Geoderma最新文献

{"title":"Quantifying spatial and vertical variations in soil C:N relationships in permafrost-affected landscapes","authors":"Joshua O. Minai ,&nbsp;Julie D. Jastrow ,&nbsp;Roser Matamala ,&nbsp;Chien-Lu Ping ,&nbsp;Gary J. Michaelson ,&nbsp;Nicolas A. Jelinski","doi":"10.1016/j.geoderma.2025.117418","DOIUrl":"10.1016/j.geoderma.2025.117418","url":null,"abstract":"<div><div>Permafrost regions are experiencing rapid changes that affect carbon (C) and nitrogen (N) cycles, with implications for vegetation dynamics and gas exchanges with the atmosphere. Soil C:N ratio is a key indicator of organic matter quality, yet spatial estimates of N stocks and C:N ratios lag behind those for C. We used quantile regression forests to compare direct and indirect digital soil mapping approaches for predicting soil C:N ratios at 0–30, 30–60, and 60–100 cm depths across a latitudinal transect in Alaska. The indirect approach – deriving C:N from separately predicted C and N stocks – outperformed direct mapping for the surface layer (0–30 cm), while direct mapping was marginally better at greater depths. However, prediction accuracy decreased with depth for both methods. Temperature and topography were the most important predictors. Both approaches overestimated low and underestimated high C:N ratios, with direct mapping showing greater bias. Our results underscore the challenges of modeling C:N ratios in heterogeneous, data-sparse permafrost soils, but also suggest that indirect mapping holds promise if supported by more extensive datasets.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117418"},"PeriodicalIF":5.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active motility and chemotactic movement regulate the microbial early-colonization and biodiversity","authors":"Yan Wang ,&nbsp;Ali Ebrahimi ,&nbsp;Guowei Chen ,&nbsp;Zi Zhang ,&nbsp;Kun Zhu ,&nbsp;Shane Franklin ,&nbsp;Yan Jin ,&nbsp;Ying Liu ,&nbsp;Gang Wang","doi":"10.1016/j.geoderma.2025.117419","DOIUrl":"10.1016/j.geoderma.2025.117419","url":null,"abstract":"<div><div>Microbial dispersal and subsequent colonization of new niches are fundamental processes in microbial ecology, particularly in patchy environments like soil. However, the heterogeneity of soil pore spaces and the resulting fragmented aqueous habitats are known to significantly impede microbial dispersal rates and ranges. Despite this, the strategies microbes employ to overcome these abiotic constraints remain poorly understood. To address this, we developed a novel experimental system using porous ceramic surfaces to simulate hydrated soil environments, enabling direct quantification of early-stage bacterial colonization. Our findings reveal that distinct taxonomic and functional bacterial populations successfully colonized the porous ceramic surfaces, differing significantly from the original soil communities. Active motility and chemotaxis emerged as two key traits facilitating early-stage colonization. However, the advantages conferred by motility and chemotaxis were significantly reduced under drier soil conditions, typically at water contents below 25% (v/v). Under such conditions, non-motile bacteria relied on passive dispersal mechanisms or physical adhesion to colonize the porous surfaces. Furthermore, functional metagenomic profiling of the colonizing microbial populations uncovered a trade-off between growth and dispersal rates. This observed trade-off was incorporated into an agent-based model simulating microbial activity in soil, which explored how correlations between microbial functional genes influence community dynamics during early colonization. The simulations demonstrated that the growth-dispersal trade-off is crucial for enhancing and maintaining microbial diversity during colonization of new niches. Our study elucidates the key biophysical mechanisms driving microbial early-stage colonization dynamics from bulk soil to new environments, highlighting this process as a core ecological phenomenon in soil ecosystems.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117419"},"PeriodicalIF":5.