Fungal response to drought in the maize rhizosphere after reusing cover crop root channels

Fungal response to drought in the maize rhizosphere after reusing cover crop root channels | 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 Fungal response to drought in the maize rhizosphere after reusing cover crop root channels Debjyoti Ghosh, Yijie Shi, Iris Zimmermann, Tobias Stürzebecher, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9052155/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 Root channels formed by winter cover crop can enhance subsoil water and nutrient access for subsequent crops such as maize ( Zea mays L.) yet their fungal inhabitants remain poorly understood under drought. Here, we assessed drought-induced shifts in maize rhizosphere fungal communities within reused cover crop root channels across three contrasting soil types in northern Germany (Luvisol, Podzol and Phaeozem). Using a multi-omics approach combining ITS2 amplicon sequencing, quantitative PCR and metaproteomics, we linked community composition with functional responses. Drought consistently restructured fungal communities, with increased relative abundances of Ascomycota and Zoopagomycota and declines in Chytridiomycota and Mucoromycota . Taxa within the same subkingdom occupied complementary niches, indicating functional differentiation beyond higher-level taxonomy. At the protein level, drought responses were characterised either by enhanced antioxidant defence mechanisms including catalase–glutathione peroxidase systems, superoxide dismutase, fatty acid synthesis and the methionine cycle–transsulfuration pathway or by reduced carbon and nitrogen metabolic activity, suggesting energy conservation strategies. Together, our results demonstrate substantial structural and functional plasticity of rhizosphere fungal communities in reused root channels under water limitation, highlighting their potential role in microbiome-mediated drought resilience in agroecosystems. Biological sciences/Ecology/Microbial ecology Biological sciences/Microbiology/Fungi/Fungal ecology Biological sciences/Microbiology/Environmental microbiology/Soil microbiology Biological sciences/Ecology/Climate-change ecology Earth and environmental sciences/Ecology/Microbial ecology Cover crop root channel re-use fungal community drought soil types metaproteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Drought is a major constraint on global crop productivity and disrupts biogeochemical processes in agricultural soils. Its frequency and severity have increased in recent decades 1 with an increase of 29% since 2000 2 driven by rising temperatures and long-term land use change 3 . As topsoil dries and nutrient mobility declines, root growth is redirected to deeper, less exposed subsoil layers. Enhancing the capacity of crops to exploit subsoil resources therefore represents a promising strategy to sustain productivity under water and nutrient limitation 4 . Root channels formed by preceding crops restructure soil microhabitats by modifying physical accessibility and microbial activity 5 – 7 . These stable macropores improve aeration, reduce mechanical resistance and facilitate deeper root penetration by subsequent crops. We previously showed that maize roots reusing winter cover crop root channels harbour higher bacterial abundance and activity in the rhizosphere 5 , contributing to mitigation of drought-like conditions 8 . In particular, bacterial communities exhibited upregulation of reactive oxygen species (ROS)-detoxifying enzymes, including catalase, glutathione peroxidase and superoxide dismutase, under water limitation 8 . Whether fungal communities in these biopores display comparable adaptive responses remains unknown. Fungi are key regulators of rhizosphere functioning, mediating soil structure, organic matter turnover, plant nutrient acquisition and stress tolerance properties 9 – 11 , organic matter decomposition 12 , and plant-fungi mutualistic relations. Their distribution is highly heterogenous and shaped by soil depths, physicochemical properties and management practices 13 , 14 . Soil type exerts strong control over fungal community composition. According to the World Reference Base for Soil Resources 15 , Luvisols and Phaeozems are characterised by high-activity clays and dark, loamy organic matter, respectively, with high base status, whereas Podzols are sandy and acidic 16 . Such differences in texture and chemistry influence the relative abundance of major fungal taxa and their vertical distribution along soil profiles 10 . Fungi are often considered comparatively drought-tolerant due to their filamentous growth form, which enables resource redistribution under low water availability 17 , 18 . However, their functional responses to drought in agricultural soils remain poorly resolved. The ecological roles of mycorrhizal symbionts, saprotrophs and pathotrophs under stress depend on their capacity to mitigate oxidative damage and maintain metabolic homeostasis 19 , 20 . Although oxidative stress responses have been described in plant-associated and clinical fungi, soil fungal proteomic responses particularly within reused cover crop root channels remain largely unexplored. This knowledge gap limits our understanding of how cover cropping strategies shape fungi-mediated soil processes under climate-induced stress. Here, we addressed this gap, through field experiments in which maize followed winter cover crop mixtures of Brassicaceae (Brassica), Fabaceae (Legume) and Poaceae (Grass), with drought imposed using rainout shelters. Across soils of contrasting texture, we combined ITS2 amplicon sequencing, quantitative PCR (qPCR), and metaproteomics to investigate the effects of drought (D) versus rainfall-fed (RF) maize cultivation on rhizosphere fungal communities of maize reusing pre-existing root channels. We hypothesised that fungal communities exhibit enhanced activity and distinct adaptive strategies under drought, modulated by soil type. By linking community composition with functional protein values across topsoil and subsoil, this study provides mechanistic insight to inform cover crop selection and strengthen crop resilience in water-limited agroecosystems. Results Fungal community structure in different soil types under drought stress The study encompassed three soil types: Luvisol, Phaeozem, and Podzol, and two soil depths (topsoil: 0–30 cm; subsoil: 30–60, and 60–90 cm) (Fig. 1 ). Soil physicochemical properties differed significantly among soil types and between depths (Fig. S1 , Table S1 ). We obtained 37,437 unique amplicon sequencing variants (ASVs) assigned to 83 classes within 12 fungal phyla (Fig. S2 ). Fungal richness increased under D relative to RF when Brassicaceae/Poaceae was the cover crop mixture in all the soil types (Luvisol: 832 D vs 729 RF , Phaeozem: 742 D vs 614 RF , Podzol: 804 D vs 696 RF ). Using Fabaceae/Poaceae , richness increased significantly in the Luvisol and Podzol but declined in the Phaeozem (Luvisol: 878 D vs 729 RF , Phaeozem: 511 D vs 579 RF , Podzol: 873 D vs 800 RF ). The richness under fallow conditions declined in the Phaeozem compared with the Luvisol and Podzol, although no clear differences were noticeable between D and RF (Luvisol: 831 D vs 815 RF , Phaeozem: 599 D vs 667 RF , Podzol: 818 D vs 827 RF ) (Fig. 2 a, Table S2 a). Evenness increased only for Fabaceae/Poaceae (0.774 Fabaceae/Poaceae > 0.766 Fallow > 0.764 Brassicaceae/Poaceae ), while no other changes were detected (Fig. 2 a, Table S2 b). Beta-diversity estimations using Bray-Curtis dissimilarity metrics revealed differences among Podzol fungal communities from those in the Luvisol and Phaeozem, as well as between the sampling depths (Fig. 2 b, Table S2 c), consistent with previously reported bacterial community behaviours in the same experimental system 8 . Quantitative PCR analysis demonstrated a 12.8-fold reduction in ITS2 gene copy numbers from the topsoil to the subsoil (1.60⋅10 7 Topsoil > 1.25⋅10 6 Subsoil ) (Fig. 2 c, Table 1 ). Absolute abundances were highest in the Phaeozem and declined sequentially in the Podzol and the Luvisol (1.44⋅10 7 Phaeozem > 0.75⋅10 7 Luvisol > 0.40⋅10 7 Podzol ). Among cover crop treatments, Fabaceae/Poaceae resulted in the highest ITS2 gene copies, exceeding Brassicaceae/Poaceae and fallow (9.52⋅10 6 Fabaceae/Poaceae > 8.97⋅10 6 Brassicaceae/Poaceae > 7.83⋅10 6 Fallow ). Under D, fungal abundance increased within Brassicaceae/Poaceae root channels in the Luvisol and Podzol, but declined with Fabaceae/Poaceae . In the subsoil, Fabaceae/Poaceae exhibited significantly lower ITS2 gene abundances than Brassicaceae/Poaceae and fallow (subsoil: 1.69⋅10 6 Brassicaceae/Poaceae ≈ 1.52 ⋅10 6 Fallow > 0.54⋅10 6 Fabaceae/Poaceae ), whereas no significant differences were observed among treatments in the topsoil (Table 1 ). Table 1 Summary of ITS2 gene copies per gram soil for different parameters in our study. We quantified the mean and SD values of ITS2 gene copies per gram soil in soils from the reused cover crop root channels for all study parameters. The significance of each category is represented using compact letter display (CLD) calculated after conducting MANOVA on the gene copies under categories of soil type, cover crop variations and soil sampling depths. The values are provided in the Supplemental Tables S4c-d. Table Variation Soil type Depth Condition ITS2 gene copies g soil − 1 (mean + SD) CLD Brassica/Grass Luvisol Topsoil Rainfall-fed 3.09E + 06 ± 2.33E + 05 bcd Drought 1.25E + 07 ± 1.49E + 06 Subsoil Rainfall-fed 7.46E + 06 ± 1.96E + 06 cd Drought 1.03E + 06 ± 1.21E + 05 Phaeozem Topsoil Rainfall-fed 3.68E + 07 ± 4.63E + 06 a Drought 3.17E + 07 ± 9.51E + 06 Subsoil Rainfall-fed 1.39E + 05 ± 1.58E + 04 d Drought 6.64E + 05 ± 7.72E + 04 Podzol Topsoil Rainfall-fed 5.92E + 05 ± 6.32E + 04 bcd Drought 1.29E + 07 ± 3.62E + 06 Subsoil Rainfall-fed 1.35E + 05 ± 1.82E + 04 d Drought 6.86E + 05 ± 9.41E + 04 Fallow Luvisol Topsoil Rainfall-fed 9.36E + 06 ± 1.94E + 06 abcd Drought 1.83E + 07 ± 1.39E + 06 Subsoil Rainfall-fed 3.27E + 06 ± 6.78E + 05 cd Drought 8.91E + 05 ± 9.49E + 04 Phaeozem Topsoil Rainfall-fed 3.51E + 07 ± 4.06E + 06 abc Drought 8.54E + 06 ± 1.52E + 06 Subsoil Rainfall-fed 8.87E + 04 ± 1.61E + 04 d Drought 2.35E + 05 ± 3.37E + 04 Podzol Topsoil Rainfall-fed 1.01E + 07 ± 3.05E + 06 bcd Drought 3.51E + 06 ± 5.36E + 05 Subsoil Rainfall-fed 2.40E + 06 ± 2.50E + 05 cd Drought 2.22E + 06 ± 4.68E + 05 Legume/Grass Luvisol Topsoil Rainfall-fed 2.68E + 07 ± 3.45E + 06 abcd Drought 4.94E + 06 ± 6.83E + 05 Subsoil Rainfall-fed 1.72E + 06 ± 1.87E + 05 cd Drought 8.41E + 05 ± 1.01E + 05 Phaeozem Topsoil Rainfall-fed 4.30E + 07 ± 1.40E + 07 ab Drought 1.65E + 07 ± 1.65E + 06 Subsoil Rainfall-fed 6.05E + 04 ± 5.25E + 03 cd Drought 2.80E + 05 ± 5.70E + 04 Podzol Topsoil Rainfall-fed 7.59E + 06 ± 2.01E + 06 bcd Drought 6.74E + 06 ± 1.51E + 06 Subsoil Rainfall-fed 1.90E + 05 ± 2.01E + 04 d Drought 1.69E + 05 ± 3.07E + 04 Although overall richness and evenness limited cover crop mixture effects, taxon-specific responses were evident at the phylum and class levels (Table S3 ). The Dikarya subkingdom, comprising Ascomycota and Basidiomycota , showed minimal change under D relative to RF ( Log2FC : 0.034 and 0.014, respectively) (Fig. 2 d, Tables S4a-b). However, within these phyla, individual classes responded divergently: Laboulbeniomycetes (-0.218) of Ascomycota and Malasseziomycetes (-0.531) of Basidiomycota decreased under D. Mucoromycota and Zoopagomycota exhibited contrasting outcomes, with the former taxa declining under D against RF (-0.136) while the latter increasing (0.130). Within Mucoromycota , Glomeromycetes (arbuscular mycorrhizal fungi; 0.399) and Mucoromycetes (0.152), one of the most well-studied zygomycete fungal classes, increased under D and were more abundant in the Luvisol and Phaeozem. Similarly, classes within Zoopagomycota were enriched in these two soils but declined upon reusing cover crop root channels (Fig. S3 ). Chytridiomycota (-0.061) and Cryptomycota (0.254), both typically associated with aquatic environments, displayed opposing abundance trends under D and in the drought-prone Podzol. Across soil depths, Dikarya exhibited vertical niche differentiation, with Ascomycota predominating in the topsoil and Basidiomycota in the subsoil. Exceptions included Saccharomycetes ( Ascomycota ) and the basidiomycete classes Atractiellomycetes and Cystobasidiomycetes (Fig. S4 ). Similar depth stratification was observed for Mucoromycota (enriched in subsoil) and Zoopagomycota (enriched in topsoil). The flagellated Chytridiomycota and the chitin cell-wall-lacking Cryptomycota preferentially inhabit the comparatively less compact topsoil. Despite these taxonomic shifts, overall phylum-level abundances showed limited responsiveness to cover crop treatments (Fig. S5 ). Correlation of fungal communities with soil physicochemical properties Soil physicochemical characteristics were examined previously, which showed that organic carbon content varied substantially with soil type and depth 8 (Fig. S1 , Table S1 ). Partial Mantel tests revealed significant associations between fungal community composition and soil physicochemical properties (Fig. 3 a, Tables S5a-d). In the clay-rich Phaeozem, Ascomycota abundance correlated positively with total nitrogen (TN) and total organic carbon (TOC) in Fabaceae/Poaceae under both D and RF conditions. Associations with pH depended on the soil, ranging from negative in the Phaeozem to positive in the topsoil, where Ascomycota also correlated positively with soil moisture. Basidiomycota association trends were exactly opposite to Ascomycota – negatively correlating with TN and TOC, and with moisture in the topsoil. Zoopagomycota correlated positively with moisture and pH in the topsoil, whereas negative associations were observed in the alkaline Phaeozem. The other zygosporangia-forming lineage, Mucoromycota , showcased the reverse pattern. Also, chitin-lacking Cryptomycota and the rare Aphelidiomycota were negatively correlated with both moisture and pH. To link taxonomic observations with ecological attributes, fungal communities were mapped to the FUNGuild database 21 to trophic modes and compare the changes in abundances under D versus RF (Fig. 3 b). Notable shifts along the trophic modes were visible in the subsoil as compared to the topsoil (Table S6 a). The most significant changes involved Mucoromycota , which includes arbuscular mycorrhizal fungi ( Glomeromycetes ), in the topsoil of all three soil types and in the subsoil of Luvisol (Table S6 b). For Ascomycota , changes were limited to the Podzol. With the effect of D, topsoil pathotrophs from Ascomycota (plant pathotrophic Eurotiomycetes and Sordariomycetes ), Basidiomycota (animal pathotrophic Malasseziomycetes ) and Mucoromycota (animal pathotrophic Mucoromycetes ) declined in the Podzol for Fabaceae/Poaceae and fallow but increased slightly for Brassicaceae/Poaceae . In contrast, subsoil pathotrophs increased with both cover crop treatments relative to bulk soil, particularly in the Podzol. Overall, saprotrophs and symbiotrophs generally increased following cover crop applications in the Phaeozem and Podzol. Notable exceptions were seen in the Luvisol, where Basidiomycota ( Agaricomycetes ) and Chytridiomycota ( Chytridiomycetes ) declined in Brassicaceae/Poaceae root channels, and in the Podzol topsoil, where for Basidiomycota and Mucoromycota ( Mucoromycetes ) symbiotrophs decreased significantly. The information about fungal trophic modes are provided in the supplemental tables S7a and S8a. Soil fungal metaproteome insights under drought To assess the effects of drought on fungal biochemical pathways within reused cover crop root channels, proteins were identified by metaproteomics and assigned to taxonomic groups. Expression of fungal proteins varied significantly with soil type, depth, and cover crop mixture under drought stress (Table S9 a). Overall trends in the number of protein groups were consistent with the ITS2 sequencing results, with some exceptions. Under D, protein groups increased in the topsoil of the Luvisol and Podzol for Brassicaceae/Poaceae compared with RF (416 Luvisol−D > 356 Luvisol−RF ; 504 Podzol−D > 362 Podzol−RF ), but increased only in the Podzol subsoil and not in the Luvisol (284 Luvisol−D 294 Podzol−RF ) (Table S9 b). For Fabaceae/Poaceae , protein groups increased in the Luvisol (429 Luvisol−D−Topsoil > 353 Luvisol−RF−Topsoil ; 341 Luvisol−D−Subsoil > 179 Luvisol−RF−Subsoil ) but showed no substantial change in Podzol topsoil, while increasing in Podzol subsoil (470 Podzol−D−Topsoil 226 Podzol−RF−Subsoil ). In contrast, no clear shifts were seen in the Phaeozem for either cover crop mixture or under fallow conditions (364 Fabaceae/Poaceae −D−Topsoil and 361 Fabaceae/Poaceae −RF−Topsoil ; 213 Fabaceae/Poaceae −D−Subsoil and 229 Fabaceae/Poaceae −RF−Subsoil ; (365 Brassicaceae/Poaceae −D−Topsoil and 369 Brassicaceae/Poaceae −RF−Topsoil ; 219 Brassicaceae/Poaceae −D−Subsoil and 211 Brassicaceae/Poaceae −RF−Subsoil ). Relative to RF, drought altered protein expression in the moisture-limited soils, Luvisol and Podzol, following the use of cover crop mixtures. In the drought-prone sandy Podzol, overall protein expression increased only with Brassicaceae/Poaceae ( Log2FC : 0.532), exceeding both fallow (0.017) and Fabaceae / Poaceae (-0.245) (Tables S9c-e). Similarly, protein expressions in the Luvisol increased only with Brassicaceae/Poaceae (0.328 Brassicaceae/Poaceae −Log2FC > 0.076 Fallow−Log2FC > -0.470 Fabaceae/Poaceae −Log2FC ). By contrast, the Phaeozem had the highest water retention under D and showed reduced protein expression following cover crop introduction relative to bulk soil (0.441 Fallow−Log2FC > 0.148 Brassicaceae/Poaceae −Log2FC > 0.044 Fabaceae/Poaceae −Log2FC ). Differential expression analysis revealed a greater number of upregulated proteins in the Luvisol and the Phaeozem (Fig. 4 , Table S10 ). Fatty-acid-synthesising 3-hydroxydecanoyl-[acyl-carrier-protein (ACP)] dehydratase, and oxidative stress regulators aldehyde dehydrogenase (NAD+) (ALDH) and superoxide dismutase (SOD) were upregulated under D (Table S7 a). In contrast, enzymes of the pyruvate dehydrogenase complex (PDC) enzymes contributing to acetyl-CoA synthesis were downregulated, particularly in Fabaceae/Poaceae root channels. In the Luvisol, osmoprotectant glycerol-3-phosphate dehydrogenase (G3PDH) and glutamate racemase (MurI) of the N cycle were upregulated with Brassicaceae/Poaceae under D. In the Podzol, membrane translocases and catalase peroxidase (CAT-PER) were upregulated, whereas phosphoribosylformylglycinamide synthase (PFAS) was downregulated. Methionine cycle enzymes, including homocysteine methyltransferase, methionine synthase (MeSe) and S-adenosylmethionine (SAM) synthase, were upregulated in Fabaceae/Poaceae root channels in the Luvisol. Consistent with amplicon sequencing, Ascomycota produced the largest proportion of the identified proteins. Based on absolute label-free quantification (LFQ) intensities of highly expressed enzymes, 3-hydroxydecanoyl-[ACP]-dehydratase was expressed exclusively by Ascomycota , particularly in the Podzol. The same patterns were observed for MeSe and homocysteine methyltransferase, whereas aspartokinase and homoserine dehydrogenase and the oxidative stress regulator ALDH were more strongly expressed in the Luvisol (Fig. 5 , S7, Table S7 a). Beyond Ascomycota , SAM synthase expression from Basidiomycota , Chytridiomycota , Mucoromycota and Zoopagomycota was relatively higher in the Podzol, while G3PDH was expressed more in the Luvisol. ALDH expressions were similar from Ascomycota in both Luvisol and Podzol and from Basidiomycota in the Phaeozem. Additional stress responders showed soil-specific patterns: SOD was relatively upregulated in Ascomycota in the Phaeozem, while CAT-PER from Basidiomycota was overexpressed in the Podzol inside Brassicaceae/Poaceae root channels. The pentose phosphate pathway enzyme 6-phosphogluconolactonase (6-PGL) was more strongly expressed by Ascomycota in the Podzol under D. Despite a relative abundance below 1%, Blastocladiomycota contributed substantially to glycolytic aldolase expression. The Podzol also represented a higher abundance of N cycle enzymes, glutamate dehydrogenase and urease, primarily contributed by Mucoromycota , Basidiomycota and Chytridiomycota . Glutamate racemase was expressed highly in the Luvisol under D. In the topsoil, Ascomycota , Basidiomycota and Mucoromycota were the top taxa contributing the most to the proteome (Table S7 b). On the other hand, in the subsoil, Mucoromycota exceeded those of Basidiomycota , while overall protein expressions from other phyla declined relative to topsoil. An extended heatmap of fungal protein expression across all the study parameters is provided in the Supplementary Material (Fig. S6 ). Fungi-mediated lignocellulose breakdown under drought Lignocellulose is degraded by lignocellulolytic enzymes, primarily hydrolases and some oxidoreductases, which are classified within different groups of carbohydrate-active enzymes (CAZymes). Fungal cellulases play a central role in the decomposition of lignocellulose and complex organic matter, thereby improving the availability of carbon, nutrients and water. Metaproteomics analysis revealed that glycoside hydrolases (GHs) were the most abundantly expressed CAZymes and were major contributors to lignocellulose decomposition. They were predominantly derived from the Ascomycota classes Eurotiomycetes and Sordariomycetes , while the other fungal taxa contributed sparsely (Fig. 6 a-b). Enzymes were further categorised according to the lignocellulosic substrates they target. Among the CAZymes, GHs were the most highly expressed, with β-glucosidases (GH1, GH3), xylanases (GH3, GH10, GH11), arabinofuranosidases (GH43, GH51), and α- and β-galactosidase (GH2, GH35) being particularly abundant. These enzymes were largely contributed by Ascomycota (Fig. 6 a-c, S7, Tables S8a-b). Auxiliary activity (AA) enzymes were the next most expressed group, notably pyranose:acceptor oxidoreductase (AA3) derived from Basidiomycota and especially abundant in the Podzol. Most identified CAZymes were extracellular, particularly those affiliated with Basidiomycota and Chytridiomycota (Fig. 6 a, Table S8 c). Although Ascomycota were the dominant producers of CAZymes, a proportion of their enzymes were predicted to be intracellular (e.g., AA4, GH2). Under D conditions, pathotrophic and symbiotrophic Ascomycota showed increased expression of GH12 and GH74 endoglucanases (Table S8 a). In contrast, GH6 cellobiohydrolases were overexpressed by saprotrophic Basidiomycota but displayed reduced expression in saprotrophic Ascomycota . The auxiliary enzyme lytic-polysaccharide monooxygenase (LPMO; AA9), produced by Ascomycota across all trophic modes, was relatively upregulated under D. Hemicellulose-degrading enzymes, including acetylxylan and feruloyl esterase (CE1), declined under D in saprotrophic Ascomycota , but were overexpressed by saprotrophic Basidiomycota . Oxidative stress conditions were associated with increased expression of intracellular vanillyl-alcohol oxidase (AA4), which is involved in vanillin and H 2 O 2 production. Contrastingly, enzymes involved in lignin and pectin degradation were not significantly affected by D relative to RF. Overall, contributions from Chytridiomycota and Mucoromycota were substantially lower than those from the Dikarya phyla. Discussion This study investigated the impact of drought on fungal communities and their biochemical pathways within reused winter cover crop root channels subsequently utilised by maize roots. Reuse of these root channels facilitates deeper maize root penetration by reducing mechanical resistance from the soil particles and improving access to subsoil nutrients. While previous studies demonstrated bacterial enrichment within the reused root channels 5 , 8 , our findings extend this understanding to fungi, revealing coordinated structural and functional responses shaped by cover crop identity, soil type and moisture availability. Fungal diversity and abundances varied among soil types, and were more strongly influenced by root channel reuse. In the drought-prone Luvisol and Podzol, richness and ITS2 gene copy numbers increased under drought, particularly within Brassicaceae/Poaceae root channels, whereas responses differed under fallow and Fabaceae / Poaceae treatments. Declines in Chytridiomycota and Mucoromycota under drought are consistent with previous bulk soil observations 22 , 23 . In contrast, enrichment of arbuscular mycorrhizal fungi ( Glomeromycetes and Mucoromycetes within Mucoromycota ) under drought likely reflects their stress-tolerant life history traits and ability to sustain nutrient exchange with host plants under water limitation 24 , 25 . Zoopagomycota increased under drought 26 , despite having phylogenetic similarity to Mucoromycota 27 , indicating niche differentiation among zygosporangia-forming lineages. The dominant Dikarya ( Ascomycota and Basidiomycota ) exhibit limited overall shifts, consistent with their ability to survive under resource limitations through stress-adaptive measures such as sporulation 28 , 29 . Associations between fungal phyla and soil physicochemical properties further support niche partitioning across soil profiles 30 . Fast-growing taxa such as Ascomycota , Chytridiomycota and Zoopagomycota preferentially exploit labile organic C and available N 31 , explaining their positive correlations with TOC and TN in the nutrient-rich Phaeozem and their predominance in the topsoil. Conversely, negative correlations for Basidiomycota and Mucoromycota with TOC and TN are consistent with adaptation to lower nutrient conditions in subsoils. Basidiomycota , known to establish symbiotic relationships with maize roots 32 , may benefit from enhanced root penetration into deeper subsoil via reused root channels. These patterns reflect pronounced vertical stratification, whereby topsoil-dominated phyla respond to nutrient availability, while subsoil-dominating communities exhibit adaptations linked to nutrient limitations and symbiotic relationships. Rare fungal phyla in this study, Aphelidiomycota and Cryptomycota , prefer low moisture and low pH conditions 33 , which explains the negative moisture and pH correlations. Drought-associated shifts in trophic modes, characterised by increased representation of saprotrophic and symbiotrophic fungi and reduced pathotrophs, likely reflect improved C sequestration besides plant defence responses and the release of plant root-derived metabolites 34 , 35 . To elucidate the functional dynamics underlying these structural changes within fungal communities in the reused root channels, we used metaproteomics. Upregulation of the fatty acid biosynthesis enzyme 3-hydroxydecanoyl-[ACP]-dehydratase advocates membrane restructuring and adjustments in energy storage under drought 17 , even in soils such as the Luvisol and Phaeozem that retain substantial moisture. Within Fabaceae / Poaceae root channels in the drought-affected Podzol, downregulation of Ascomycota and Mucoromycota -derived PDC enzymes reduces acetyl-CoA flux into the citric acid cycle, thereby conserving energy under stress 36 . Pyruvate may instead be redirected towards proline biosynthesis, an established osmoprotectant pathway 37 . Downregulation of the proline-degrading enzyme P5CDH in Ascomycota , Basidiomycota , Chytridiomycota , and Mucoromycota in Fabaceae / Poaceae root channels and fallow favour proline accumulation, enhancing osmotic protection and ROS scavenging 38 . In contrast, P5CDH upregulation in Brassicaceae / Poaceae root channels may reflect inherent stress tolerance or regulation of preventing proline-inflicted cell toxicity 39 . Oxidative stress regulation emerged as a prominent feature of fungal drought adaptation. Increased expression of superoxide dismutase (SOD), catalase-peroxidase (CAT-PER) and aldehyde dehydrogenase (ALDH) indicates coordinated detoxification of ROS and reactive aldehydes 40 . Treatment-specific expressions of these enzymes suggest that fungi within Brassicaceae / Poaceae root channels may possess greater oxidative resilience and may have potentially developed ‘stress memory’ that invokes counter-mediating approaches under drought 41 . Overexpression of glycolytic aldolase in Blastocladiomycota , Chytridiomycota and Zoopagomycota also contributes to cellular repairs by using root exudates as primary C sources for energy 42 . Higher expression of 6-PGL in the pentose phosphate pathway, particularly in the Podzol, implies enhanced NADPH generation essential for glutathione-dependent redox buffering 43 . Collectively, these findings point towards integrated regulation of energy metabolism and redox homeostasis under water limitation. Increased expression of N metabolism enzymes in the Podzol underscores metabolic plasticity in nutrient-poor sandy soils. Upregulation of glutamate dehydrogenase (GDH) and urease suggests alternative N assimilation routes 44 , while GDH-mediated deamination may provide C skeletons during limited carbohydrate availability 45 , 46 . Concurrent overexpression of methionine cycle and transsulfuration pathway enzymes, such as homocysteine methyltransferase, MeSe and SAM synthase across Dikarya , zygosporic Mucoromycota and Zoopagomycota , and zoosporic Chytridiomycota under drought, potentially catalyse SAM to synthesise abiotic stress-regulating polyamines 47 and glutathione, central mediators of abiotic stress tolerance 48 . Notably, these metabolic adjustments were most pronounced in the coarse-textured Podzol, whereas the silt-rich Phaeozem exhibited comparatively moderate functional restructuring, underscoring the role of soil physical context in mediating drought responses. Higher expression of CAZymes may have resulted because of the increased presence of Ascomycota ( Eurotiomycetes , Sordariomycetes ) 49 and Mucoromycota ( Mucoromycetes ) 50 , all being previously reported as major fungal CAZyme producers. (Fig. 2 c). The predominance of extracellular CAZymes indicates active mobilisation of complex organic matter and breaking down to simpler forms for emergency metabolic steps. Increased expression of cellulose-degrading enzymes β-glucosidase, endoglucanase, exoglucanase and LPMO by Ascomycota and Mucoromycota , enhance the breakdown of complex carbohydrates into soluble sugars 51 , potentially contributing to osmotic homeostasis and cellular stability during drought. These soluble sugars assist in the mobilisation of smaller molecules during water scarcity under drought. The prominence of auxiliary active oxidoreductases, particularly in the Podzol, further suggests active redox-coupled decomposition under drought. Elevated vanillyl-alcohol oxidase activity supports redox balancing through regulated H 2 O 2 production. Similarly, hemicellulolytic enzymes such as acetylxylan/feruloyl esterase, arabinofuranosidase and xylanase from Ascomycota and Basidiomycota likely contribute to cell wall remodelling under stress and metabolic flexibility under stress, requiring simpler sugars synthesised by degrading hemicelluloses 52 . Interpreting microbial dynamics in reused root channels is difficult because these environments are highly variable in space and time. Conditions such as soil moisture, nutrient availability, oxygen levels, and soil structure can change over short distances. These changes strongly influence how microbes grow, interact, and survive. As a result, microbial patterns in these channels are often complex and context-dependent. The open nature of root channels means they are continuously influenced by surrounding soil properties. This makes it challenging to clearly link environmental factors to specific microbial responses. In addition, results can differ depending on the taxonomic level examined. For example, we observed an increase in arbuscular mycorrhizal fungi (class Glomeromycetes ) under drought conditions, which agrees with the idea that plants rely more on fungal partners when water is limited. However, this pattern was not consistent at the broader phylum level (Mucoromycota). This shows that broad taxonomic groups may hide important ecological differences . Another limitation is the small number of studies focusing on soil fungal communities, especially within rhizosphere environments and structured microsites such as reused root channels. Because of this limited evidence, it is difficult to directly compare or fully confirm our findings. Further research is needed to better understand how fungal communities develop and respond to environmental stress belowground. Studies that combine detailed spatial sampling with functional analyses will help clarify the dynamic processes shaping fungal communities in soil. In conclusion, drought indicates coordinated structural and functional adaptations in fungal communities inhabiting the maize rhizosphere within reused cover crop root channels. These responses involve regulation of oxidative stress pathways to counter stress and reduction of C-N metabolic pathways to conserve energy, which varied with soil type and cover crop choices. Root channels formed by mixtures of Brassicaceae and Poaceae supported fungal communities that showed strong oxidative stress regulation and improved resource management. This pattern suggests a higher tolerance to drought. Therefore, such cover crop mixtures may represent suitable choices for future cropping strategies under similar environmental conditions. Overall, our findings provide mechanistic insight into how cover crop selection can shape fungal-mediated soil processes. They support the concept of strategic cover cropping as an approach to strengthen agroecosystem resilience under climate-induced oxidative stress. Methods Crop cultivation and sampling regimes Crops were grown in three agricultural fields – at experimental estates Hohenschulen of the Kiel University (Achterwehr, Germany, 54°18’44” N, 9°59’46” E), Karkendamm of the Kiel University (Bad Bramstedt, Germany, 53°55’52” N, 9°55’15” E), and Reinshof of the Georg-August-University of Göttingen (Rosdorf, Germany, 51°29’05” N, 9°53’34” E). The cash crop in this study was maize ( Zea mays L.). Plots without cover crops during the winter (bare fallow) were established as a control and compared against two cover crop mixtures on distinct plots. The cover crops were shallow- and deep-rooting Brassicaceae ( Brassica napus L., rapeseed, shallow-rooting; Raphanus sativus L. var. oleiformis , oilseed radish, deep-rooting); Fabaceae ( Trifolium repens L., white clover, shallow-rooting; Trifolium pratense L., red clover, deep-rooting); and Poaceae ( Lolium perenne , perennial