained by averaging predictions across the 50 models, while the 95% confidence interval was used as an indicator of prediction uncertainty. Uncertainties in the mapped mean CUE are presented in Fig. S8. 4.5. Statistical analysis All statistical analyses were conducted in R (v 4.5.0). To assess differences in CUE across soil layers, we used linear mixed-effects models via the “lmer” function in the “lmerTest” package (Kuznetsova et al., 2017), with “soil layer” as a fixed effect and “sampling site” as a random effect. Pairwise comparisons among soil layers were performed using estimated marginal means (“emmeans”), with 95% confidence intervals. Latitudinal patterns of CUE in topsoils and subsoils were examined using quadratic regression models, while differences between topsoils and subsoils within the same climatic zone were evaluated using the Wilcoxon rank-sum test. To quantify the relative importance of environmental predictors across soil layers, we employed a Random Forest modeling approach using the “rfPermute” function from the “rfPermute” package (Archer, 2025), which effectively captures non-linear relationships and complex interactions among predictors. Notably, soil nutrients, including TN, TP, and SOC, were used to estimate CUE, thus they did not include in Random Forest analysis. Potential causal relationships between environmental factors and CUE were further explored using path analysis based on a priori conceptual model (Fig. S4) and implemented with the “plspm” function in the “plspm” package (Tenenhaus et al., 2005). Data processing and visualization were conducted using the “tidyverse” suite (Hadley Wickham et al., 2019). Declarations Data and code availability Data and codes used in this study are available at: https://github.com/yaqinguo/CUE-in-top--and-subsoil Acknowledgements Y. C. was supported by a fellowship of the Alexander von Humboldt Foundation. We are grateful to scientists who provided numerical data from their published studies, and we would like to thank the HPC Service of FUB-IT, Freie Universität Berlin, for providing computing resources (https://doi.org/10.17169/refubium-26754). Contributions Y. G.: study design, data analysis, and writing. Y. C.: data curation, review and editing. T. C.: review and editing. M.C.R.: review and editing, funding acquisition. Competing interests The authors declare no competing interests. Supplementary information Supplementary information provides additional tables and figures showing detailed information of variables, modeling validation, and prediction results. References Allison, S D, 2025. Rethinking microbial carbon use efficiency in soil models. 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Responses of soil microbial carbon use efficiency to warming: Review and prospects. Soil Ecology Letters 4, 307-318. https://doi.org/10.1007/s42832-022-0137-3 Zosso, C U, Ofiti, N O E, Torn, M S, Wiesenberg, G L B, Schmidt, M W I, 2023. Rapid loss of complex polymers and pyrogenic carbon in subsoils under whole-soil warming. Nature Geoscience 16, 344-348. https://doi.org/10.1038/s41561-023-01142-1 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationNatureGeoscienceCUE.pdf Supplementary_Information Cite Share Download PDF Status: Posted 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. 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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-8625878","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588672771,"identity":"90a37045-9cf6-40e3-b554-6bf4b59bf7c1","order_by":0,"name":"Yongxing Cui","email":"data:image/png;base64,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","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":true,"prefix":"","firstName":"Yongxing","middleName":"","lastName":"Cui","suffix":""},{"id":588672772,"identity":"3efe10f0-a9ee-4075-b66a-daf4ed581ad6","order_by":1,"name":"Yaqin Guo","email":"","orcid":"","institution":"Institute of Biology, Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Yaqin","middleName":"","lastName":"Guo","suffix":""},{"id":588672773,"identity":"e62f5747-6836-40ae-9811-92d22b8902ca","order_by":2,"name":"Tessa Camenzind","email":"","orcid":"","institution":"Department of Soil Biology, University of Hohenheim","correspondingAuthor":false,"prefix":"","firstName":"Tessa","middleName":"","lastName":"Camenzind","suffix":""},{"id":588672774,"identity":"2c0a6752-73e1-40b5-bd98-05fe65e63c51","order_by":3,"name":"Matthias Rillig","email":"","orcid":"https://orcid.org/0000-0003-3541-7853","institution":"Free University of Berlin","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Rillig","suffix":""}],"badges":[],"createdAt":"2026-01-17 12:15:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8625878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8625878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102394214,"identity":"18b6c7ce-e632-4dd9-9923-4cfe25a55e2c","added_by":"auto","created_at":"2026-02-11 09:21:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":549063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatterns of microbial carbon use efficiency (CUE) across sampling sites.