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gemmatirosa adaptations to arid and low soil organic carbon conditions worldwide","authors":"Yuanyuan Bao ,&nbsp;Tadeo Sáez-Sandino ,&nbsp;Youzhi Feng ,&nbsp;Xuebin Yan ,&nbsp;Shiying He ,&nbsp;Shilun Feng ,&nbsp;Ruirui Chen ,&nbsp;Hui Guo ,&nbsp;Manuel Delgado-Baquerizo","doi":"10.1016/j.geoderma.2025.117420","DOIUrl":"10.1016/j.geoderma.2025.117420","url":null,"abstract":"<div><div>Aridity and warming accelerate soil organic carbon (SOC) loss, thereby compromising essential functions of soil health, such as nutrient retention and microbial diversity. However, the mechanisms by which microbes adapt to arid and low SOC conditions remain poorly understood. Here, using data from an 8-y field-scale manipulation experiment, we found that the largely undescribed <em>Gemmatimonadetes</em> could be among the well-adapted bacterial taxa for thriving under low SOC content and arid ecosystems. Their enhanced ability to tolerate drought stress—mediated by metabolic pathways for the synthesis of osmolytes (e.g., glycine, betaine, choline, ectoine, and histidine)—and their capacity to acquire carbon resource through glycoside hydrolase genes involved in organic matter decomposition (41.6 % and 11.8 % higher than those in the total bacterial community, respectively), could explain this pattern. Further analyses based on a global-scale standardized field survey covering all continents and major ecosystem types further confirmed that <em>Gemmatimonadetes</em>—and, at a finer resolution, <em>Gemmatirosa</em>—predominated in arid (with a peak relative abundance of <em>Gemmatimonadetes</em> reaching 3.8 % in dry grasslands) and warm regions (peaking at 4.5 % in Africa) of the planet, where the SOC content is low. Our work provides new insights into how a largely neglected microbial group, such as <em>Gemmatimonadetes/Gemmatirosa</em>, can adapt to increasing environmental stress in arid and low-carbon environments in a changing world.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117420"},"PeriodicalIF":5.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting soil organic matter using corrected field spectra and stacking ensemble learning","authors":"Yu Wang ,&nbsp;Xuhui Yan ,&nbsp;Rongyanting Huo ,&nbsp;Longcai Zhao ,&nbsp;Jie Peng ,&nbsp;Yongsheng Hong ,&nbsp;Jing Liu","doi":"10.1016/j.geoderma.2025.117417","DOIUrl":"10.1016/j.geoderma.2025.117417","url":null,"abstract":"<div><div>Soil organic matter (SOM), as a key indicator of soil fertility and the carbon cycle, its field rapid and precise quantification is of great scientific significance for precise agricultural management. Visible near-infrared (Vis-NIR) spectroscopy technology is a rapid and highly accurate SOM quantification method. While the laboratory spectra measurement requires a series of processing procedures. Compared with laboratory spectra, field spectra measurement has the advantages of being faster and more convenient. However, achieving high-precision estimation of SOM based on field spectra poses significant challenges, primarily in mitigating the effects of interfering factors, such as soil moisture and developing a highly robust spectra prediction model. In the practical application of field spectroscopy, eliminating interference through spectra correction methods is an effective strategy. The field prediction of SOM using spectra correction algorithms in conjunction with ensemble learning remains a significant and unresolved challenge. In this study, we gathered 180 soil samples from Hancheng City, Shaanxi Province, China, and built Stacking models using field spectra, field corrected spectra, and lab spectra, respectively, and comprehensively compared their predictive abilities. The study aims to assess the ability of spectra correction methods, including non-negative matrix decomposition (NMF), and generalized least squares weight (GLSW), when combined with the Stacking model, to predict SOM. The results showed that it was difficult challenging to accurately predict SOM using models calibrated with field spectra. However, spectra data corrected by NMF and GLSW could effectively mitigate the influence of environmental interference factors and significantly enhance the model’s predictive performance. The GLSW (R² = 0.85, RMSE = 3.74 g kg⁻¹) outperformed the NMF method (R² = 0.69, RMSE = 5.14 g kg⁻¹) and was close to the laboratory spectra model (R² = 0.89, RMSE = 3.81 g kg⁻¹). Combining spectra correction and stacking improves field SOM prediction accuracy, increasing R² value by 0.1 and 0.26, and decreasing RMSE by 1.16 and 2.56 g kg⁻¹. The performance of all Stacking models was superior to that of the best single model. The stacking model could effectively improve the accuracy of SOM model.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117417"},"PeriodicalIF":5.6,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of soil thickness in karst landforms using a quantile regression forests approach","authors":"Fayong Fang ,&nbsp;Ruyi Zi ,&nbsp;Zhen Han ,&nbsp;Qian Fang ,&nbsp;Rui Hou ,&nbsp;Longshan Zhao","doi":"10.1016/j.geoderma.2025.117416","DOIUrl":"10.1016/j.geoderma.2025.117416","url":null,"abstract":"<div><div>Soil thickness is a basic feature of the earth’s land surface. Accurately representing the spatial distribution of soil thickness is important for various models of earth surface processes. However, mapping soil thickness in karst landforms is highly uncertain. To address this challenge, this study