ryegrass, shallow-rooting; Festuca arundinaceae , tall fescue, deep-rooting). The mixtures were grown as a combination of shallow- and deep-rooting cover crops of Brassicaceae , Fabaceae and Poaceae, complementing the niche complementarity principle, which has been reported to allow polycultures to overyield when plants compete for resources 53 . All the cover crops were sown in October 2022 and grew until May 2023 in distinct randomised plots with four replicates of each variation. In May 2023, a herbicide formulation (Roundup, Bayer AG, Leverkusen, Germany) was applied to all experimental plots (including fallow plots) to terminate all cover crops, and subsequently maize was sown in the same plots with the cover crop variations in addition to the fallow plots. Maize was grown in the fields from May to September 2023 (Fig. 1). To compare drought-like conditions against normal conditions, we artificially induced dry conditions using interrow rainout shelters and continued with the approach after observing differences in soil moisture trends between the sheltered and non-sheltered profiles using time-domain reflectometry sensors (Fig. S8a-c). These specific types of rainout shelters covered half of the plot area between the maize rows, reducing 50% rainfall and restricting water infiltration to stemflow. The shelters were installed between the crop rows and below the maize canopy in June, at approximately 50 cm aboveground. This position ensured that the structures did not interfere with leaf-level photosynthesis or direct light interception by the maize plants. They were constructed with a sloped configuration to promote air flow and reduce the risk of heat accumulation or stagnant humidity. The plastic film used was standard greenhouse-grade transparent polyethene, which allows high light transmission while providing effective rain exclusion. A depiction of our rainout shelter setup used in the experiment has been provided as a supplementary figure (Fig. S9). Before soil sampling, the soil profile was excavated to a depth of 1 m, then excavated 40 cm forward to obtain a fresh profile and fresh maize root system and to prevent any contamination by the neighbouring soil. To compare the difference inflicted by drought on microbial communities in the reused root channels of cover crops by maize, we collected soil samples from a vertical soil profile from the topsoil (0-30 cm) and the subsoil (30-60 cm and 60-90 cm) for two different conditions: 1) maize roots growing in the cover crop root biopores (MCR) from those profiles subjected to drought (D), and 2) maize roots growing in the MCR in the profiles under rainfall-fed conditions (RF). Maize rhizosphere soil collected along decayed cover crop roots was considered as originating from maize reusing cover crop root channels. The rhizosphere of maize and decayed cover crop roots was defined as extending 2 mm from the root surface. Therefore, the overlapping 2 mm rhizosphere zone of maize and decayed cover crop roots was designated as the sampling area. The sampling focused on clearly visible maize roots that were closely associated with remaining cover crop root residues, ensuring consistency in sample type across replicates and treatments to reduce subjective variation and avoid systematic error. Pictures of the reused root channels have been provided as a supplementary figure (Fig. S3). Additionally, maize rhizosphere samples from the control plot were collected, representing maize roots growing in bulk soil without reusing cover crop root channels. The sampling was done during the R1-RX growth stage of maize (bolting) grown in the Luvisol (01.08.2023), the Phaeozem (19.07.2023) and the Podzol (26.07.2023), and the profiles were maintained until sampling was done around the flowering and reproduction stage of maize. The samples were extracted from the profiles using a spatula and collected in plastic zip-lock bags. Until shipment to the laboratory, samples were stored in ice coolers containing dry ice in order to preserve microbial communities and the metabolic picture for distinct sampling time points. In the laboratory, all samples were stored at -80°C until further processing. No repetition of sampling was done from the profiles of the same plot in order to avoid bias and duplicates. To minimise potential operator bias when identifying “root overlap regions”, we used a randomised sampling scheme with the four blocks representing the four replicates and the ambient (natural rainfall) and drought (with interrow rainout shelters) plots always paired in direct proximity to each other. Soil physicochemical properties Soil microbial biomass carbon (C) and nitrogen (N) were determined using the chloroform fumigation extraction method 54 . In brief, 7.5 g of soil was fumigated with chloroform for 24 h and then extracted with 30 mL of 0.05 M K 2 SO 4 on a shaker for 1 h. C and N were measured with the N/C 2100 TOC/N analyser (Analytik Jena, Jena, Germany). MBC was calculated as the difference between extracted C from fumigated and non-fumigated soil with a conversion factor ( k C ) of 0.45 55 . MBN was calculated as the difference between extracted N from fumigated and non-fumigated soil with a conversion factor ( k N ) of 0.54 55,56 . The MBC and MBN were presented as µg g -1 dry soil. Soil density and moisture content were also measured alongside. Soil cylinder samples were taken to measure soil bulk density at each soil depth. Soil moisture content was also measured by water content sensors (Teros 10, Meter Group, München, Germany), which were installed in the control plots (in 0-30 cm, 30‒60 cm, and 60-90 cm depth under rainout shelter and on the rainfall-fed side) at each experimental site. To investigate the influence of soil physicochemical properties on the fungal microbiome in the maize root channels, we correlated the parameters pH, soil moisture content, TOC, TN, and soil bulk density with the ITS2 gene amplicon data using Partial Mantel’s test 57 using the R package microeco (v1.10) 58 . The soil physicochemical proteins were correlated to individual fungal phyla along the different soil types and soil moisture conditions to understand the impacts of these factors on the individual communities. ITS2 gene amplicon sequencing Fungal community composition in the root-vicinity samples was analysed by sequencing amplicons targeting the fungal internal transcribed spacer (ITS) regions of rRNA (2*300 bp) on an Illumina NextSeq™ 550 (Illumina, San Diego, CA, USA). DNA was extracted from 0.25 g of soil using the DNeasy ® PowerSoil ® Pro Kit (QIAGEN GmbH, Hilden, Germany). PCR amplicons of the ITS2 region of the fungal rRNA gene were prepared using the forward and reverse primers fITS7 and ITS4 and the NEBNext ® Ultra™ II Q5 ® Master Mix (New England Biolabs GmbH, Frankfurt, Germany) 59 . Sequencing libraries were prepared from 100 ng of DNA according to the Illumina protocol. Dual index adapters for the sequencing were attached using the NEBNext Multiplex Oligos for Illumina. The final concentration of the libraries was 2 nM after pooling. We sequenced triplicates of samples from each soil depth and root-vicinity source per cover crop variation plot for all three sampling sites (ITS2 gene amplicons, n =147). The sequencing data were analysed using QIIME2 v2023.5 60 . First, the raw sequence reads were demultiplexed and quality-filtered (q-score 25) using the q2‐demux plugin, followed by denoising with q2‐dada2. The ITS2 regions of the fungal rRNA were trimmed to 230 bp for both the forward and reverse sequences. All amplicon sequence variants (ASVs) were aligned with q2‐alignment, and then maximum-likelihood trees were constructed using q2‐phylogeny. We chose ASV-based methods over OTU approaches to limit the effect of spurious taxa on diversity indices 61 . Taxonomic assignment of fungal ASVs was carried out using the q2‐feature‐classifier and the classify-sklearn Naïve Bayes taxonomy classifier against the UNITE v10.0 database 62 for QIIME2 (released on 04.04.2024). ASVs with a relative abundance of <0.01% were defined as rare taxa. Quantitative PCR (qPCR) The copy number of the fungal ITS2 gene per gram of soil was quantified by SYBR ® Green-based qPCR using a 7500 Fast Real-Time PCR System (Applied Biosystems™, Thermo Fisher Scientific, Waltham, MA, USA). Aliquots of the same DNA extract utilised in amplicon sequencing were used for qPCR. Dilutions of template DNA were used to compensate for the effect of PCR inhibitors in the samples. Each sample was analysed in triplicate. A PCR amplicon of the ITS2 region derived from Trametes versicolor (DSM 11269) was used to generate the standard curve. Each 20 µL reaction contained 1 µL of template DNA, the forward and reverse primers fITS7 and ITS4 for the ITS2 gene 59 without adapter nucleotides and Luna ® Universal qPCR Master Mix (NEB). Reaction conditions were an initial denaturation for 1 min at 95°C, followed by 40 cycles of denaturation at 95°C for 15 s and extension at 60°C for 30 s. The melting curve was recorded in the temperature range of 60°C to 95°C. The gene copy numbers per gram of soil were determined in comparison against the standard essentially as before 63 . The average efficiency value was 100.8 ± 3.2%. Metaproteomics analysis At each timepoint, samples were collected separately from three plots for each cover crop variation at the analysed soil depths and root-vicinity sources and used for proteomic analyses following a previously described protocol 5 ( n =119). Approximately 4 g of soil was used for protein extraction using the SDS buffered-phenol extraction method as previously described 5 . The protein extract was purified using 1-D SDS-PAGE, and then the protein extract was further proteolytically cleaved using trypsin (Promega). A nano-HPLC system (UltiMate™ 3000 RSLCnano system, Thermo Fisher Scientific, Waltham, MA, USA) was used to separate the peptide lysates. The system was connected to a Q Exactive HF Orbitrap LC-MS/MS system (Thermo Fisher Scientific) equipped with a nano electrospray ion source, Triversa NanoMate ® (Advion, Ithaca, NY, USA). We searched the MS/MS data against an in-house generated proteome database containing all the defined proteomes in UniProt for the fungi identified by ITS2 gene amplicon sequencing. The database search was performed with Proteome Discoverer™ (v2.5.0.8, Thermo Fisher Scientific) using the SEQUEST-HT algorithm, and all of the outputs are available on PRIDE (EMBL-EBI) 64 . The precursor mass tolerance of the MS was set to 10 ppm, and the fragment mass tolerance of the MS/MS was 0.02 Da. Carbamidomethylation of cysteine was considered fixed, and oxidation of methionine was set as a dynamic modification. Enzyme specificity was set to trypsin with up to two missed cleavages allowed using 10 ppm peptide ions and 0.02 Da MS/MS tolerances. Only rank-one peptides with a Percolator-estimated false discovery rate (FDR) <1% were accepted as identified. The GhostKOALA and KEGG 65 and COG 66 databases were used for protein functional annotation. Pathways with a minimum of two proteins and a minimum coverage of 5% were selected for downstream processing. The carbohydrate-active enzymes (CAZymes) were identified using Unipept Desktop (v2.0.0, Ghent University) 67 . The identified CAZy enzymes and their preferred substrates provide information regarding rhizo-deposits in the soil profile along the cover crop root channels. To estimate the proportion of extracellular carbohydrate-active enzymes (CAZymes), signal peptides were predicted using Phobius 68 and proteins harbouring a signal peptide were classified as extracellular. Label-free quantification (LFQ) intensities of the identified proteins were analysed to characterise functional metabolic pathways and to assess their variation across sampling sites, soil moisture regimes, soil depths, and cover crop treatments. A custom reference database was constructed from the UniProtKB database by selecting protein sequences corresponding to the fungal phyla identified through ITS2 gene amplicons. During database assembly, redundancy was minimised while maintaining taxonomic and functional relevance to avoid repeated assignment of already quantified proteins. Only proteins unambiguously identified by unique peptides and successfully mapped to the UniProt reference database were retained for quantification. Taxonomic identities were retrieved using ENTREZ identifiers from the NCBI database 69,70 employing KEGG Orthology (KO) numbers as unique protein identifiers. Mapping identified proteins to NCBI entries enabled comprehensive taxonomic annotation of their source organisms. Non-fungal proteins were excluded from further analyses. Functional pathway annotation was performed using KEGG and COG identifiers, which were linked to individual proteins. The integration of KO-based taxonomic assignments with pathway annotations enabled the generation of combined datasets capturing both community composition and functional potential. LFQ intensities were normalised by log₂ transformation using the log function of base R (v4.3.1) 71 prior to visualisation and statistical tests. Following pathway categorisation, proteins were mapped onto biogeochemical cycles of interest to evaluate shifts associated with soil properties, moisture status, and the re-use of cover crop root biopores, thereby elucidating microbial functional responses. To assess the impact of environmental stress following root channel re-use, fold changes in LFQ intensities under drought conditions were calculated relative to rainfall-fed controls. Proteins were considered differentially abundant when |log₂ fold change| >0.6 and p <0.05 72 . In addition, to quantify the effects of cover crop root channel re-use, differential abundances were determined by subtracting protein intensities measured under cover crop treatments ( Brassicaceae/Poaceae and Fabaceae/Poaceae rotations) from those observed under fallow conditions. Statistical data analysis We used R (v4.3.1) 71 to perform all statistical analyses of the sequencing and the metaproteomics data. All measures of significance were calculated using permutational multivariate analysis of variance (PERMANOVA), followed by Tukey’s range post-hoc test (TukeyHSD) with package stats (v3.6.2) 73 . In the ITS2 gene sequencing analysis, the ASV abundance tables were filtered with total-frequency-based filtering based on 95% sequence identity (via q2-feature-table summarize) and rarefied at 30,000 sequences to ensure equal sampling depth and sorting in the maximum number of samples for diversity analyses. Alpha and beta diversity metrics were calculated using the packages phyloseq 74 and metacoder from R (v4.3.1) 71 . Observed ASV richness measured for each cover crop variation at different sampling sites and conditions was used for estimating alpha diversity richness. Pielou’s evenness is the most widely used diversity evenness index in the ecological literature 75 . For beta diversity, we used Bray-Curtis dissimilarity 76 and visualised differences via Principal Coordinate Analysis (PCoA) using the vegan package (v2.6-4) 77 . Using a four-way permutational multivariate analysis of variance (PERMANOVA), we evaluated the significantly different cover crop variations using the sampling sites, sampling conditions and sampling depths as random effects and cover crop variations as the fixed effect. This was followed by Tukey’s HSD for evaluating MANOVA test outcomes with parameters of cover crop variations, depth, sampling sites, soil moisture conditions and fungal phyla. For fungal abundances under different parameters, the significance between the parameters was represented using the Compact Letter Display (CLD) 78 since we can represent multiple pairwise significances using linear models. For metaproteomics, the significantly different cover crop variations or proteins of different metabolic pathways or fungal phyla were calculated using MANOVA, using cover crop variations, sampling depths, soil moisture conditions, sampling sites and fungal phyla as fixed factors. Upon determination, they were represented by significant stars based on the adjusted p -values (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001). All figures were generated in RStudio. Other integrated packages used for statistical analyses and figure generation were tidyverse (v2.0.0), and dplyr (v1.1.3). Declarations Data availability The metaproteomics datasets generated during the current study are available in the PRIDE data repository with the sample metadata, vide PRIDE dataset identifier PXD062138 (https://www.ebi.ac.uk/pride). The raw sequencing data and the respective metadata generated from this study are available under the NCBI BioProject ID PRJNA1240274, which can be accessed using the link: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1240274. The raw qPCR data, along with the sample metadata, are available on Zenodo under the DOI: https://doi.org/10.5281/zenodo.18889777. Acknowledgments D.G. would like to take this opportunity to thank Helmholtz Centre for Environmental Research – UFZ GmbH, especially the UFZ-funded ProMetheus platform for metaproteomics and support. We acknowledge Kathleen Eismann for her help in sample preparation for metaproteomic assessments; Madlen Schubert for Trametes versicolor DSM 11269 strain; Habibu Aliyu, Florian Lenk and David Thiele for their assistance during Illumina NextSeq™ sequencing; Stephan Schreiber for advice during NextSeq™ sample preparations; Katja Holzhauser and Tobias Stürzebecher for assistance in fieldwork; Iris M. Zimmermann and Sandra Spielvogel with the project administration and management; and Matthias Bernt for his advises during data analysis and as the administrator of the Galaxy UFZ computational workbench. Funding NJ has received funding from the project 2020-RootWayS-BMBF under the section of Rhizo4Bio (FKZ: 031B0911B, Phase 1), sanctioned by the Federal Ministry of Education and Research (BMBF), Germany. N.J., J.A.M., M.v.B. and A.