\u003c/strong\u003ea) Whittaker’s Biome Diagram, showing annual temperature (˚C) and annual precipitation (cm) for all 1193 values. Each point represents a sampling point. b) Differences of CUE between topsoils and subsoils. The boxes represent the first and the third quartiles. The line within the box represents the median. The whiskers represent the data range and points indicate individual values. Different letters denote significant differences (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001) based on the analysis of linear mixed-effect models followed by estimated marginal means. c) Latitudinal patterns of CUE fitted by quadratic models in both topsoil and subsoil, respectively. Formulas and adjusted coefficients were annotated in the plot; \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001\u003cem\u003e \u003c/em\u003eindicates statistical significance of the overall regression model. d) Differences between topsoils and subsoils among different climate zones. The boxes represent the first and the third quartiles. The line within the box represents the median. The whiskers represent the data range and points indicate individual values. **** means \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001 and ns denotes not significant. The polar climate zone only has few data points, we included these data points into the cold zone.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625878/v1/f77cbaa7820b9c3faf892fbf.jpg"},{"id":102394213,"identity":"75bf5cf2-a852-4cbe-8b0c-2754d0c27d63","added_by":"auto","created_at":"2026-02-11 09:21:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey environmental factors driving microbial carbon use efficiency (CUE).\u003c/strong\u003e Random Forest model identifies important variables a) in topsoil and b) in subsoil. See Fig. S2 for method validation. MAT: mean annual temperature; MAP: mean annual precipitation; TS: temperature seasonality; PS: precipitation seasonality; AI: aridity index; GPP: gross primary production; LAI: leaf area index; AGB: aboveground biomass; BGB: belowground biomass; BD: bulk density; CEC: cation exchange capacity. Path modeling showing cascading relationships of topography, climatic conditions, seasonality, vegetation, clay content, soil properties, and CUE c) in topsoil and e) in subsoil. The red dotted arrows indicated positive effect and the blue dotted arrows denoted negative effect, gray arrows mean non-significant. The width of the arrows is proportional to the strength of the path coefficients. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e indicated the proportion of variance explained. Models were evaluated by a goodness-of-fit (GOF) statistic. * \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; *** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001. A \u003cem\u003epriori\u003c/em\u003e path model is shown in Fig. S4. Standardized total effects (sum of direct plus indirect effects) of latent variables on CUE derived from path analysis d) in topsoil and e) in subsoil. Detailed results of path analysis are shown in Table S1 and Table S2.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625878/v1/4b0a0e14069f57c201cc37b8.jpg"},{"id":102394217,"identity":"5c078102-1917-4dc9-8694-28d26ebacd4d","added_by":"auto","created_at":"2026-02-11 09:21:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal patterns of microbial carbon use efficiency (CUE) in topsoil and subsoil. \u003c/strong\u003ePrediction maps of CUE at 0.25˚ x 0.25˚ resolution a) in topsoil and c) in subsoil. The latitudinal profiles of CUE b) in topsoil and d) in subsoil. Plots show the mean CUE trend across latitude (shaded area is 95% confidence interval). The solid line denotes the mean value of CUE and the dashed line indicates the trend line of CUE across latitude. Areas of water, permanent wetlands, cropland, urban/built-up areas, and snow/ice areas were excluded from projections.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625878/v1/df0acde28076665460ba2494.jpg"},{"id":102394215,"identity":"dbeb4ece-fb76-42c9-88b5-19183de128a1","added_by":"auto","created_at":"2026-02-11 09:21:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal hotspots of microbial carbon use efficiency (CUE) across soil layers. \u003c/strong\u003ea) Bivariate global map showing joint variation in topsoil and subsoil CUE. Diagonal colors indicate similar values and off-diagonal tones highlighting layer contrast. Areas of water, permanent wetlands, cropland, urban/built-up areas, and snow/ice areas were excluded from projections. Boxplots across b) climate zones and c) biomes. The boxes represent the first and the third quartiles. The line within the box represents the median. The whiskers represent the data range and points indicate individual values.