analyzed the correlation between 906 soil thickness measurements and 376 environmental characteristics in a typical karst landscape covering 54,000 km². We employed a quantile regression forests (QRF) approach to estimate soil thickness and evaluate the associated uncertainty in the predicted results. We found that, like other regional scale soil mapping models, climate and topographic data were key factors influencing soil thickness. Specific for karst landscapes, we found that the characteristics of karst rocky desertification play a key role in predicting soil thickness. The rocky desertification information indexes (RIs), which use exposure rate of bedrock to represent the degree of karst rocky desertification, showed relatively high importance in the variable importance assessment. The developed model explained 40 % of the spatial variability of soil thickness across the study area, with an RMSE (37.3 cm) of 50–60 % of the mean thickness. This indicates that the model, and environmental factors evaluated within, explained a little less than half of the spatial variability. The prediction results reveal the distribution pattern of soil thickness at both regional and local scales within karst landforms. Thick soil was commonly found in low-lying landscape features like depressions and foothills, whereas areas with steep slopes, ridges, and peaks tended to have thin soil, following a typical toposequence. In areas with relatively deep soil or severe rocky desertification, the uncertainty of predicting soil thickness is relatively high. The results of uncertainty analysis, as a supplement to the prediction results, have improved the usability of the predictions to a certain extent. This study has, to some extent, addressed the challenges of predicting soil thickness in karst areas and has also provided transferable methods for other complex regions.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117416"},"PeriodicalIF":5.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in water characteristics and pore distributions in loam soil under the coexistence of microplastics and salts","authors":"Xuguang Xing ,&nbsp;Liuchang Su ,&nbsp;Dongwei Li ,&nbsp;Fengyue Zhao ,&nbsp;Weihua Wang","doi":"10.1016/j.geoderma.2025.117415","DOIUrl":"10.1016/j.geoderma.2025.117415","url":null,"abstract":"<div><div>Farmland salinization and microplastics (MPs) pollution are increasing worldwide, posing threats to environmental health and accelerating land degradation. In the context of the global ecological challenges of land degradation and pollution, clarifying the interactions between MPs and salts is beneficial for land development and sustainable management. We measured the hydraulic parameters and calculated the water characteristics and pore distributions of the soils with different salinities (i.e., 0, 1, 3, and 5 g/kg) and MPs mixing contents (i.e., 0, 5, 10, and 30 g/kg). Results indicated that MPs generally reduced the soil saturated hydraulic conductivity, but the increased soil salinity weakened the effects of MPs on it. According to the soil water retention curves, MPs weakened the water-holding capacity, with a greater impact in non-saline soil than in saline soil. In non-saline soil, MPs caused the saturated water content to decrease by 4.6 %–8.1 %. In addition, MPs reduced the field capacity and wilting coefficient of both soils. The effect of MPs on available water was greater in non-saline soil than in saline soil. Furthermore, MPs reduced the total porosity of both soils. However, MPs had no significant effect on pore distribution in non-saline soil, whereas, in saline soil, MPs increased the proportion of micro pores and small pores but decreased the proportion of macro pores and voids. We determined that, in addition to changing the pore distribution, MPs and salts changed the water characteristics through hydrophobicity and ionic interactions, respectively. Our findings provide evidence of the influence of MPs on the physical properties of saline soil, highlighting the need for improved regulation and land management in plastic-polluted soil–crop systems.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117415"},"PeriodicalIF":5.