-K.K. were supported by the Helmholtz Association of German Research Centers through its research program “PoF IV”. The amplicon sequencing and metaproteomics data were computed on Galaxy UFZ and the High-Performance Computing (HPC) Cluster EVE, a joint effort of Helmholtz Centre for Environmental Research – UFZ GmbH and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. Author information Authors and Affiliations Department of Molecular Toxicology, Helmholtz Centre for Environmental Research – UFZ GmbH, Leipzig, Germany Debjyoti Ghosh, Nico Jehmlich, Martin von Bergen Institute of Biological Interfaces, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany Jochen A. Müller, Anne-Kristin Kaster Department of Bio- and Environmental Sciences, International Institute Zittau, Dresden Institute of Technology, Zittau, Germany Harald Kellner Institute of Plant Nutrition and Soil Science, Kiel University, Kiel, Germany Yijie Shi, Iris M. Zimmermann, Sandra Spielvogel Institute of Crop Science and Plant Breeding, Kiel University, Kiel, Germany Katja Holzhauser Biogeochemistry of Agroecosystems, University of Göttingen, Göttingen, Germany Tobias Stürzebecher Geo-Biosphere Interactions, University of Tübingen, Tübingen, Germany Michaela A. Dippold Institute for Biochemistry, University of Leipzig, Leipzig, Germany Martin von Bergen German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany Martin von Bergen Author contributions N.J., J.A.M., M.A.D., and S.S. conceived and designed the study; D.G., Y.S., I.M.Z., T.S. and K.H. organised and coordinated fieldwork; D.G. performed the amplicon sequencing and metaproteomics experiments; Y.S. performed the soil physicochemical characterisations; D.G. analysed and interpreted all experimental observations; D.G., N.J., H.K and J.A.M wrote the paper with inputs from all authors; all authors read and approved the final manuscript. Corresponding authors Correspondence to Nico Jehmlich ( [email protected] ) and Debjyoti Ghosh ( [email protected] ). Ethical declarations Competing interests The authors declare that they have no known competing interests that could have influenced the work being reported in this manuscript. 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Nucleic Acids Res 43:D261–D269 Verschaffelt P, Den Bossche T, Martens L, Dawyndt P, Mesuere B (2021) Unipept Desktop: A Faster, More Powerful Metaproteomics Results Analysis Tool. J Proteome Res 20:2005–2009 Kall L, Krogh A, Sonnhammer EL (2007) L. Advantages of combined transmembrane topology and signal peptide prediction–the Phobius web server. Nucleic Acids Res 35:W429–W432 Sayers EW et al (2019) GenBank. Nucleic Acids Res 47:D94–D99 Schoch CL et al (2020) NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database baaa062 (2020) R-Core-Team (2023) R: A Language and Environment for Statistical Computing. https://www.r-project.org/ McCarthy DJ, Smyth GK (2009) Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics 25:765–771 Tukey JW (1949) Comparing Individual Means in the Analysis of Variance Author (s): John W. Tukey Published by: International Biometric Society Stable URL. Int Biom Soc 5:99–114. http://www.jstor.org/stable/3001913 McMurdie PJ, Holmes S (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 8 Daly AJ, Baetens JM, De Baets B (2018) Ecological Diversity: Measuring the Unmeasurable. Mathematics 6:119 Bray JR, Curtis JT (1957) An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecol Monogr 27:325–349 Oksanen J et al (2022) vegan: Community Ecology Package. https://github.com/vegandevs/vegan Piepho H-P (2004) An Algorithm for a Letter-Based Representation of All-Pairwise Comparisons. J Comput Graph Stat 13:456–466 Additional Declarations There is NO Competing Interest. Supplementary Files FigureS2.pdf Supplemental Figure S2 SupplementalTableS3.xlsx Supplemental Table S3 FigureS61.pdf Supplemental Figure S6 SupplementalTableS14.xlsx Supplemental Table S14 SupplementalTablesS2ah.xlsx Supplemental Tables S2a-h SupplementalTablesS4ae.xlsx Supplemental Tables S4a-e SupplementalTablesS5ad.xlsx Supplemental Tables S5a-d SupplementalTableS1.xlsx Supplemental Table S1 SupplementalTablesS7ab.xlsx Supplemental Tables S7a-b SupplementarytextfiguresS1S101.docx Supplemental Text and Figures S1, S3-S5, S7-S10 SupplementalTablesS8ab.xlsx Supplemental Tables S8a-b SupplementalTableS10.xlsx Supplemental Table S10 SupplementalTablesS11ac.xlsx Supplemental Tables S11a-c SupplementalTablesS6ab.xlsx Supplemental Tables S6a-b SupplementalTablesS9ae.xlsx Supplemental Tables S9a-e SupplementalTableS13.xlsx Supplemental Table S13 SupplementalTablesS12ac.xlsx Supplemental Tables S12a-c GA.png Graphical abstract 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. 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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-9052155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602774506,"identity":"d980fa44-7b95-40ed-bb8f-57ddb1ff0d33","order_by":0,"name":"Debjyoti Ghosh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIie3PMUsDMRTA8RcO0uVds+Y49TOkBgqFUr/KhYKTuLh0DAiZ4u7gV3B3M6XQLrGuBx3k9iKOHnQwLS1uaUfB/Dk4CPnduweQSv3FHIIIL9w9RA+howH4CUQcyDWgO4GE9oOInh0n3cWDu8PJ5gzY87RpX96VZY7UXxFS+GUl0Ycf459jmfuVsuCywWOEiPpGyNxsie+XxKzUK9G0xBj5WP+SojVLZTOg5SY6BfeE2T7PjVOWBhJbv/C3Ve/JS6QcZbBjaZHcD2yEdBdvc7GeXFwx5ntNa0bnls2m9XdsDAC93C5LeXU4IDoOALJm903mjl1MpVKp/9oPdvFK+87M9/0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-1496-1677","institution":"Helmholtz Centre for Environmental Research - 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UFZ","correspondingAuthor":false,"prefix":"","firstName":"Nico","middleName":"","lastName":"Jehmlich","suffix":""}],"badges":[],"createdAt":"2026-03-06 15:37:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9052155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9052155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104378580,"identity":"26f0db17-0c29-4cd3-9490-d62294be72c5","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8832219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe experimental design for studying reuse of winter cover crop root channels to cultivate maize.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003e Field sites: samples from Luvisol, Podzol, and Phaeozem in Germany with comparison of fungal microbiome associated with roots of maize (\u003cem\u003eZea mays\u003c/em\u003e L.) in root channels of cover crop [Legume/Grass (\u003cem\u003eFabaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e), Brassica/Grass (\u003cem\u003eBrassicaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e)] or fallow (no cover crops) as control conditions. To replicate drought conditions, plots were covered with rainout shelters. \u003cstrong\u003eb.\u003c/strong\u003e Community profiling using ITS2 gene-based amplicon sequencing, qPCR, and metaproteomics were used to visualise how the composition and functional role of fungal communities in the maize rhizosphere changed after being exposed to drought.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/1095b20ca8263504458ff9c2.png"},{"id":104378593,"identity":"2229f790-3d1e-4cff-b462-4c607e23ee0c","added_by":"auto","created_at":"2026-03-11 07:05:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1478549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe taxonomic diversity and absolute abundances at the different experimental sites under soil moisture conditions.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003eAlpha diversity richness (Observed ASVs) and evenness (Pielou’s evenness index) metrices reflecting the distribution of fungal communities at the three sampling sites under drought (D) and rainfall-fed (RF) conditions. Pairwise correlation between the variations is shown using a compact letter display representation for the significant differences between the variations, calculated using Tukey’s range test (TukeyHSD); \u003cstrong\u003eb.\u003c/strong\u003e Fungal community beta-diversity visualised using PCoA ordination based on Bray-Curtis dissimilarities along the sampling sites and depths for each cover crop variation under soil moisture conditions. Distance metrics were calculated from the identified amplicon sequence variants (ASVs). The different colours of the density plots next to the axes represent the soil types and sampling depths, and peak height and shape abundance; \u003cstrong\u003ec.\u003c/strong\u003e ITS2 gene copies measured using qPCR as surrogate for absolute fungal abundances in the soil types, of each cover crop variation (Fallow, Legume/Grass and Brassica/Grass) and soil moisture conditions (drought (D) and rainfall-fed (RF)); \u003cstrong\u003ed.\u003c/strong\u003e Taxonomic heat trees represent the differential abundance of the fungal phylotypes between two parameters of soil moisture content [drought (D) vs rainfall-fed (RF)]. The yellow-ochre colour indicates increases under D conditions and green colour under RF conditions. For Luvisol, \u003cem\u003en\u003c/em\u003e = 35; for Phaeozem, \u003cem\u003en\u003c/em\u003e = 36;and for Podzol, \u003cem\u003en\u003c/em\u003e = 35; For soil moisture conditions (drought and rainfall-fed) and depth (topsoil and subsoil), \u003cem\u003en\u003c/em\u003e = 106 (statistical details in Supplemental Tables S2c-h).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/0692f36249e7ceaa4b5a2980.png"},{"id":104378578,"identity":"3ca1e823-6695-4287-81b5-bde9b566e5c6","added_by":"auto","created_at":"2026-03-11 07:05:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1163420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of individual fungal communities and the soil physicochemical properties across the parameters of study and the comparative abundance of fungal communities based on trophic modes. a.\u003c/strong\u003e The correlations between each fungal phylum with the physicochemical properties (TN, TOC, pH, Moisture and Density) have been estimated for each cover crop variation, soil moisture conditions, soil types and sampling depths using Partial Mantel’s test. Pearson’s correlation values and the significance estimation for each physicochemical protein and each phylum are provided in the Supplemental Tables S5a-d; \u003cstrong\u003eb.\u003c/strong\u003e The comparative abundance of the fungal communities classified by trophic modes, as defined in the FUNGuild database. This was estimated by \u003cem\u003elog2-FC\u003c/em\u003evalues of ITS2 gene copies per gram soil for drought versus rainfall-fed conditions. The yellow-ochre colour indicates increases under D conditions and green colour under RF conditions. The significant correlations are represented by asterisks inside the heatmap tiles; *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/1ea3fde380edd46d9a08c242.png"},{"id":104378607,"identity":"48bc8987-652e-4d2a-acd8-3d9c311df74c","added_by":"auto","created_at":"2026-03-11 07:05:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":649680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpregulated and downregulated fungal proteins under drought. \u003c/strong\u003eA volcano plot representation for the upregulation and downregulation of fungal proteins differing significantly under drought conditions at the three sampling sites (Luvisol, Phaeozem and Podzol) for the cover crop variations (Fallow, Legume/Grass and Brassica/Grass). The \u003cem\u003ex\u003c/em\u003e-axis represents the \u003cem\u003elog2-FC\u003c/em\u003e and the \u003cem\u003ey\u003c/em\u003e-axis represents the -Log\u003csub\u003e10\u003c/sub\u003e p-value according to Mann-Whitney U test. The proteins having \u003cem\u003elog2\u003c/em\u003e fold-change (FC) values \u0026lt; 0.6 or \u0026gt; -0.6 and p \u0026gt; 0.05 were classified as ‘Not significant’. \u003cem\u003elog2-FC\u003c/em\u003e \u0026gt; 0.6 and p \u0026lt; 0.05 were the ‘Upregulated’ proteins and \u003cem\u003elog2-FC\u003c/em\u003e \u0026lt; 0.6 and p \u0026lt; 0.05 were the proteins ‘Downregulated’. Names of several identified proteins are not shown to avoid crowdedness. The information for the all the represented significantly upregulated or downregulated proteins have been provided in the Supplemental Table S10.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/b46c9840749eb6edaad0f01b.png"},{"id":104378609,"identity":"b7879689-d792-43cf-be59-baf127051263","added_by":"auto","created_at":"2026-03-11 07:05:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1218737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the functional pathways affected by drought in three soils after cover crop root-channel-reuse across the soil sampling depths.\u003c/strong\u003e The heatmap is showing the fold change of expressions based on measured protein LFQ intensities for each protein identified under drought and rainfall-fed conditions. Positive logarithmic values of the relative change are denoted by red (higher in drought than in rainfall-fed conditions) and negative values by blue (lower in drought than in rainfall-fed conditions). The small circles next to the names of the biochemical cycles have unique colours, which also have been used to denote the cycles in the circular packing plot, denoting the fungal protein distribution across the soil sampling depths and the contribution from the fungal communities. The significance of the changes in protein expression under drought conditions at each site was calculated using a four-way ANOVA model, significant changes in expression are marked with an asterisk (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Supplemental Table S7a consists of a list of proteins which represented significant changes across the parameters of study); At the end, a schematic representation of the dynamics of the functional pathways observed in this study has been portrayed.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/8388fe29edfd44b584d18ca3.png"},{"id":104378585,"identity":"d8ed7deb-5f2b-4aef-8b25-e67fe3645382","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2548306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe behaviour of fungal CAZymes under drought stress.\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003e The heatmap represents a combination of several heatmaps expressing in the following order: the total number of peptides mapped to fungal CAZymes that act on lignocelluloses or are involved in supportive activities related to lignocellulose degradation; positive fold-change values of the number of peptides from CAZymes under drought against rainfall-fed are denoted by red (higher in drought than in rainfall-fed conditions) and negative values by blue (lower in drought than in rainfall-fed conditions). The CAZymes are linked to the fungal phyla, mentioned at the \u003cem\u003ex\u003c/em\u003e-axis and at the top of the heatmap contains information about fungal trophic modes (pathotroph, saprotroph and symbiotroph); the total number of signal peptides mapped back to the CAZymes using Phobius; the quantification of the fraction of the number of extracellular proteins, detected by the presence of signal peptides. Further details about the fungal classes linked to these enzymes are represented in the Sankey diagram \u003cstrong\u003e(b)\u003c/strong\u003e, which we link to the soil types featuring the communities; \u003cstrong\u003ec. \u003c/strong\u003eA\u003cstrong\u003e \u003c/strong\u003etreemap representation of the CAZy database-classified categories that had the highest representation in the heatmap. The three colours are for representing the three categories of CAZymes identified – enzymes with auxiliary activity (AA) in red, carbohydrate esterase (CE) in blue and glycoside hydrolase (GH) in green. A summary of identified CAZymes is provided in Tables S8a-b and the entire list of signal peptides predicted by Phobius are provided in Tables S11a-c.\u003c/p\u003e","description":"","filename":"Figure61.png","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/92bbda10535cf881e5549912.png"},{"id":104782969,"identity":"6980ec8b-6b2c-482c-92c5-57044c78adb2","added_by":"auto","created_at":"2026-03-17 07:58:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19419856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/2f59ca58-6383-4a1d-bdf4-05a772c282ec.pdf"},{"id":104406151,"identity":"72e6f809-42b5-44bb-9c06-aa9ff6911ab7","added_by":"auto","created_at":"2026-03-11 12:24:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31846,"visible":true,"origin":"","legend":"Supplemental Figure S2","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/70cd6de66241878a321e4a97.pdf"},{"id":104378612,"identity":"de9fd275-5f91-4263-86ad-f21a33c396fb","added_by":"auto","created_at":"2026-03-11 07:05:50","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19676,"visible":true,"origin":"","legend":"Supplemental Table S3","description":"","filename":"SupplementalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/cebf05468864a71f366dfc1f.xlsx"},{"id":104378581,"identity":"5943b316-3e8e-45bb-a8e6-b75ad109dbaf","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":53880,"visible":true,"origin":"","legend":"Supplemental Figure S6","description":"","filename":"FigureS61.