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8625878/v1/aee691c0ef42353f56ca1e5d.jpg"},{"id":107704687,"identity":"aaf85951-3968-41a7-b0c4-74bcb05b8d28","added_by":"auto","created_at":"2026-04-24 08:54:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1345891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8625878/v1/b157c34d-f68b-42cf-b161-c02fde03979c.pdf"},{"id":102394218,"identity":"542c7ffd-74a9-4043-95f7-28f6c0aecf84","added_by":"auto","created_at":"2026-02-11 09:21:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3823768,"visible":true,"origin":"","legend":"Supplementary_Information","description":"","filename":"SupplementaryInformationNatureGeoscienceCUE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8625878/v1/734ab0efadc68587604cd84d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"From plants to minerals: depth-dependent controls on microbial carbon use efficiency across the global","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eSoils are the largest carbon (C) pool of the terrestrial ecosystems and therefore play critical role in mediating climate change (Paustian et al., 2016; Balesdent et al., 2018). The turnover of soil organic carbon (SOC) is primarily regulated by microorganisms that assimilate C into biomass while releasing CO\u003csub\u003e2\u003c/sub\u003e through respiration (Crowther et al., 2019; Beattie et al., 2024). The efficiency with which microorganisms allocate assimilated C to growth rather than respiration, defined as microbial carbon use efficiency (CUE), is a key determinant of SOC storage (Manzoni et al., 2018; He et al., 2024). Subsoils store more than half of SOC in terrestrial ecosystems and exhibit substantially longer SOC turnover time than topsoils (Rumpel and K\u0026ouml;gel-Knabner, 2011; Luo et al., 2019; Wang et al., 2025). Yet, how CUE varies with soil depths and mechanisms governing this variation remain unclear, constraining our ability to predict whole-profile SOC dynamics and their responses to climate change.\u003c/p\u003e\n\u003cp\u003eThe prevailing assumptions expect that CUE declines with soil depth, due to sharp decrease in fresh and labile C from plants (Zhang et al., 2023; Guo et al., 2025), forcing microorganisms to invest more energy in extracellular enzymes to access recalcitrant substrates in deeper layers (Fontaine et al., 2007; Huang and Su, 2025). However, empirical evidence is highly inconsistent. While a recent study on continental scale reported declining CUE with depth (Pei et al., 2025), other site-specific studies have shown increasing trends or no significant variation (Spohn et al., 2016; Li et al., 2021). These discrepancies suggest that fundamental controls on CUE may differ between soil layers. Although various factors such as climatic conditions (e.g., temperature), substrate quantity and quality, and soil physicochemical properties, are known to primarily influence microbial metabolism (Manzoni et al., 2012; Zhang et al., 2022; Cui et al., 2024; Shi et al., 2024; Hu et al., 2025), a comparative assessment of drivers behind CUE variations among different layers remains lacking. As a result, identifying the relative importance of these environmental drivers in CUE globally across depths is critical for improving CUE representation in Earth System models (Allison, 2025).\u003c/p\u003e\n\u003cp\u003eIn topsoils, microbial activity is tightly coupled to the quantity and quality of fresh C inputs from vegetation (Button et al., 2022; Huang et al., 2023). In contrast, subsoils are substrate-poor environments where C resources are often older, more chemically complex, or physically protected organic matter (Eusterhues et al., 2003; Hicks Pries et al., 2017; Zosso et al., 2023). As a result, soil physicochemical properties such as clay content, aggregate structure, and pH play a central role in regulating the accessibility and transformation of organic matter through organo-mineral interactions and physical protection in deeper soil layers (Wordell-Dietrich et al., 2017; Luo et al., 2019; Qin et al., 2019; Kang et al., 2021). Supporting this view, a recent deep-soil manipulation experiment found that CUE in subsoils, but not in topsoils, increased following glucose addition and aggregate disruption, suggesting co-limitation of microbial growth by substrate availability and physical inaccessibility in subsoils (Pei et al., 2025). Together, these findings imply that while C inputs primarily drive topsoil CUE, C accessibility and mineral protection likely exert dominant control on subsoil CUE.\u003c/p\u003e\n\u003cp\u003eHere, we hypothesize that (1) CUE is systematically higher in topsoils than in subsoils due to greater C availability; (2) vegetation production, the primary sources of fresh C inputs, exerts the dominant control on CUE in topsoils, whereas physicochemical properties, particularly mineral associations that regulate C accessibility, are the primary drivers of CUE in subsoils. To test these hypotheses, we compiled a global dataset from natural ecosystems spanning a wide range of climatic zones and biomes (Fig. 1a). We estimated community-level CUE using a culture-independent stoichiometric model, which captures the \u003cem\u003ein-situ\u003c/em\u003e adaptation of microbial communities to their resource environment (Sinsabaugh et al., 2013; Schimel et al., 2022). We then examined relationships between CUE and 21 potential drivers related to topography, climate, vegetation, and soil properties, to identify the dominant controls of CUE in topsoils and subsoils. Finally, we integrated these factors to generate global maps of CUE (0.25˚ x 0.25˚ resolution) for both layers.\u003c/p\u003e"},{"header":"2.