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digitally mapping soil health at regional scale: disentangling drivers and predicting spatial land use effects","authors":"Valentina Rubio ,&nbsp;Joseph Amsili ,&nbsp;David G. Rossiter ,&nbsp;Andrew McDonald ,&nbsp;Harold van Es","doi":"10.1016/j.geoderma.2025.117401","DOIUrl":"10.1016/j.geoderma.2025.117401","url":null,"abstract":"<div><div>Soil health (SH) is inherently dynamic, and traditional soil surveys do not capture it. Its spatiotemporal variability presents challenges to understanding its drivers. We applied machine learning (ML) models and digital soil mapping (DSM) techniques to integrate SH observations with remotely-sensed data products for regions in New York State, representing soil forming factors, cropping systems, and management. Four biological (soil organic matter, permanganate-oxidizable C, protein, and respiration) and two physical (water aggregate stability and available water capacity) soil indicators as well as a composite SH index were evaluated to 1) quantify the relationships among climate, inherent soil properties, and land use to SH indicators; 2) develop data-driven models for predicting and mapping SH indicators at regional scale; and 3) use predicted SH maps to estimate impacts from hypothetical regional land use change scenarios. Model performance varied among SH indicators and region, with R² values ranging from 0.47 to 0.71 in the smaller domain and from 0.45 to 0.65 in the larger one. The models include conventional effects of inherent soil properties and climate on SH, but also prove the pivotal role of land use and cropping systems, explaining on average 63% of SH variation. Positive effects were associated with perennial forage crops, and adverse effects of intensive annual crop production. Also, biomass production and cycling strongly affect SH. Modeling SH responses to land use changes at a regional scale showed interactions between management and inherent properties that can affect SH benefits from alternative cropping systems. Overall, the geospatial application of ML models to mapping SH provides insights into its drivers that can support the design of informed policies and management interventions to improve SH.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117401"},"PeriodicalIF":5.6,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salinity and inundation drivers shift microbial necromass carbon distribution patterns in estuarine mangrove wetlands","authors":"Linke Zheng ,&nbsp;Manlin Su ,&nbsp;Xiaoting Zhang ,&nbsp;Lei Wang ,&nbsp;Hualong Hong ,&nbsp;Qian Zhang ,&nbsp;Lijuan Zhong ,&nbsp;Haoliang Lu","doi":"10.1016/j.geoderma.2025.117413","DOIUrl":"10.1016/j.geoderma.2025.117413","url":null,"abstract":"<div><div>Microbial necromass carbon (MNC) plays an important role in the long-term preservation of soil organic carbon (SOC) in coastal wetlands. However, the impact of increased salinity and inundation due to sea-level rise on MNC remains unclear. Here, we established a gradient experiment with three salinity levels (7.4 ‰, 15.6 ‰, 21.2 ‰) and four inundation periods (5 h/d, 7 h/d, 11 h/d, 13 h/d) across six mangrove sampling sites to investigate vertical distribution patterns of MNC and the environmental factors influencing its dynamics. Depth-resolved analyses revealed distinct MNC distribution patterns, and the topsoil (0–20 cm) exhibited considerably higher MNC concentrations (4.6–8.2 mg g⁻¹) than the subsoil (3.0–5.4 mg g⁻¹, 40–50 cm), whereas the proportional contribution of MNC to SOC showed opposite trends (topsoil: 22.2 %–28.1 %; subsoil: 24.3 %–36.5 %). This inverse relationship suggests differential preservation mechanisms across soil depths. Different salinity and inundation periods induced pronounced responses. Under high salinity condition (21.2 ‰), MNC concentrations decreased by 30.2 % relative to those under low salinity conditions (7.4 ‰), and MNC/SOC showed a 13.6 % reduction. Prolonged inundation (13 h/d) further worsened these effects, leading to a 28.6 % decline in MNC relative to intermittent inundation (5 h/d). In addition, fungal necromass carbon (FNC) is the main component of MNC in coastal estuary mangrove wetlands. Redundancy analyses revealed that SOC, total nitrogen (TN), soil water content (SWC) and clay had a substantial impact on MNC. Elevated salinity and inundation period were identified as the main factors hindering MNC accumulation in mangrove sediments. Our research demonstrates that MNC is a crucial component of the soil carbon pool in mangroves, contributing 27.1 % of SOC. However, high salinity and prolonged inundation severely disrupt this carbon sequestration process, thereby suppressing MNC production by nearly 30 %. Additionally, sea-level rise and saltwater intrusion lead to the decomposition of recalcitrant carbon components and the loss of existing carbon pools.