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/4319b75aa3aa87fed5b0385c.pdf"},{"id":104378579,"identity":"531274ee-00f5-41a0-a77c-c8f6a8b4a08a","added_by":"auto","created_at":"2026-03-11 07:05:43","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14415,"visible":true,"origin":"","legend":"Supplemental Table S14","description":"","filename":"SupplementalTableS14.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/cd09dd667498e371f61b6982.xlsx"},{"id":104378597,"identity":"200232a9-b90e-40b4-ae9a-5850e0df7958","added_by":"auto","created_at":"2026-03-11 07:05:45","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":19783,"visible":true,"origin":"","legend":"Supplemental Tables S2a-h","description":"","filename":"SupplementalTablesS2ah.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/e3907cd91ab136dc1ceb11ae.xlsx"},{"id":104378587,"identity":"0b0387c8-0eff-4ed1-8245-d6e8882eac69","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":108218,"visible":true,"origin":"","legend":"Supplemental Tables S4a-e","description":"","filename":"SupplementalTablesS4ae.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/7fea06ba3ad7c62c6d55eaaf.xlsx"},{"id":104378595,"identity":"d54d7e24-e5c9-428b-9088-58fca4936fa1","added_by":"auto","created_at":"2026-03-11 07:05:45","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":35396,"visible":true,"origin":"","legend":"Supplemental Tables S5a-d","description":"","filename":"SupplementalTablesS5ad.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/4efd462e00bf379b577f8457.xlsx"},{"id":104406338,"identity":"387f2745-5e2a-4cdd-a5b5-ee85df9feb50","added_by":"auto","created_at":"2026-03-11 12:25:21","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10169,"visible":true,"origin":"","legend":"Supplemental Table S1","description":"","filename":"SupplementalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/17168638e0ec79fbf0eb116f.xlsx"},{"id":104378583,"identity":"d32f637f-65a8-455e-ab2f-237c95db7182","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1298283,"visible":true,"origin":"","legend":"Supplemental Tables S7a-b","description":"","filename":"SupplementalTablesS7ab.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/f04b395786942eb9a55ae72b.xlsx"},{"id":104378589,"identity":"eaee3ef9-3faf-411f-931c-5263e73a3603","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":4643300,"visible":true,"origin":"","legend":"Supplemental Text and Figures S1, S3-S5, S7-S10","description":"","filename":"SupplementarytextfiguresS1S101.docx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/84aa1d0240b25fd72e7501f4.docx"},{"id":104378590,"identity":"006e7abc-7ab3-47bb-b7f5-9fb8b1eba4bd","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":109759,"visible":true,"origin":"","legend":"Supplemental Tables S8a-b","description":"","filename":"SupplementalTablesS8ab.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/6463fe13d2a4d14de3ba416c.xlsx"},{"id":104406039,"identity":"4eb5f1f8-2f81-41ff-b22e-710c640999af","added_by":"auto","created_at":"2026-03-11 12:24:39","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":128006,"visible":true,"origin":"","legend":"Supplemental Table S10","description":"","filename":"SupplementalTableS10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/a6b830ead21d2bb05f26be79.xlsx"},{"id":104378591,"identity":"0d846cb0-e797-4edb-8a9f-712ad1c35218","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":522232,"visible":true,"origin":"","legend":"Supplemental Tables S11a-c","description":"","filename":"SupplementalTablesS11ac.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/f8ccd700d649520f28a66ed9.xlsx"},{"id":104378592,"identity":"049b6ac6-f8c9-4635-9a7e-d4cfb4d47bd0","added_by":"auto","created_at":"2026-03-11 07:05:45","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":20923,"visible":true,"origin":"","legend":"Supplemental Tables S6a-b","description":"","filename":"SupplementalTablesS6ab.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/07656d5a95992856b7aa32ea.xlsx"},{"id":104378582,"identity":"f80214ee-9e9a-4122-b1f6-a05e6f214dee","added_by":"auto","created_at":"2026-03-11 07:05:44","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":18579,"visible":true,"origin":"","legend":"Supplemental Tables S9a-e","description":"","filename":"SupplementalTablesS9ae.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/2498e32d25a1713313a5f6af.xlsx"},{"id":104406270,"identity":"2a24060e-0334-4408-8a5f-28600aa31fe2","added_by":"auto","created_at":"2026-03-11 12:25:11","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":21876,"visible":true,"origin":"","legend":"Supplemental Table S13","description":"","filename":"SupplementalTableS13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/980b00bded95bf60a8a5faf7.xlsx"},{"id":104406356,"identity":"57f13c7d-018e-41b0-8e50-48d0cde7ad56","added_by":"auto","created_at":"2026-03-11 12:25:28","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":37196,"visible":true,"origin":"","legend":"Supplemental Tables S12a-c","description":"","filename":"SupplementalTablesS12ac.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/06f6a416440b43e1685cd74e.xlsx"},{"id":104378598,"identity":"f0b58807-3939-4c8e-89fc-8508e08fd124","added_by":"auto","created_at":"2026-03-11 07:05:45","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":370416,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-9052155/v1/4bc6d580797960c8266828c7.png"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Fungal response to drought in the maize rhizosphere after reusing cover crop root channels","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDrought is a major constraint on global crop productivity and disrupts biogeochemical processes in agricultural soils. Its frequency and severity have increased in recent decades\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e with an increase of 29% since 2000\u003csup\u003e2\u003c/sup\u003e driven by rising temperatures and long-term land use change\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As topsoil dries and nutrient mobility declines, root growth is redirected to deeper, less exposed subsoil layers. Enhancing the capacity of crops to exploit subsoil resources therefore represents a promising strategy to sustain productivity under water and nutrient limitation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Root channels formed by preceding crops restructure soil microhabitats by modifying physical accessibility and microbial activity\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These stable macropores improve aeration, reduce mechanical resistance and facilitate deeper root penetration by subsequent crops.\u003c/p\u003e \u003cp\u003eWe previously showed that maize roots reusing winter cover crop root channels harbour higher bacterial abundance and activity in the rhizosphere\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, contributing to mitigation of drought-like conditions\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In particular, bacterial communities exhibited upregulation of reactive oxygen species (ROS)-detoxifying enzymes, including catalase, glutathione peroxidase and superoxide dismutase, under water limitation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Whether fungal communities in these biopores display comparable adaptive responses remains unknown.\u003c/p\u003e \u003cp\u003eFungi are key regulators of rhizosphere functioning, mediating soil structure, organic matter turnover, plant nutrient acquisition and stress tolerance properties\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, organic matter decomposition\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and plant-fungi mutualistic relations. Their distribution is highly heterogenous and shaped by soil depths, physicochemical properties and management practices\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Soil type exerts strong control over fungal community composition. According to the World Reference Base for Soil Resources\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, Luvisols and Phaeozems are characterised by high-activity clays and dark, loamy organic matter, respectively, with high base status, whereas Podzols are sandy and acidic\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Such differences in texture and chemistry influence the relative abundance of major fungal taxa and their vertical distribution along soil profiles\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFungi are often considered comparatively drought-tolerant due to their filamentous growth form, which enables resource redistribution under low water availability\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, their functional responses to drought in agricultural soils remain poorly resolved. The ecological roles of mycorrhizal symbionts, saprotrophs and pathotrophs under stress depend on their capacity to mitigate oxidative damage and maintain metabolic homeostasis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Although oxidative stress responses have been described in plant-associated and clinical fungi, soil fungal proteomic responses particularly within reused cover crop root channels remain largely unexplored. This knowledge gap limits our understanding of how cover cropping strategies shape fungi-mediated soil processes under climate-induced stress.\u003c/p\u003e \u003cp\u003eHere, we addressed this gap, through field experiments in which maize followed winter cover crop mixtures of \u003cem\u003eBrassicaceae\u003c/em\u003e (Brassica), \u003cem\u003eFabaceae\u003c/em\u003e (Legume) and \u003cem\u003ePoaceae\u003c/em\u003e (Grass), with drought imposed using rainout shelters. Across soils of contrasting texture, we combined ITS2 amplicon sequencing, quantitative PCR (qPCR), and metaproteomics to investigate the effects of drought (D) versus rainfall-fed (RF) maize cultivation on rhizosphere fungal communities of maize reusing pre-existing root channels. We hypothesised that fungal communities exhibit enhanced activity and distinct adaptive strategies under drought, modulated by soil type. By linking community composition with functional protein values across topsoil and subsoil, this study provides mechanistic insight to inform cover crop selection and strengthen crop resilience in water-limited agroecosystems.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFungal community structure in different soil types under drought stress\u003c/p\u003e \u003cp\u003eThe study encompassed three soil types: Luvisol, Phaeozem, and Podzol, and two soil depths (topsoil: 0\u0026ndash;30 cm; subsoil: 30\u0026ndash;60, and 60\u0026ndash;90 cm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Soil physicochemical properties differed significantly among soil types and between depths (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe obtained 37,437 unique amplicon sequencing variants (ASVs) assigned to 83 classes within 12 fungal phyla (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Fungal richness increased under D relative to RF when \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e was the cover crop mixture in all the soil types (Luvisol: 832\u003csub\u003eD\u003c/sub\u003e vs 729\u003csub\u003eRF\u003c/sub\u003e, Phaeozem: 742\u003csub\u003eD\u003c/sub\u003e vs 614\u003csub\u003eRF\u003c/sub\u003e, Podzol: 804\u003csub\u003eD\u003c/sub\u003e vs 696\u003csub\u003eRF\u003c/sub\u003e). Using \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e, richness increased significantly in the Luvisol and Podzol but declined in the Phaeozem (Luvisol: 878\u003csub\u003eD\u003c/sub\u003e vs 729\u003csub\u003eRF\u003c/sub\u003e, Phaeozem: 511\u003csub\u003eD\u003c/sub\u003e vs 579\u003csub\u003eRF\u003c/sub\u003e, Podzol: 873\u003csub\u003eD\u003c/sub\u003e vs 800\u003csub\u003eRF\u003c/sub\u003e). The richness under fallow conditions declined in the Phaeozem compared with the Luvisol and Podzol, although no clear differences were noticeable between D and RF (Luvisol: 831\u003csub\u003eD\u003c/sub\u003e vs 815\u003csub\u003eRF\u003c/sub\u003e, Phaeozem: 599\u003csub\u003eD\u003c/sub\u003e vs 667\u003csub\u003eRF\u003c/sub\u003e, Podzol: 818\u003csub\u003eD\u003c/sub\u003e vs 827\u003csub\u003eRF\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea). Evenness increased only for \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e (0.774\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.766\u003csub\u003eFallow\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.764\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u003c/sub\u003e), while no other changes were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). Beta-diversity estimations using Bray-Curtis dissimilarity metrics revealed differences among Podzol fungal communities from those in the Luvisol and Phaeozem, as well as between the sampling depths (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec), consistent with previously reported bacterial community behaviours in the same experimental system\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuantitative PCR analysis demonstrated a 12.8-fold reduction in ITS2 gene copy numbers from the topsoil to the subsoil (1.60\u0026sdot;10\u003csup\u003e7\u003c/sup\u003e\u003csub\u003eTopsoil\u003c/sub\u003e \u0026gt; 1.25\u0026sdot;10\u003csup\u003e6\u003c/sup\u003e\u003csub\u003eSubsoil\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Absolute abundances were highest in the Phaeozem and declined sequentially in the Podzol and the Luvisol (1.44\u0026sdot;10\u003csup\u003e7\u003c/sup\u003e\u003csub\u003ePhaeozem\u003c/sub\u003e \u0026gt; 0.75\u0026sdot;10\u003csup\u003e7\u003c/sup\u003e\u003csub\u003eLuvisol\u003c/sub\u003e \u0026gt; 0.40\u0026sdot;10\u003csup\u003e7\u003c/sup\u003e\u003csub\u003ePodzol\u003c/sub\u003e). Among cover crop treatments, \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e resulted in the highest ITS2 gene copies, exceeding \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e and fallow (9.52\u0026sdot;10\u003csup\u003e6\u003c/sup\u003e\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u003c/sub\u003e \u0026gt; 8.97\u0026sdot;10\u003csup\u003e6\u003c/sup\u003e\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u003c/sub\u003e \u0026gt; 7.83\u0026sdot;10\u003csup\u003e6\u003c/sup\u003e\u003csub\u003eFallow\u003c/sub\u003e). Under D, fungal abundance increased within \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e root channels in the Luvisol and Podzol, but declined with \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e. In the subsoil, \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e exhibited significantly lower ITS2 gene abundances than \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e and fallow (subsoil: 1.69\u0026sdot;10\u003csup\u003e6\u003c/sup\u003e\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u003c/sub\u003e \u0026asymp; 1.52 \u0026sdot;10\u003csup\u003e6\u003c/sup\u003e\u003csub\u003eFallow\u003c/sub\u003e \u0026gt; 0.54\u0026sdot;10\u003csup\u003e6\u003c/sup\u003e\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u003c/sub\u003e), whereas no significant differences were observed among treatments in the topsoil (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of ITS2 gene copies per gram soil for different parameters in our study.