\tResults","content":"\u003ch2\u003e\u003cstrong\u003e2.1\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003ePatterns of CUE across sampling sites\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAcross all sites CUE\u003csub\u003e\u0026nbsp;\u003c/sub\u003ewas significantly higher in subsoils than in topsoils (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, linear mixed effects model; Fig. 1b), though the effect size was modest (0.02). The estimated marginal means were 0.37 (95% confidence interval (CI): 0.34-0.41) for subsoils and 0.35 (95% CI: 0.31-0.38) for topsoils. Across soil layers (0-30, 30-60, and 60-100 cm), CUE in the 30-60 cm and 60-100 cm layers was significantly higher than in the 0-30 cm layer, with no statistical significance between the two deeper layers (Fig. S1a). Therefore, the 30-60 and 60-100 cm layers were pooled and designated as subsoils for subsequent analyses.\u003c/p\u003e\n\u003cp\u003eCUE displayed contrasting latitudinal patterns between topsoils and subsoils (Fig. 1c and Fig. S1b). In tropical regions, CUE was significantly higher in topsoils than in subsoils, whereas in cold regions the opposite pattern was observed, with subsoil CUE significantly exceeding that of topsoil (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001; Fig. 1e). In temperate and arid regions, no significant differences between topsoils and subsoils were detected (\u003cem\u003eP\u003c/em\u003e = 0.49 and \u003cem\u003eP\u003c/em\u003e = 0.76, respectively; Fig. 1e).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.2\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003eEnvironmental drivers of CUE in different layers\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo identify the key drivers of CUE, we employed Random Forest analysis on a comprehensive set of predictors representing topographic, climatic, vegetation, and soil properties (Fig. S2). When data from topsoils and subsoils were combined, the analysis revealed that soil depth was a significant predictor of CUE (Fig. S3). In topsoils, bulk density emerged as the single most important predictor, whereas in subsoils, clay content was the most important predictor (Fig. 2a-b). Beyond these primary drivers, soil pH was identified as the second most important predictor in both layers (Fig. 2a-b). While these factors were dominant, the analysis highlighted the complex nature of CUE, with multiple predictors showing statistical significance in both topsoils and subsoils.\u003c/p\u003e\n\u003cp\u003eTo further elucidate the causal relationships among predictor groups, path analysis was conducted to quantify the direct and indirect effects of topography, climate, seasonality, soil texture, vegetation, and soil properties on CUE based on a \u003cem\u003epriori\u003c/em\u003e model (Fig. S4). In topsoils, vegetation-related metrics\u0026ndash;including gross primary productivity (GPP), Shannon_EVI and belowground biomass (BGB)\u0026ndash;exerted the strongest positive effect on CUE (Fig. 2c, d and Table S1). In contrast, CUE in subsoils was primarily constrained by soil physicochemical properties, particularly pH, bulk density, and moisture, which showed pronounced negative effects (Fig. 2e, f and Table S2).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e2.3\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003eGlobal prediction of CUE in topsoils and subsoils\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo extrapolate our site-level findings to the global scale, we generated spatially explicit maps of CUE at 0.25˚ x 0.25˚ resolution using the XGBoost algorithm (Figs. 3 and 4). Globally, mean CUE was 0.35 (95% CI: 0.26-0.43) in topsoils and slightly higher at 0.37 (95% CI: 0.28-0.45) in subsoils (Figs. 3 and 4). In both layers, CUE increased from the equatorial to high-latitude regions (Fig. 3b, d), consistent with patterns across climate zones (Fig. 4b). Across biomes, CUE in subsoils generally exceeded topsoils, and grasslands exhibited higher CUE than forests (Figs. 4d and S5).\u003c/p\u003e"},{"header":"3.\tDiscussion","content":"\u003cp\u003eHigher CUE in subsoils than in topsoils indicates that deep-soil microbial communities allocate a greater fraction of assimilated C to biomass relative to respiration. This finding challenges the conventional view that decomposition of low-quality substrates requires higher energy, therefore leading to lower metabolic efficiency (Bosatta and \u0026Aring;gren, 1999; Mganga et al., 2022). Instead, our findings align with ecological theory that microorganisms in resource-limited environments employ efficiency-oriented metabolic strategies to sustain long-term survival (Roller and Schmidt, 2015). This interpretation is supported by global syntheses, which showed that microbial communities utilizing complex substrates (e.g., cellulose, plant residues, necromass) generally exhibit higher CUE than those metabolizing labile glucose (Qiao et al., 2019). Similarly, microbial communities with genes for degrading recalcitrant C (e.g., lipids and lignin) have higher CUE when supplied with low-quality C sources (Ren et al., 2024). Collectively, these findings consistently suggest that subsoil microbial communities may possess evolutionary adaptations that enable them to thrive in low-energy, resource-constrained environments.