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117413"},"PeriodicalIF":5.6,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-member analysis of the grain size of surface sediments used to synthesize Holocene aeolian activity across the Ordos Plateau in northern China","authors":"Bing Liu ,&nbsp;Heling Jin ,&nbsp;Hui Zhao ,&nbsp;Jianhui Jin ,&nbsp;Aijun Sun ,&nbsp;Xingfan Wang ,&nbsp;Xiaomei Zhang ,&nbsp;Jianhui Ge ,&nbsp;Yujie Xu ,&nbsp;Jianbao Liu","doi":"10.1016/j.geoderma.2025.117400","DOIUrl":"10.1016/j.geoderma.2025.117400","url":null,"abstract":"<div><div>The deserts of northern China comprise a major terrestrial ecosystem in the middle latitudes of the Northern Hemisphere, and aeolian activity and dust emissions of these deserts have affected the evolution of climate and ecosystems on both continental and global scales. However, previous reconstructions of Holocene aeolian activity within this region are controversial due to the erosional susceptibility of the dunefields on the Asian summer monsoonal boundary (ASMB), the diverse proxies used to reconstruct aeolian dynamics, and the spatial heterogeneity of the sedimentary environment across the various studied archives. Using the spatiotemporal substitution approach, we developed a new proxy of aeolian activity based on end-member analysis (EMA) of the grain size of surface samples from various types of aeolian deposit. We then applied the results to six aeolian sand-palaeosol sequences on the Ordos Plateau to reconstruct Holocene aeolian activity. We then produced a synthesis of regional aeolian activity based on records from sedimentary sequences, with their ages constrained by luminescence dating, and quantitatively estimated the contributions of various environmental factors. The results indicated that components EM2 + 3 are transported mainly via saltation and creep, and their abundances gradually increase with increases in the intensity of aeolian activity and decreasing vegetation cover. Our regional synthesis demonstrated that the strongest aeolian activity was in the Early Holocene, especially during 12–10 ka, whereas it was weakest during 7.5–3.5 ka and especially during 6–5 ka; however, strong aeolian activity was renewed at ∼ 2–1 ka. These findings agree with the integrated results from typical dunefields in the eastern-central parts of the ASMB. Quantitative analyses of the potential drivers indicated that vegetation cover was the principal control on Holocene aeolian activity. This finding emphasizes that protecting the natural vegetation cover should be the principal measures used to combat aeolian activity in this region.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117400"},"PeriodicalIF":5.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbial biomass – not diversity – drives soil carbon and nitrogen mineralization in Spanish holm oak ecosystems","authors":"Elisa Bruni ,&nbsp;Jorge Curiel Yuste ,&nbsp;Lorenzo Menichetti ,&nbsp;Omar Flores ,&nbsp;Daniela Guasconi ,&nbsp;Bertrand Guenet ,&nbsp;Ana-Maria Hereș ,&nbsp;Aleksi Lehtonen ,&nbsp;Raisa Mäkipää ,&nbsp;Marleen Pallandt ,&nbsp;Leticia Pérez-Izquierdo ,&nbsp;Etienne Richy ,&nbsp;Mathieu Santonja ,&nbsp;Boris Tupek ,&nbsp;Stefano Manzoni","doi":"10.1016/j.geoderma.2025.117408","DOIUrl":"10.1016/j.geoderma.2025.117408","url":null,"abstract":"<div><div>Soil microbial communities drive essential ecosystem functions, catalyzing biogeochemical cycles and contributing to climate regulation. However, due to the complexity of microbial communities, the magnitude and direction of microbial biomass and diversity contributions to carbon (C) and nutrient cycling remain unclear. For this reason, most models predicting soil organic matter (SOM) dynamics at the ecosystem level do not explicitly describe the role of microorganisms as mediators of SOM decomposition. Incorporating microbial properties, and especially diversity, into ecosystem models remains an open question, requiring careful consideration of the tradeoff between model complexity and performance.</div><div>This work addresses this knowledge gap by implementing a simple C and nitrogen (N) cycling model to predict heterotrophic respiration and net N mineralization rates in soils sampled under different land-uses and tree health conditions across Spain. To understand the role of microorganisms on ecosystem functioning, we progressively incorporated microbial biomass and diversity (i.e., alpha diversity of taxa and of fungal functional groups), and selected the model that optimized prediction accuracy, while minimizing complexity.</div><div>We found that microbial biomass had a strong and positive effect on both C and N mineralization rates, with heterotrophic respiration being nearly linearly controlled by biomass. In contrast, microbial diversity had minimal but negative effects on mineralization processes, with land-use differences explaining part of the variability in these effects. Our study confirms microbial biomass as a key driver of C and N mineralization rates, while highlights that microbial diversity based on taxonomic identification inadequately explains microbial effects on these ecosystem functions.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"460 ","pages":"Article 117408"},"PeriodicalIF":5.6,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}