\u003c/b\u003e We quantified the mean and SD values of ITS2 gene copies per gram soil in soils from the reused cover crop root channels for all study parameters. The significance of each category is represented using compact letter display (CLD) calculated after conducting MANOVA on the gene copies under categories of soil type, cover crop variations and soil sampling depths. The values are provided in the Supplemental Tables S4c-d. Table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eITS2 gene copies g soil\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mean\u0026thinsp;+\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCLD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eBrassica/Grass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLuvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.09E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 2.33E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ebcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 1.49E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.46E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 1.96E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 1.21E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePhaeozem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.68E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 4.63E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.17E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 9.51E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 1.58E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.64E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 7.72E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePodzol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.92E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 6.32E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ebcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 3.62E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 1.82E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.86E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 9.41E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eFallow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLuvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.36E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 1.94E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eabcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.83E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 1.39E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.27E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 6.78E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.91E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 9.49E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePhaeozem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.51E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 4.06E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eabc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.54E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 1.52E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.87E\u0026thinsp;+\u0026thinsp;04 \u0026plusmn; 1.61E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 3.37E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePodzol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 3.05E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ebcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.51E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 5.36E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 2.50E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.22E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 4.68E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eLegume/Grass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLuvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.68E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 3.45E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eabcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.94E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 6.83E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.72E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 1.87E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.41E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 1.01E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePhaeozem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.30E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 1.40E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65E\u0026thinsp;+\u0026thinsp;07 \u0026plusmn; 1.65E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.05E\u0026thinsp;+\u0026thinsp;04 \u0026plusmn; 5.25E\u0026thinsp;+\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.80E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 5.70E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePodzol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.59E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 2.01E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ebcd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.74E\u0026thinsp;+\u0026thinsp;06 \u0026plusmn; 1.51E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubsoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.90E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 2.01E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69E\u0026thinsp;+\u0026thinsp;05 \u0026plusmn; 3.07E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough overall richness and evenness limited cover crop mixture effects, taxon-specific responses were evident at the phylum and class levels (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The \u003cem\u003eDikarya\u003c/em\u003e subkingdom, comprising \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e, showed minimal change under D relative to RF (\u003cem\u003eLog2FC\u003c/em\u003e: 0.034 and 0.014, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Tables S4a-b). However, within these phyla, individual classes responded divergently: \u003cem\u003eLaboulbeniomycetes\u003c/em\u003e (-0.218) of \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eMalasseziomycetes\u003c/em\u003e (-0.531) of \u003cem\u003eBasidiomycota\u003c/em\u003e decreased under D. \u003cem\u003eMucoromycota\u003c/em\u003e and \u003cem\u003eZoopagomycota\u003c/em\u003e exhibited contrasting outcomes, with the former taxa declining under D against RF (-0.136) while the latter increasing (0.130). Within \u003cem\u003eMucoromycota\u003c/em\u003e, \u003cem\u003eGlomeromycetes\u003c/em\u003e (arbuscular mycorrhizal fungi; 0.399) and \u003cem\u003eMucoromycetes\u003c/em\u003e (0.152), one of the most well-studied zygomycete fungal classes, increased under D and were more abundant in the Luvisol and Phaeozem. Similarly, classes within \u003cem\u003eZoopagomycota\u003c/em\u003e were enriched in these two soils but declined upon reusing cover crop root channels (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). \u003cem\u003eChytridiomycota\u003c/em\u003e (-0.061) and \u003cem\u003eCryptomycota\u003c/em\u003e (0.254), both typically associated with aquatic environments, displayed opposing abundance trends under D and in the drought-prone Podzol.\u003c/p\u003e \u003cp\u003eAcross soil depths, \u003cem\u003eDikarya\u003c/em\u003e exhibited vertical niche differentiation, with \u003cem\u003eAscomycota\u003c/em\u003e predominating in the topsoil and \u003cem\u003eBasidiomycota\u003c/em\u003e in the subsoil. Exceptions included \u003cem\u003eSaccharomycetes\u003c/em\u003e (\u003cem\u003eAscomycota\u003c/em\u003e) and the basidiomycete classes \u003cem\u003eAtractiellomycetes\u003c/em\u003e and \u003cem\u003eCystobasidiomycetes\u003c/em\u003e (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Similar depth stratification was observed for \u003cem\u003eMucoromycota\u003c/em\u003e (enriched in subsoil) and \u003cem\u003eZoopagomycota\u003c/em\u003e (enriched in topsoil). The flagellated \u003cem\u003eChytridiomycota\u003c/em\u003e and the chitin cell-wall-lacking \u003cem\u003eCryptomycota\u003c/em\u003e preferentially inhabit the comparatively less compact topsoil. Despite these taxonomic shifts, overall phylum-level abundances showed limited responsiveness to cover crop treatments (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelation of fungal communities with soil physicochemical properties\u003c/p\u003e \u003cp\u003eSoil physicochemical characteristics were examined previously, which showed that organic carbon content varied substantially with soil type and depth\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Partial Mantel tests revealed significant associations between fungal community composition and soil physicochemical properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Tables S5a-d). In the clay-rich Phaeozem, \u003cem\u003eAscomycota\u003c/em\u003e abundance correlated positively with total nitrogen (TN) and total organic carbon (TOC) in \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e under both D and RF conditions. Associations with pH depended on the soil, ranging from negative in the Phaeozem to positive in the topsoil, where \u003cem\u003eAscomycota\u003c/em\u003e also correlated positively with soil moisture. \u003cem\u003eBasidiomycota\u003c/em\u003e association trends were exactly opposite to \u003cem\u003eAscomycota \u0026ndash;\u003c/em\u003e negatively correlating with TN and TOC, and with moisture in the topsoil. \u003cem\u003eZoopagomycota\u003c/em\u003e correlated positively with moisture and pH in the topsoil, whereas negative associations were observed in the alkaline Phaeozem. The other zygosporangia-forming lineage, \u003cem\u003eMucoromycota\u003c/em\u003e, showcased the reverse pattern. Also, chitin-lacking \u003cem\u003eCryptomycota\u003c/em\u003e and the rare \u003cem\u003eAphelidiomycota\u003c/em\u003e were negatively correlated with both moisture and pH.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo link taxonomic observations with ecological attributes, fungal communities were mapped to the FUNGuild database\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e to trophic modes and compare the changes in abundances under D versus RF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Notable shifts along the trophic modes were visible in the subsoil as compared to the topsoil (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003ea). The most significant changes involved \u003cem\u003eMucoromycota\u003c/em\u003e, which includes arbuscular mycorrhizal fungi (\u003cem\u003eGlomeromycetes\u003c/em\u003e), in the topsoil of all three soil types and in the subsoil of Luvisol (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eb). For \u003cem\u003eAscomycota\u003c/em\u003e, changes were limited to the Podzol. With the effect of D, topsoil pathotrophs from \u003cem\u003eAscomycota\u003c/em\u003e (plant pathotrophic \u003cem\u003eEurotiomycetes\u003c/em\u003e and \u003cem\u003eSordariomycetes\u003c/em\u003e), \u003cem\u003eBasidiomycota\u003c/em\u003e (animal pathotrophic \u003cem\u003eMalasseziomycetes\u003c/em\u003e) and \u003cem\u003eMucoromycota\u003c/em\u003e (animal pathotrophic \u003cem\u003eMucoromycetes\u003c/em\u003e) declined in the Podzol for \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e and fallow but increased slightly for \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e. In contrast, subsoil pathotrophs increased with both cover crop treatments relative to bulk soil, particularly in the Podzol. Overall, saprotrophs and symbiotrophs generally increased following cover crop applications in the Phaeozem and Podzol. Notable exceptions were seen in the Luvisol, where \u003cem\u003eBasidiomycota\u003c/em\u003e (\u003cem\u003eAgaricomycetes\u003c/em\u003e) and \u003cem\u003eChytridiomycota\u003c/em\u003e (\u003cem\u003eChytridiomycetes\u003c/em\u003e) declined in \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e root channels, and in the Podzol topsoil, where for \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e (\u003cem\u003eMucoromycetes\u003c/em\u003e) symbiotrophs decreased significantly. The information about fungal trophic modes are provided in the supplemental tables S7a and S8a.\u003c/p\u003e \u003cp\u003eSoil fungal metaproteome insights under drought\u003c/p\u003e \u003cp\u003eTo assess the effects of drought on fungal biochemical pathways within reused cover crop root channels, proteins were identified by metaproteomics and assigned to taxonomic groups. Expression of fungal proteins varied significantly with soil type, depth, and cover crop mixture under drought stress (Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003ea). Overall trends in the number of protein groups were consistent with the ITS2 sequencing results, with some exceptions. Under D, protein groups increased in the topsoil of the Luvisol and Podzol for \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e compared with RF (416\u003csub\u003eLuvisol\u0026minus;D\u003c/sub\u003e \u0026gt; 356\u003csub\u003eLuvisol\u0026minus;RF\u003c/sub\u003e; 504\u003csub\u003ePodzol\u0026minus;D\u003c/sub\u003e \u0026gt; 362\u003csub\u003ePodzol\u0026minus;RF\u003c/sub\u003e), but increased only in the Podzol subsoil and not in the Luvisol (284\u003csub\u003eLuvisol\u0026minus;D\u003c/sub\u003e \u0026lt; 337\u003csub\u003eLuvisol\u0026minus;RF\u003c/sub\u003e; 336\u003csub\u003ePodzol\u0026minus;D\u003c/sub\u003e \u0026gt; 294\u003csub\u003ePodzol\u0026minus;RF\u003c/sub\u003e) (Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eb). For \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e, protein groups increased in the Luvisol (429\u003csub\u003eLuvisol\u0026minus;D\u0026minus;Topsoil\u003c/sub\u003e \u0026gt; 353\u003csub\u003eLuvisol\u0026minus;RF\u0026minus;Topsoil\u003c/sub\u003e; 341\u003csub\u003eLuvisol\u0026minus;D\u0026minus;Subsoil\u003c/sub\u003e \u0026gt; 179\u003csub\u003eLuvisol\u0026minus;RF\u0026minus;Subsoil\u003c/sub\u003e) but showed no substantial change in Podzol topsoil, while increasing in Podzol subsoil (470\u003csub\u003ePodzol\u0026minus;D\u0026minus;Topsoil\u003c/sub\u003e \u0026lt; 488\u003csub\u003ePodzol\u0026minus;RF\u0026minus;Topsoil\u003c/sub\u003e; 288\u003csub\u003ePodzol\u0026minus;D\u0026minus;Subsoil\u003c/sub\u003e \u0026gt; 226\u003csub\u003ePodzol\u0026minus;RF\u0026minus;Subsoil\u003c/sub\u003e). In contrast, no clear shifts were seen in the Phaeozem for either cover crop mixture or under fallow conditions (364\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u0026minus;D\u0026minus;Topsoil\u003c/sub\u003e and 361\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u0026minus;RF\u0026minus;Topsoil\u003c/sub\u003e; 213\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u0026minus;D\u0026minus;Subsoil\u003c/sub\u003e and 229\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u0026minus;RF\u0026minus;Subsoil\u003c/sub\u003e; (365\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u0026minus;D\u0026minus;Topsoil\u003c/sub\u003e and 369\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u0026minus;RF\u0026minus;Topsoil\u003c/sub\u003e; 219\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u0026minus;D\u0026minus;Subsoil\u003c/sub\u003e and 211\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u0026minus;RF\u0026minus;Subsoil\u003c/sub\u003e). Relative to RF, drought altered protein expression in the moisture-limited soils, Luvisol and Podzol, following the use of cover crop mixtures. In the drought-prone sandy Podzol, overall protein expression increased only with \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e (\u003cem\u003eLog2FC\u003c/em\u003e: 0.532), exceeding both fallow (0.017) and \u003cem\u003eFabaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e (-0.245) (Tables S9c-e). Similarly, protein expressions in the Luvisol increased only with \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e (0.328\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u0026minus;Log2FC\u003c/sub\u003e \u0026gt; 0.076\u003csub\u003eFallow\u0026minus;Log2FC\u003c/sub\u003e \u0026gt; -0.470\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u0026minus;Log2FC\u003c/sub\u003e). By contrast, the Phaeozem had the highest water retention under D and showed reduced protein expression following cover crop introduction relative to bulk soil (0.441\u003csub\u003eFallow\u0026minus;Log2FC\u003c/sub\u003e \u0026gt; 0.148\u003csub\u003e\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e\u0026minus;Log2FC\u003c/sub\u003e \u0026gt; 0.044\u003csub\u003e\u003cem\u003eFabaceae/Poaceae\u003c/em\u003e\u0026minus;Log2FC\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eDifferential expression analysis revealed a greater number of upregulated proteins in the Luvisol and the Phaeozem (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e). Fatty-acid-synthesising 3-hydroxydecanoyl-[acyl-carrier-protein (ACP)] dehydratase, and oxidative stress regulators aldehyde dehydrogenase (NAD+) (ALDH) and superoxide dismutase (SOD) were upregulated under D (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea). In contrast, enzymes of the pyruvate dehydrogenase complex (PDC) enzymes contributing to acetyl-CoA synthesis were downregulated, particularly in \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e root channels. In the Luvisol, osmoprotectant glycerol-3-phosphate dehydrogenase (G3PDH) and glutamate racemase (MurI) of the N cycle were upregulated with \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e under D. In the Podzol, membrane translocases and catalase peroxidase (CAT-PER) were upregulated, whereas phosphoribosylformylglycinamide synthase (PFAS) was downregulated. Methionine cycle enzymes, including homocysteine methyltransferase, methionine synthase (MeSe) and S-adenosylmethionine (SAM) synthase, were upregulated in \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e root channels in the Luvisol.