\u003c/p\u003e\n\u003cp\u003eFurthermore, clay content was identified as the strongest predictor of CUE in subsoils (Fig. 2), suggesting the critical role of mineral protection of SOC in meditating CUE. Mineral protection is a well-known mechanism governing the fate of organic substrates in deeper horizons, where organo-mineral associations, sorption, and physical occlusion constrain substrate accessibility and microbial metabolism (Eusterhues et al., 2003; Dungait et al., 2012; Lehmann and Kleber, 2015; Xiao et al., 2024). Soil physiochemical properties, such as soil pH and moisture as identified by our path modeling (Fig. 2), could further shape the capacity of minerals to protect organic matter and regulate microbial activity (Six et al., 2002; Malik et al., 2018; Luo et al., 2019; Luo et al., 2021). Together, our findings indicate that subsoil CUE is primarily regulated by mineral constraints and soil physicochemical conditions.\u003c/p\u003e\n\u003cp\u003eIn topsoils, however, CUE is fundamentally linked to vegetation productivity, which provide fresh C and foster the development of soil structure as supported by both path analysis and Random Forest model (Fig. 2). Notably, bulk density, the most important predictor of CUE in topsoils identified by Random Forest model not only reflects soil structure (Heuscher et al., 2005; Niedźwiecka et al., 2025), but also can serve as a robust proxy of organic matter content and substrate accessibility (Fu et al., 2021; Guo et al., 2025). Indeed, the organic carbon in topsoils is primarily from plant C inputs, with higher vegetation productivity generally leading to higher SOC content and lower bulk density (Cotrufo et al., 2013; Sinsabaugh et al., 2017; Cui et al., 2023; Huang et al., 2023). As a result, topsoil CUE is regulated by biologically mediated soil structure and vegetation-derived C inputs.\u003c/p\u003e\n\u003cp\u003eGlobal prediction illustrated spatial patterns of CUE across soil layers. The global mean CUE was consistently higher in subsoils than in topsoils, suggesting greater microbial C retention in deeper horizons that potentially contributes to long-term SOC sequestration (Tao et al., 2023; Yang et al., 2025). Our models suggested a poleward increase in CUE across soil layers, likely reflecting temperature constraints on microbial metabolism, whereby higher respiration costs in warm tropics reduce efficiency relative to cooler temperate and boreal zones (Sinsabaugh et al., 2013; Wang et al., 2021; Cui et al., 2024). This pattern implies that CUE in high-latitude soils may be more vulnerable than in low latitudes due to the amplified warming occurring at high latitudes (Post et al., 2019; Rantanen et al., 2022). Additionally, we found that mean annual temperature was not a primary driver of CUE in either layer (Fig. 2). Although it correlated significantly with CUE, the direction of the relationship differed between soil layers and was generally weak (Fig. S6). Despite temperature-dependent functions being widely used to parameterize CUE (Wieder et al., 2015; Qiao et al., 2019; Ye et al., 2019), our findings emphasize the necessity of considering the distinction between topsoil and subsoil processes, when simplify CUE as a function of temperature.\u003c/p\u003e\n\u003cp\u003eDespite providing an important global investigation of CUE estimates, our study includes several limitations. First, our estimates of CUE relied on the stoichiometric modeling that assumes microorganisms regulate extracellular enzyme activities to balance the elemental (C, N, or P) composition of their substrates (Sinsabaugh and Follstad Shah, 2012). While this approach leverages widely available data, enzyme-based metrics may not fully capture transient microbial nutrient limitations due to potential temporal lags in enzyme production and turnover (Cui et al., 2025). Integrating this method with isotope tracer incubation experiments and genomic data can provide a more comprehensive understanding of global CUE dynamics (Geyer et al., 2019; Saifuddin et al., 2019). Second, though our dataset spans multiple climate zones, it is geographically biased towards the northern hemisphere (Fig. S7). Expanding data coverage in the southern hemisphere is necessary to reduce prediction uncertainties in global maps (Meyer and Pebesma, 2022). Finally, given that subsoils play a pivotal role in long-term C sequestration yet remain understudied, targeted investigation of subsoil microbial processes will be essential for refining global C cycle models.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study reveals that CUE was higher in subsoils than in topsoils, and was driven by distinct environmental factors across soil layers. While topsoil CUE was most strongly influenced by vegetation-derived C inputs, subsoil CUE was constrained by mineral protection and soil physicochemical conditions that govern C accessibility. Recognizing these depth-dependent mechanisms underscores the importance of viewing subsoils as distinct, microbially active compartments rather than extensions of topsoils. Incorporating vertical heterogeneity in microbial functioning is essential for advancing predictions of long-term soil C storage under changing climate.\u003c/p\u003e"},{"header":"4.\tMethods","content":"\u003ch2\u003e\u003cstrong\u003e4.