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsistent with amplicon sequencing, \u003cem\u003eAscomycota\u003c/em\u003e produced the largest proportion of the identified proteins. Based on absolute label-free quantification (LFQ) intensities of highly expressed enzymes, 3-hydroxydecanoyl-[ACP]-dehydratase was expressed exclusively by \u003cem\u003eAscomycota\u003c/em\u003e, particularly in the Podzol. The same patterns were observed for MeSe and homocysteine methyltransferase, whereas aspartokinase and homoserine dehydrogenase and the oxidative stress regulator ALDH were more strongly expressed in the Luvisol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S7, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea). Beyond \u003cem\u003eAscomycota\u003c/em\u003e, SAM synthase expression from \u003cem\u003eBasidiomycota\u003c/em\u003e, \u003cem\u003eChytridiomycota\u003c/em\u003e, \u003cem\u003eMucoromycota\u003c/em\u003e and \u003cem\u003eZoopagomycota\u003c/em\u003e was relatively higher in the Podzol, while G3PDH was expressed more in the Luvisol. ALDH expressions were similar from \u003cem\u003eAscomycota\u003c/em\u003e in both Luvisol and Podzol and from \u003cem\u003eBasidiomycota\u003c/em\u003e in the Phaeozem. Additional stress responders showed soil-specific patterns: SOD was relatively upregulated in \u003cem\u003eAscomycota\u003c/em\u003e in the Phaeozem, while CAT-PER from \u003cem\u003eBasidiomycota\u003c/em\u003e was overexpressed in the Podzol inside \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e root channels. The pentose phosphate pathway enzyme 6-phosphogluconolactonase (6-PGL) was more strongly expressed by \u003cem\u003eAscomycota\u003c/em\u003e in the Podzol under D. Despite a relative abundance below 1%, \u003cem\u003eBlastocladiomycota\u003c/em\u003e contributed substantially to glycolytic aldolase expression. The Podzol also represented a higher abundance of N cycle enzymes, glutamate dehydrogenase and urease, primarily contributed by \u003cem\u003eMucoromycota\u003c/em\u003e, \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eChytridiomycota\u003c/em\u003e. Glutamate racemase was expressed highly in the Luvisol under D. In the topsoil, \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e were the top taxa contributing the most to the proteome (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eb). On the other hand, in the subsoil, \u003cem\u003eMucoromycota\u003c/em\u003e exceeded those of \u003cem\u003eBasidiomycota\u003c/em\u003e, while overall protein expressions from other phyla declined relative to topsoil. An extended heatmap of fungal protein expression across all the study parameters is provided in the Supplementary Material (Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFungi-mediated lignocellulose breakdown under drought\u003c/p\u003e \u003cp\u003eLignocellulose is degraded by lignocellulolytic enzymes, primarily hydrolases and some oxidoreductases, which are classified within different groups of carbohydrate-active enzymes (CAZymes). Fungal cellulases play a central role in the decomposition of lignocellulose and complex organic matter, thereby improving the availability of carbon, nutrients and water. Metaproteomics analysis revealed that glycoside hydrolases (GHs) were the most abundantly expressed CAZymes and were major contributors to lignocellulose decomposition. They were predominantly derived from the \u003cem\u003eAscomycota\u003c/em\u003e classes \u003cem\u003eEurotiomycetes\u003c/em\u003e and \u003cem\u003eSordariomycetes\u003c/em\u003e, while the other fungal taxa contributed sparsely (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEnzymes were further categorised according to the lignocellulosic substrates they target. Among the CAZymes, GHs were the most highly expressed, with β-glucosidases (GH1, GH3), xylanases (GH3, GH10, GH11), arabinofuranosidases (GH43, GH51), and α- and β-galactosidase (GH2, GH35) being particularly abundant. These enzymes were largely contributed by \u003cem\u003eAscomycota\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-c, S7, Tables S8a-b). Auxiliary activity (AA) enzymes were the next most expressed group, notably pyranose:acceptor oxidoreductase (AA3) derived from \u003cem\u003eBasidiomycota\u003c/em\u003e and especially abundant in the Podzol. Most identified CAZymes were extracellular, particularly those affiliated with \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eChytridiomycota\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003ec). Although \u003cem\u003eAscomycota\u003c/em\u003e were the dominant producers of CAZymes, a proportion of their enzymes were predicted to be intracellular (e.g., AA4, GH2). Under D conditions, pathotrophic and symbiotrophic \u003cem\u003eAscomycota\u003c/em\u003e showed increased expression of GH12 and GH74 endoglucanases (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003ea). In contrast, GH6 cellobiohydrolases were overexpressed by saprotrophic \u003cem\u003eBasidiomycota\u003c/em\u003e but displayed reduced expression in saprotrophic \u003cem\u003eAscomycota\u003c/em\u003e. The auxiliary enzyme lytic-polysaccharide monooxygenase (LPMO; AA9), produced by \u003cem\u003eAscomycota\u003c/em\u003e across all trophic modes, was relatively upregulated under D. Hemicellulose-degrading enzymes, including acetylxylan and feruloyl esterase (CE1), declined under D in saprotrophic \u003cem\u003eAscomycota\u003c/em\u003e, but were overexpressed by saprotrophic \u003cem\u003eBasidiomycota\u003c/em\u003e. Oxidative stress conditions were associated with increased expression of intracellular vanillyl-alcohol oxidase (AA4), which is involved in vanillin and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e production. Contrastingly, enzymes involved in lignin and pectin degradation were not significantly affected by D relative to RF. Overall, contributions from \u003cem\u003eChytridiomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e were substantially lower than those from the \u003cem\u003eDikarya\u003c/em\u003e phyla.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the impact of drought on fungal communities and their biochemical pathways within reused winter cover crop root channels subsequently utilised by maize roots. Reuse of these root channels facilitates deeper maize root penetration by reducing mechanical resistance from the soil particles and improving access to subsoil nutrients. While previous studies demonstrated bacterial enrichment within the reused root channels\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, our findings extend this understanding to fungi, revealing coordinated structural and functional responses shaped by cover crop identity, soil type and moisture availability.\u003c/p\u003e \u003cp\u003eFungal diversity and abundances varied among soil types, and were more strongly influenced by root channel reuse. In the drought-prone Luvisol and Podzol, richness and ITS2 gene copy numbers increased under drought, particularly within \u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e root channels, whereas responses differed under fallow and \u003cem\u003eFabaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e treatments. Declines in \u003cem\u003eChytridiomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e under drought are consistent with previous bulk soil observations\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In contrast, enrichment of arbuscular mycorrhizal fungi (\u003cem\u003eGlomeromycetes\u003c/em\u003e and \u003cem\u003eMucoromycetes\u003c/em\u003e within \u003cem\u003eMucoromycota\u003c/em\u003e) under drought likely reflects their stress-tolerant life history traits and ability to sustain nutrient exchange with host plants under water limitation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eZoopagomycota\u003c/em\u003e increased under drought\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, despite having phylogenetic similarity to \u003cem\u003eMucoromycota\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, indicating niche differentiation among zygosporangia-forming lineages. The dominant \u003cem\u003eDikarya\u003c/em\u003e (\u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e) exhibit limited overall shifts, consistent with their ability to survive under resource limitations through stress-adaptive measures such as sporulation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Associations between fungal phyla and soil physicochemical properties further support niche partitioning across soil profiles\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Fast-growing taxa such as \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eChytridiomycota\u003c/em\u003e and \u003cem\u003eZoopagomycota\u003c/em\u003e preferentially exploit labile organic C and available N\u003csup\u003e31\u003c/sup\u003e, explaining their positive correlations with TOC and TN in the nutrient-rich Phaeozem and their predominance in the topsoil. Conversely, negative correlations for \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e with TOC and TN are consistent with adaptation to lower nutrient conditions in subsoils. \u003cem\u003eBasidiomycota\u003c/em\u003e, known to establish symbiotic relationships with maize roots\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, may benefit from enhanced root penetration into deeper subsoil via reused root channels. These patterns reflect pronounced vertical stratification, whereby topsoil-dominated phyla respond to nutrient availability, while subsoil-dominating communities exhibit adaptations linked to nutrient limitations and symbiotic relationships. Rare fungal phyla in this study, \u003cem\u003eAphelidiomycota\u003c/em\u003e and \u003cem\u003eCryptomycota\u003c/em\u003e, prefer low moisture and low pH conditions\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which explains the negative moisture and pH correlations. Drought-associated shifts in trophic modes, characterised by increased representation of saprotrophic and symbiotrophic fungi and reduced pathotrophs, likely reflect improved C sequestration besides plant defence responses and the release of plant root-derived metabolites\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo elucidate the functional dynamics underlying these structural changes within fungal communities in the reused root channels, we used metaproteomics. Upregulation of the fatty acid biosynthesis enzyme 3-hydroxydecanoyl-[ACP]-dehydratase advocates membrane restructuring and adjustments in energy storage under drought\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, even in soils such as the Luvisol and Phaeozem that retain substantial moisture. Within \u003cem\u003eFabaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e root channels in the drought-affected Podzol, downregulation of \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e-derived PDC enzymes reduces acetyl-CoA flux into the citric acid cycle, thereby conserving energy under stress\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Pyruvate may instead be redirected towards proline biosynthesis, an established osmoprotectant pathway\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Downregulation of the proline-degrading enzyme P5CDH in \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eBasidiomycota\u003c/em\u003e, \u003cem\u003eChytridiomycota\u003c/em\u003e, and \u003cem\u003eMucoromycota\u003c/em\u003e in \u003cem\u003eFabaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e root channels and fallow favour proline accumulation, enhancing osmotic protection and ROS scavenging\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In contrast, P5CDH upregulation in \u003cem\u003eBrassicaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e root channels may reflect inherent stress tolerance or regulation of preventing proline-inflicted cell toxicity\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Oxidative stress regulation emerged as a prominent feature of fungal drought adaptation. Increased expression of superoxide dismutase (SOD), catalase-peroxidase (CAT-PER) and aldehyde dehydrogenase (ALDH) indicates coordinated detoxification of ROS and reactive aldehydes\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Treatment-specific expressions of these enzymes suggest that fungi within \u003cem\u003eBrassicaceae\u003c/em\u003e/\u003cem\u003ePoaceae\u003c/em\u003e root channels may possess greater oxidative resilience and may have potentially developed \u0026lsquo;stress memory\u0026rsquo; that invokes counter-mediating approaches under drought\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Overexpression of glycolytic aldolase in \u003cem\u003eBlastocladiomycota\u003c/em\u003e, \u003cem\u003eChytridiomycota\u003c/em\u003e and \u003cem\u003eZoopagomycota\u003c/em\u003e also contributes to cellular repairs by using root exudates as primary C sources for energy\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Higher expression of 6-PGL in the pentose phosphate pathway, particularly in the Podzol, implies enhanced NADPH generation essential for glutathione-dependent redox buffering\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings point towards integrated regulation of energy metabolism and redox homeostasis under water limitation.\u003c/p\u003e \u003cp\u003eIncreased expression of N metabolism enzymes in the Podzol underscores metabolic plasticity in nutrient-poor sandy soils. Upregulation of glutamate dehydrogenase (GDH) and urease suggests alternative N assimilation routes\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, while GDH-mediated deamination may provide C skeletons during limited carbohydrate availability\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Concurrent overexpression of methionine cycle and transsulfuration pathway enzymes, such as homocysteine methyltransferase, MeSe and SAM synthase across \u003cem\u003eDikarya\u003c/em\u003e, zygosporic \u003cem\u003eMucoromycota\u003c/em\u003e and \u003cem\u003eZoopagomycota\u003c/em\u003e, and zoosporic \u003cem\u003eChytridiomycota\u003c/em\u003e under drought, potentially catalyse SAM to synthesise abiotic stress-regulating polyamines\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and glutathione, central mediators of abiotic stress tolerance\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Notably, these metabolic adjustments were most pronounced in the coarse-textured Podzol, whereas the silt-rich Phaeozem exhibited comparatively moderate functional restructuring, underscoring the role of soil physical context in mediating drought responses.\u003c/p\u003e \u003cp\u003eHigher expression of CAZymes may have resulted because of the increased presence of Ascomycota (\u003cem\u003eEurotiomycetes\u003c/em\u003e, \u003cem\u003eSordariomycetes\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e (\u003cem\u003eMucoromycetes\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, all being previously reported as major fungal CAZyme producers. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The predominance of extracellular CAZymes indicates active mobilisation of complex organic matter and breaking down to simpler forms for emergency metabolic steps. Increased expression of cellulose-degrading enzymes β-glucosidase, endoglucanase, exoglucanase and LPMO by \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e, enhance the breakdown of complex carbohydrates into soluble sugars\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, potentially contributing to osmotic homeostasis and cellular stability during drought. These soluble sugars assist in the mobilisation of smaller molecules during water scarcity under drought. The prominence of auxiliary active oxidoreductases, particularly in the Podzol, further suggests active redox-coupled decomposition under drought. Elevated vanillyl-alcohol oxidase activity supports redox balancing through regulated H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e production. Similarly, hemicellulolytic enzymes such as acetylxylan/feruloyl esterase, arabinofuranosidase and xylanase from \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e likely contribute to cell wall remodelling under stress and metabolic flexibility under stress, requiring simpler sugars synthesised by degrading hemicelluloses\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterpreting microbial dynamics in reused root channels is difficult because these environments are highly variable in space and time. Conditions such as soil moisture, nutrient availability, oxygen levels, and soil structure can change over short distances. These changes strongly influence how microbes grow, interact, and survive. As a result, microbial patterns in these channels are often complex and context-dependent.\u003c/p\u003e \u003cp\u003eThe open nature of root channels means they are continuously influenced by surrounding soil properties. This makes it challenging to clearly link environmental factors to specific microbial responses. In addition, results can differ depending on the taxonomic level examined. For example, we observed an increase in arbuscular mycorrhizal fungi (class \u003cem\u003eGlomeromycetes\u003c/em\u003e) under drought conditions, which agrees with the idea that plants rely more on fungal partners when water is limited. However, this pattern was not consistent at the broader phylum level (Mucoromycota). This shows that broad taxonomic groups may hide important ecological \u003cem\u003edifferences\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAnother limitation is the small number of studies focusing on soil fungal communities, especially within rhizosphere environments and structured microsites such as reused root channels. Because of this limited evidence, it is difficult to directly compare or fully confirm our findings.\u003c/p\u003e \u003cp\u003eFurther research is needed to better understand how fungal communities develop and respond to environmental stress belowground. Studies that combine detailed spatial sampling with functional analyses will help clarify the dynamic processes shaping fungal communities in soil.