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;Data collection\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe conducted a systematic literature search using the keywords \u0026ldquo;soil extracellular enzymes OR soil enzymes\u0026rdquo; in the Web of Science (https://www.webofscience.com) and the Google scholar Integrated Database (https://scholar.google.com) ranging from 1980 to 2024. Although the terms are broad, they ensure comprehensive retrieval of studies reporting soil enzyme data across diverse ecosystems, which we then refine through well-defined inclusion criteria to capture studies relevant for CUE estimation in both topsoils and subsoils. The following criteria needed to be met: 1) Soil physicochemical characteristics and extracellular enzyme activity should be measured simultaneously, and the activities of these ecoenzymes must be measured fluorometrically using a 200 \u0026mu;M solution of substrate labeled with 4-methylumbelliferone or 7-amino-4-methylcoumarin. The following C, N, and P-acquiring enzymes should be included. Namely, \u0026beta;-1,4-glucosidase (BG), \u0026beta;-1, 4-N-acetylglucosaminidase (NAG) and / or L-leucine aminopeptidase (LAP), and acid or alkaline phosphatase (AP). Soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) must be available. Microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and microbial biomass phosphorus (MBP) were obtained from the same studies when reported. In total, 64.7% of MBC data, 63.6% of MBN data, and 57.4% of MBP data were retrieved, representing the proportion of all collected data that met the required quality criteria. These parameters were essential components to calculate CUE based on a biogeochemical equilibrium model. 2) Studies with natural or field conditions were included and greenhouse or laboratory experiments were excluded. 3) Wetland ecosystems were excluded because the biogeochemical cycles in wetlands are very different from those in terrestrial soils (Bahram et al., 2022). 4) Samples from litter layers were excluded (Takele et al., 2025). In total, our database comprised 1193 data points from 103 sites (Fig. S7). Data were extracted from tables or figures of selected papers using GetData Graph Digitizer v.2.25. In addition, we collected the following parameters: location (latitude and longitude), elevation, mean annual temperature (MAT), mean annual precipitation (MAP), and various soil properties including soil texture, pH, soil moisture, bulk density (BD), and cation exchange capacity (CEC). For data not directly recorded in the study, we supplemented information from relevant databases (Table S3). To comprehensively identify potential environmental drivers, we also retrieved other variables from multiple databases at relatively fine spatial resolutions (Table S3). These include topography (slope and elevation), climate (MAT, MAP, aridity index (AI), temperature seasonality (TS), precipitation seasonality (PS)), and vegetation (gross primary production (GPP), aboveground biomass (AGB), below ground biomass (BGB), root depth, leaf area index (LAI), Shannon_EVI). Due to inconsistencies in soil layer depths across profiles, we classified the depth intervals into 0-30 cm, 30-60 cm, and 60-100 cm, following previous studies (Luo et al., 2019; Tautges et al., 2019). Due to non-significant difference of CUE between 30-60 cm and 60-100 cm (Fig. S1), we further defined the 0-30 cm and 30-100 cm as Topsoil and Subsoil (Balesdent et al., 2018), respectively. All topographic, climatic, vegetation variables were applied uniformly across depths, whereas soil properties were assigned specifically to each depth interval (See explanation in Fig. S4).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;Calculation of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCUE\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCUE was derived using an updated biogeochemical equilibrium model,\u0026nbsp;based\u0026nbsp;on the following equations:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"449\" height=\"19\" 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\" alt=\"image\"\u003e\u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"131\" height=\"29\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;(2)\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"127\" height=\"29\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAL8AAAAsCAMAAAADxpnZAAAAAXNSR0IArs4c6QAAAJNQTFRFAAAAAAAAAAA6AABmADo6ADpmADqQAGa2OgAAOgA6OgBmOjoAOjpmOjqQOmaQOma2OpC2OpDbZgAAZgA6ZjoAZjo6ZmYAZmY6ZpC2ZpDbZrbbZrb/kDoAkDo6kGYAkGY6kNv/tmYAtmY6tpA6ttv/tv//25A625Bm27Zm27aQ29u22////7Zm/9uQ/9u2//+2///bUiMBNAAAAAF0Uk5TAEDm2GYAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAAZdEVYdFNvZnR3YXJlAE1pY3Jvc29mdCBPZmZpY2V/7TVxAAADZUlEQVRoQ+1Z2XbaMBCVIGncJaG4K4Eupo0Lxcb6/6/rLJY3FLDHg08fMg8ccYhGd0azXE2MeZEXD/wPHkgtymyljWX3+qe2yrC+ZP5k3C+7Vj1t/9GC2inEbV4djMmjpeZh2eKQToS/iO8Aeabsf2Omwp9HEDn7aKHpftQ1FX7K39mjNvzJ8HP6zqhYHH+ALfecd26Dht2IDZvI/xz+nL55dHeo/VbES+MSNkwgE+Fn5JmFzyKmSnQPHyg5FvD0qvh3b/GKH34L3OO3EMB9hMWuwuo2eCfowCOZJBG3uZhT8CerA5wtdhE5nqL8K6B0G1+xGX+Ceb2QwS9i0nu+p6T8OyaghnAmVILhf1Xx/kqlV9xBx16vwv+ko1W/GPNHgy0VsZLjvR1Ya454CWTJthuWqfWOSq1Gt4MY0+VcblsWfMQPba3rHm9AaluBJg6yxM56G8B56kWY8WyAFnzqkML6IPQZGgDwZTXp9Ey8cKXk7WlQam/04MOZu2hkLLbCKvylaZpL2lnRY/t5pZktY/nvJ6Re5c3ym/BB655rC9D/qjcOPRj6PybV+4P7XqnGnpY3e79K/mLqQvyrGJC/oa5bNjHiXybzdY3fhFqNuXI/p66OASXZAsoL6pk5Nns/ERjl/uYrj0oFSuwt0M59dIvXwO4nIbvoTciXoCd13d+OLBqIaf+FuPMjQWy8Mgg/frgTAjDSlKwmDVvxqyyIoWa+5c9Ife27Mc+CkbYO296NlG46nNcWYDoDjudSTVS/sUQFHT5+TidnauLZ0MBhlPyNS7GLZ1PCNZf4rX+hzezKzxAw9NNBxHRcnvPu4gNkXWOJNWX+uX/9AB4B72DMZcAPvWoIwRxwz4EYaOxuKSri9WQTiN4sPIAfI8d9A+9zEJVLiB5gqsr955kMko9IUCElLQdKY0nz1HGKe9eQuk1Lxm+4u+QtjSWFUnYy1Bs70wvZVEetZPz2TPiXpRQDs6V15EwvhJ+LbbqUjd/KwSMS99MlFtX2UG/kTC+EvyJMovEbbTryvK5aHnkykiFN8lo1Znoh+OXDYP4kGr/5ZwU4ul5ilAPyHGr6utI6fqZ3IaGvM35ra73mTO/S+K13PWv9YVur/n+pGoddGL/J8BO1x6FeeKYnVBrcdmn8JjvLa+3M9P4BxVdZdWkaBUEAAAAASUVORK5CYII=\" alt=\"image\"\u003e\u0026nbsp;(3)\u003c/p\u003e\n\u003cp\u003eWhere S\u003csub\u003eC:N\u003c/sub\u003e and S\u003csub\u003eC:P\u003c/sub\u003e are scalar values representing the extent to which the allocation of extracellular enzyme activity (EEA) offsets the imbalance between the elemental composition of available resources and that of microbial biomass (Sinsabaugh et al., 2016).\u0026nbsp;In this study, EEA\u003csub\u003eC:N\u0026nbsp;\u003c/sub\u003ewas calculated as BG/(NAG+LAP) and EEA\u003csub\u003eC:P\u003c/sub\u003e was defined as BG/AP. The molar ratios of SOC:TN and SOC:TP were used to estimate L\u003csub\u003eC:N\u003c/sub\u003e and L\u003csub\u003eC:P\u003c/sub\u003e, respectively, while B\u003csub\u003eC:N\u003c/sub\u003e and B\u003csub\u003eC:P\u003c/sub\u003e were calculated as molar ratios of MBC:MBN and MBC:MBP. For selected articles lacking direct measurements of microbial C, N, and P, mean molar B\u003csub\u003eC:N\u003c/sub\u003e = 8.6 and B\u003csub\u003eC:P\u003c/sub\u003e = 60 were used in the CUE calculations (Cleveland and Liptzin, 2007). In addition, K\u003csub\u003eC:N\u003c/sub\u003e and K\u003csub\u003eC:P\u003c/sub\u003e were each set to 0.5, representing half-saturation constants for CUE based on C, N, P availability (Sinsabaugh and Follstad Shah, 2012). The maximum value of CUE (CUE\u003csub\u003emax\u003c/sub\u003e) was set at 0.6, which is the upper limit for microbial growth efficiency based on thermodynamic constraints (Gommers et al., 1988).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;Development of machine-learning models\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e80% of samples from our compiled global-scale \u003cem\u003ein situ\u003c/em\u003e databases were selected as the training set and the remaining 20% as the test set to diagnose the generalizability of machine-learning models as in (Li et al., 2025). Three machine-learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), were trained to explore the best set of hyperparameters and predictive accuracy using a \u0026lsquo;grid search\u0026rsquo; procedure on the entire training set (Xu et al., 2024). Specifically, the \u0026lsquo;holdout\u0026rsquo; method was performed, in which the model was trained with 70% of the samples in the training set and validated on the remaining 30%. Package the \u0026lsquo;mlr3verse\u0026rsquo; in R software was utilized to train machine-learning models (Lang et al., 2025). The root mean square error (RMSE) and the coefficient of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) values produced on the test set were used to evaluate the predictive performance of machine-learning models.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.4.\u0026nbsp; \u0026nbsp; \u0026nbsp;Global prediction of CUE in top- and subsoils\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTo determine the spatial variation\u0026nbsp;of CUE in topsoils and subsoils, the site-level CUE was treated as the dependent variable and 21 selected factors were considered as the candidate independent variables. To prevent the overfitting of machine-learning models, the final predictor variables were selected using the recursive feature elimination method (Darst et al., 2018). This approach can effectively reduce the number of predictor variables while maintaining the high predictive power of machine-learning models. Results of the recursive feature elimination method were shown in Fig. S9 and S10. All three machine-learning methods were evaluated (Fig. S11 and S12). Among them, the XGBoost model outperformed the other two methods in both layers, with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eand RMSE of 0.94 and 0.04 in topsoils, while an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003eand RMSE of 0.96 and 0.03 in subsoils. We combined the training and test set to retrain the final XGBoost model using the same set of hyperparameters derived from the XGBoost model development, to map global CUE with a 0.25˚ x 0.25˚ resolution. Among the list of 21 candidate predictor variables, GPP and root depth were excluded from training the final XGBoost model in topsoils, while only 8 factors remained in subsoils, namely MAT, TS, Elevation, pH, CEC, Clay, Bedrock, and LAI (Table S4 and S5). To quantify prediction uncertainty, we first repeated the XGBoost model development 50 times to generate a set of hyperparameters. Using the 50 sets of hyperparameters, we then trained 50 XGBoost models on the full dataset. The final value was obtained by averaging predictions across the 50 models, while the 95% confidence interval was used as an indicator of prediction uncertainty. Uncertainties in the mapped mean CUE are presented in Fig. S8.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted in R (v 4.5.0). To assess differences in CUE across soil layers, we used linear mixed-effects models via the \u0026ldquo;lmer\u0026rdquo; function in the \u0026ldquo;lmerTest\u0026rdquo; package (Kuznetsova et al., 2017), with \u0026ldquo;soil layer\u0026rdquo; as a fixed effect and \u0026ldquo;sampling site\u0026rdquo; as a random effect. Pairwise comparisons among soil layers were performed using estimated marginal means (\u0026ldquo;emmeans\u0026rdquo;), with 95% confidence intervals. Latitudinal patterns of CUE in topsoils and subsoils were examined using quadratic regression models, while differences between topsoils and subsoils within the same climatic zone were evaluated using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003eTo quantify the relative importance of environmental predictors across soil layers, we employed a Random Forest modeling approach\u0026nbsp;using the \u0026ldquo;rfPermute\u0026rdquo; function from the \u0026ldquo;rfPermute\u0026rdquo; package (Archer, 2025), which effectively captures non-linear relationships and complex interactions among predictors. Notably, soil nutrients, including TN, TP, and SOC, were used to estimate CUE, thus they did not include in Random Forest analysis. Potential causal relationships between environmental factors and CUE were further explored using path analysis based on a \u003cem\u003epriori\u003c/em\u003e conceptual model (Fig. S4) and implemented with the \u0026ldquo;plspm\u0026rdquo; function in the \u0026ldquo;plspm\u0026rdquo; package (Tenenhaus et al., 2005). Data processing and visualization were conducted using the \u0026ldquo;tidyverse\u0026rdquo; suite (Hadley Wickham et al., 2019).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData and code availability\u003c/p\u003e\n\u003cp\u003eData and codes used in this study are available at: https://github.com/yaqinguo/CUE-in-top--and-subsoil\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eY. C. was supported by a fellowship of the Alexander von Humboldt Foundation. We are grateful to scientists who provided numerical data from their published studies, and we would like to thank the HPC Service of FUB-IT, Freie Universit\u0026auml;t Berlin, for providing computing resources (https://doi.org/10.17169/refubium-26754).\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eY. G.:\u0026nbsp;study design, data analysis, and writing. Y. C.: data curation, review and editing. T. C.: review and editing. M.C.R.: review and editing, funding acquisition.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eSupplementary information\u003c/p\u003e\n\u003cp\u003eSupplementary information provides additional tables and figures showing detailed information of variables, modeling validation, and prediction results.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAllison, S D, 2025. Rethinking microbial carbon use efficiency in soil models. 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Nature Geoscience 16, 344-348. https://doi.org/10.1038/s41561-023-01142-1\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil carbon cycling, Soil profile, Spatial variations, Microbial metabolism, Ecological stoichiometry","lastPublishedDoi":"10.21203/rs.3.rs-8625878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8625878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Microbial carbon use efficiency (CUE) represents a key trait linking microbial metabolism to soil carbon (C) cycling. While subsoils store over 50% of total soil C and are supposed to be more vulnerable to global change than topsoils, the patterns and controls of CUE in subsoils remain unclear, limiting predictions of whole-profile soil C dynamics. Here, we estimated CUE in topsoils (n = 814) and subsoils (n = 379) worldwide using an enzyme-based stoichiometric model and identified dominant drivers in each layer. We found that subsoil CUE was significantly higher than topsoil CUE, indicating a greater allocation of assimilated C to microbial biomass relative to respiration in deeper soils. Topsoil CUE was primarily influenced by vegetation-derived C inputs, whereas subsoil CUE was strongly constrained by mineral protection and soil physicochemical conditions, which suggests subsoil CUE may be less sensitive to global change than previously assumed. Global prediction revealed a poleward increase in CUE across layers, highlighting high soil C retention potential at high latitudes. This geographical pattern also implies that high-latitude soil C is vulnerable and may experience accelerated loss under ongoing climate warming.","manuscriptTitle":"From plants to minerals: depth-dependent controls on microbial carbon use efficiency across the global","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 09:21:10","doi":"10.21203/rs.3.rs-8625878/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bee6e55e-3d2e-4823-a354-2c6c6926ad40","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62627809,"name":"Earth and environmental sciences/Ecology/Microbial ecology"},{"id":62627810,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"},{"id":62627811,"name":"Earth and environmental sciences/Ecology/Climate-change ecology"}],"tags":[],"updatedAt":"2026-03-27T07:27:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 09:21:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8625878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8625878","identity":"rs-8625878","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}