\u003c/p\u003e \u003cp\u003eIn conclusion, drought indicates coordinated structural and functional adaptations in fungal communities inhabiting the maize rhizosphere within reused cover crop root channels. These responses involve regulation of oxidative stress pathways to counter stress and reduction of C-N metabolic pathways to conserve energy, which varied with soil type and cover crop choices. Root channels formed by mixtures of \u003cem\u003eBrassicaceae\u003c/em\u003e and \u003cem\u003ePoaceae\u003c/em\u003e supported fungal communities that showed strong oxidative stress regulation and improved resource management. This pattern suggests a higher tolerance to drought. Therefore, such cover crop mixtures may represent suitable choices for future cropping strategies under similar environmental conditions. Overall, our findings provide mechanistic insight into how cover crop selection can shape fungal-mediated soil processes. They support the concept of strategic cover cropping as an approach to strengthen agroecosystem resilience under climate-induced oxidative stress.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eCrop cultivation and sampling regimes\u003c/h3\u003e\n\u003cp\u003eCrops were grown in three agricultural fields \u0026ndash; at experimental estates Hohenschulen of the Kiel University (Achterwehr, Germany, 54\u0026deg;18\u0026rsquo;44\u0026rdquo; N, 9\u0026deg;59\u0026rsquo;46\u0026rdquo; E), Karkendamm of the Kiel University (Bad Bramstedt, Germany, 53\u0026deg;55\u0026rsquo;52\u0026rdquo; N, 9\u0026deg;55\u0026rsquo;15\u0026rdquo; E), and Reinshof of the Georg-August-University of G\u0026ouml;ttingen (Rosdorf, Germany, 51\u0026deg;29\u0026rsquo;05\u0026rdquo; N, 9\u0026deg;53\u0026rsquo;34\u0026rdquo; E). The cash crop in this study was maize (\u003cem\u003eZea mays\u003c/em\u003e L.). Plots without cover crops during the winter (bare fallow) were established as a control and compared against two cover crop mixtures on distinct plots. The cover crops were shallow- and deep-rooting \u003cem\u003eBrassicaceae\u003c/em\u003e (\u003cem\u003eBrassica napus\u003c/em\u003e L., rapeseed, shallow-rooting; \u003cem\u003eRaphanus sativus\u003c/em\u003e L.\u003cem\u003e \u003c/em\u003evar.\u003cem\u003e oleiformis\u003c/em\u003e, oilseed radish, deep-rooting); \u003cem\u003eFabaceae\u003c/em\u003e (\u003cem\u003eTrifolium repens\u003c/em\u003e L., white clover, shallow-rooting;\u003cem\u003e Trifolium pratense\u003c/em\u003e L., red clover, deep-rooting); and \u003cem\u003ePoaceae\u003c/em\u003e (\u003cem\u003eLolium perenne\u003c/em\u003e, perennial ryegrass, shallow-rooting;\u003cem\u003e Festuca arundinaceae\u003c/em\u003e, tall fescue, deep-rooting). The mixtures were grown as a combination of shallow- and deep-rooting cover crops of \u003cem\u003eBrassicaceae\u003c/em\u003e, \u003cem\u003eFabaceae\u003c/em\u003e and \u003cem\u003ePoaceae,\u003c/em\u003e complementing the niche complementarity principle, which has been reported to allow polycultures to overyield when plants compete for resources\u003csup\u003e53\u003c/sup\u003e. All the cover crops were sown in October 2022 and grew until May 2023 in distinct randomised plots with four replicates of each variation. In May 2023, a herbicide formulation (Roundup, Bayer AG, Leverkusen, Germany) was applied to all experimental plots (including fallow plots) to terminate all cover crops, and subsequently maize was sown in the same plots with the cover crop variations in addition to the fallow plots. Maize was grown in the fields from May to September 2023 (Fig. 1). \u003c/p\u003e\n\u003cp\u003eTo compare drought-like conditions against normal conditions, we artificially induced dry conditions using interrow rainout shelters and continued with the approach after observing differences in soil moisture trends between the sheltered and non-sheltered profiles using time-domain reflectometry sensors (Fig. S8a-c). These specific types of rainout shelters covered half of the plot area between the maize rows, reducing 50% rainfall and restricting water infiltration to stemflow. The shelters were installed between the crop rows and below the maize canopy in June, at approximately 50 cm aboveground. This position ensured that the structures did not interfere with leaf-level photosynthesis or direct light interception by the maize plants. They were constructed with a sloped configuration to promote air flow and reduce the risk of heat accumulation or stagnant humidity. The plastic film used was standard greenhouse-grade transparent polyethene, which allows high light transmission while providing effective rain exclusion. A depiction of our rainout shelter setup used in the experiment has been provided as a supplementary figure (Fig. S9). \u003c/p\u003e\n\u003cp\u003eBefore soil sampling, the soil profile was excavated to a depth of 1 m, then excavated 40 cm forward to obtain a fresh profile and fresh maize root system and to prevent any contamination by the neighbouring soil. To compare the difference inflicted by drought on microbial communities in the reused root channels of cover crops by maize, we collected soil samples from a vertical soil profile from the topsoil (0-30 cm) and the subsoil (30-60 cm and 60-90 cm) for two different conditions: 1) maize roots growing in the cover crop root biopores (MCR) from those profiles subjected to drought (D), and 2) maize roots growing in the MCR in the profiles under rainfall-fed conditions (RF). Maize rhizosphere soil collected along decayed cover crop roots was considered as originating from maize reusing cover crop root channels. The rhizosphere of maize and decayed cover crop roots was defined as extending 2 mm from the root surface. Therefore, the overlapping 2 mm rhizosphere zone of maize and decayed cover crop roots was designated as the sampling area. The sampling focused on clearly visible maize roots that were closely associated with remaining cover crop root residues, ensuring consistency in sample type across replicates and treatments to reduce subjective variation and avoid systematic error. Pictures of the reused root channels have been provided as a supplementary figure (Fig. S3). Additionally, maize rhizosphere samples from the control plot were collected, representing maize roots growing in bulk soil without reusing cover crop root channels. The sampling was done during the R1-RX growth stage of maize (bolting) grown in the Luvisol (01.08.2023), the Phaeozem (19.07.2023) and the Podzol (26.07.2023), and the profiles were maintained until sampling was done around the flowering and reproduction stage of maize. The samples were extracted from the profiles using a spatula and collected in plastic zip-lock bags. Until shipment to the laboratory, samples were stored in ice coolers containing dry ice in order to preserve microbial communities and the metabolic picture for distinct sampling time points. In the laboratory, all samples were stored at -80\u0026deg;C until further processing. No repetition of sampling was done from the profiles of the same plot in order to avoid bias and duplicates. To minimise potential operator bias when identifying \u0026ldquo;root overlap regions\u0026rdquo;, we used a randomised sampling scheme with the four blocks representing the four replicates and the ambient (natural rainfall) and drought (with interrow rainout shelters) plots always paired in direct proximity to each other. \u003c/p\u003e\n\n\u003ch2\u003eSoil physicochemical properties\u003c/h2\u003e\n\u003cp\u003eSoil microbial biomass carbon (C) and nitrogen (N) were determined using the chloroform fumigation extraction method\u003csup\u003e54\u003c/sup\u003e. In brief, 7.5 g of soil was fumigated with chloroform for 24 h and then extracted with 30 mL of 0.05 M K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e on a shaker for 1 h. C and N were measured with the N/C 2100 TOC/N analyser (Analytik Jena, Jena, Germany). MBC was calculated as the difference between extracted C from fumigated and non-fumigated soil with a conversion factor (\u003cem\u003ek\u003csub\u003eC\u003c/sub\u003e\u003c/em\u003e) of 0.45\u003csup\u003e55\u003c/sup\u003e. MBN was calculated as the difference between extracted N from fumigated and non-fumigated soil with a conversion factor (\u003cem\u003ek\u003csub\u003eN\u003c/sub\u003e\u003c/em\u003e) of 0.54\u003csup\u003e55,56\u003c/sup\u003e. The MBC and MBN were presented as \u0026micro;g g\u003csup\u003e-1\u003c/sup\u003e dry soil. Soil density and moisture content were also measured alongside.\u003c/p\u003e\n\u003cp\u003eSoil cylinder samples were taken to measure soil bulk density at each soil depth. Soil moisture content was also measured by water content sensors (Teros 10, Meter Group, M\u0026uuml;nchen, Germany), which were installed in the control plots (in 0-30 cm, 30‒60 cm, and 60-90 cm depth under rainout shelter and on the rainfall-fed side) at each experimental site.\u003c/p\u003e\n\u003cp\u003eTo investigate the influence of soil physicochemical properties on the fungal microbiome in the maize root channels, we correlated the parameters pH, soil moisture content, TOC, TN, and soil bulk density with the ITS2 gene amplicon data using Partial Mantel\u0026rsquo;s test\u003csup\u003e57\u003c/sup\u003e using the R package \u003cem\u003emicroeco\u003c/em\u003e (v1.10)\u003csup\u003e58\u003c/sup\u003e. The soil physicochemical proteins were correlated to individual fungal phyla along the different soil types and soil moisture conditions to understand the impacts of these factors on the individual communities. \u003c/p\u003e\n\n\u003ch2\u003eITS2 gene amplicon sequencing\u003c/h2\u003e\n\u003cp\u003eFungal community composition in the root-vicinity samples was analysed by sequencing amplicons targeting the fungal internal transcribed spacer (ITS) regions of rRNA (2*300 bp) on an Illumina NextSeq\u0026trade; 550 (Illumina, San Diego, CA, USA). DNA was extracted from 0.25 g of soil using the DNeasy\u003csup\u003e\u0026reg;\u003c/sup\u003e PowerSoil\u003csup\u003e\u0026reg;\u003c/sup\u003e Pro Kit (QIAGEN GmbH, Hilden, Germany). PCR amplicons of the ITS2 region of the fungal rRNA gene were prepared using the forward and reverse primers fITS7 and ITS4 and the NEBNext\u003csup\u003e\u0026reg;\u003c/sup\u003eUltra\u0026trade; II Q5\u003csup\u003e\u0026reg;\u003c/sup\u003e Master Mix (New England Biolabs GmbH, Frankfurt, Germany)\u003csup\u003e59\u003c/sup\u003e. Sequencing libraries were prepared from 100 ng of DNA according to the Illumina protocol. Dual index adapters for the sequencing were attached using the NEBNext Multiplex Oligos for Illumina. The final concentration of the libraries was 2 nM after pooling. We sequenced triplicates of samples from each soil depth and root-vicinity source per cover crop variation plot for all three sampling sites (ITS2 gene amplicons, \u003cem\u003en\u003c/em\u003e=147).\u003c/p\u003e\n\u003cp\u003eThe sequencing data were analysed using QIIME2 v2023.5\u003csup\u003e60\u003c/sup\u003e. First, the raw sequence reads were demultiplexed and quality-filtered (q-score 25) using the q2‐demux plugin, followed by denoising with q2‐dada2. The ITS2 regions of the fungal rRNA were trimmed to 230 bp for both the forward and reverse sequences. All amplicon sequence variants (ASVs) were aligned with q2‐alignment, and then maximum-likelihood trees were constructed using q2‐phylogeny. We chose ASV-based methods over OTU approaches to limit the effect of spurious taxa on diversity indices\u003csup\u003e61\u003c/sup\u003e. Taxonomic assignment of fungal ASVs was carried out using the q2‐feature‐classifier and the classify-sklearn Na\u0026iuml;ve Bayes taxonomy classifier against the UNITE v10.0 database\u003csup\u003e62\u003c/sup\u003e for QIIME2 (released on 04.04.2024). ASVs with a relative abundance of \u0026lt;0.01% were defined as rare taxa.\u003c/p\u003e\n\n\u003ch2\u003eQuantitative PCR (qPCR)\u003c/h2\u003e\n\u003cp\u003eThe copy number of the fungal ITS2 gene per gram of soil was quantified by SYBR\u003csup\u003e\u0026reg;\u003c/sup\u003e Green-based qPCR using a 7500 Fast Real-Time PCR System (Applied Biosystems\u0026trade;, Thermo Fisher Scientific, Waltham, MA, USA). Aliquots of the same DNA extract utilised in amplicon sequencing were used for qPCR. Dilutions of template DNA were used to compensate for the effect of PCR inhibitors in the samples. Each sample was analysed in triplicate. A PCR amplicon of the ITS2 region derived from \u003cem\u003eTrametes versicolor \u003c/em\u003e(DSM 11269) was used to generate the standard curve. Each 20 \u0026micro;L reaction contained 1 \u0026micro;L of template DNA, the forward and reverse primers fITS7 and ITS4 for the ITS2 gene\u003csup\u003e59\u003c/sup\u003e without adapter nucleotides and Luna\u003csup\u003e\u0026reg;\u003c/sup\u003e Universal qPCR Master Mix (NEB). Reaction conditions were an initial denaturation for 1 min at 95\u0026deg;C, followed by 40 cycles of denaturation at 95\u0026deg;C for 15 s and extension at 60\u0026deg;C for 30 s. The melting curve was recorded in the temperature range of 60\u0026deg;C to 95\u0026deg;C. The gene copy numbers per gram of soil were determined in comparison against the standard essentially as before\u003csup\u003e63\u003c/sup\u003e. The average efficiency value was 100.8 \u0026plusmn; 3.2%.\u003c/p\u003e\n\n\u003ch2\u003eMetaproteomics analysis\u003c/h2\u003e\n\u003cp\u003eAt each timepoint, samples were collected separately from three plots for each cover crop variation at the analysed soil depths and root-vicinity sources and used for proteomic analyses following a previously described protocol\u003csup\u003e5\u003c/sup\u003e (\u003cem\u003en\u003c/em\u003e=119). Approximately 4 g of soil was used for protein extraction using the SDS buffered-phenol extraction method as previously described\u003csup\u003e5\u003c/sup\u003e. The protein extract was purified using 1-D SDS-PAGE, and then the protein extract was further proteolytically cleaved using trypsin (Promega). A nano-HPLC system (UltiMate\u0026trade; 3000 RSLCnano system, Thermo Fisher Scientific, Waltham, MA, USA) was used to separate the peptide lysates. The system was connected to a Q Exactive HF Orbitrap LC-MS/MS system (Thermo Fisher Scientific) equipped with a nano electrospray ion source, Triversa NanoMate\u003csup\u003e\u0026reg;\u003c/sup\u003e (Advion, Ithaca, NY, USA). We searched the MS/MS data against an in-house generated proteome database containing all the defined proteomes in UniProt for the fungi identified by ITS2 gene amplicon sequencing. The database search was performed with Proteome Discoverer\u0026trade; (v2.5.0.8, Thermo Fisher Scientific) using the SEQUEST-HT algorithm, and all of the outputs are available on PRIDE (EMBL-EBI)\u003csup\u003e64\u003c/sup\u003e. The precursor mass tolerance of the MS was set to 10 ppm, and the fragment mass tolerance of the MS/MS was 0.02\u0026thinsp;Da. Carbamidomethylation of cysteine was considered fixed, and oxidation of methionine was set as a dynamic modification. Enzyme specificity was set to trypsin with up to two missed cleavages allowed using 10\u0026thinsp;ppm peptide ions and 0.02\u0026thinsp;Da MS/MS tolerances. Only rank-one peptides with a Percolator-estimated false discovery rate (FDR)\u0026thinsp;\u0026lt;1% were accepted as identified. The GhostKOALA and KEGG\u003csup\u003e65\u003c/sup\u003e and COG\u003csup\u003e66\u003c/sup\u003e databases were used for protein functional annotation. Pathways with a minimum of two proteins and a minimum coverage of 5% were selected for downstream processing. The carbohydrate-active enzymes (CAZymes) were identified using Unipept Desktop (v2.0.0, Ghent University)\u003csup\u003e67\u003c/sup\u003e. The identified CAZy enzymes and their preferred substrates provide information regarding rhizo-deposits in the soil profile along the cover crop root channels. To estimate the proportion of extracellular carbohydrate-active enzymes (CAZymes), signal peptides were predicted using Phobius\u003csup\u003e68\u003c/sup\u003e and proteins harbouring a signal peptide were classified as extracellular. Label-free quantification (LFQ) intensities of the identified proteins were analysed to characterise functional metabolic pathways and to assess their variation across sampling sites, soil moisture regimes, soil depths, and cover crop treatments.\u003c/p\u003e\n\u003cp\u003eA custom reference database was constructed from the UniProtKB database by selecting protein sequences corresponding to the fungal phyla identified through ITS2 gene amplicons. During database assembly, redundancy was minimised while maintaining taxonomic and functional relevance to avoid repeated assignment of already quantified proteins. Only proteins unambiguously identified by unique peptides and successfully mapped to the UniProt reference database were retained for quantification.\u003c/p\u003e\n\u003cp\u003eTaxonomic identities were retrieved using ENTREZ identifiers from the NCBI database\u003csup\u003e69,70\u003c/sup\u003e employing KEGG Orthology (KO) numbers as unique protein identifiers. Mapping identified proteins to NCBI entries enabled comprehensive taxonomic annotation of their source organisms. Non-fungal proteins were excluded from further analyses. Functional pathway annotation was performed using KEGG and COG identifiers, which were linked to individual proteins. The integration of KO-based taxonomic assignments with pathway annotations enabled the generation of combined datasets capturing both community composition and functional potential.\u003c/p\u003e\n\u003cp\u003eLFQ intensities were normalised by log₂ transformation using the \u003cem\u003elog\u003c/em\u003e function of base R (v4.3.1)\u003csup\u003e71\u003c/sup\u003e prior to visualisation and statistical tests. Following pathway categorisation, proteins were mapped onto biogeochemical cycles of interest to evaluate shifts associated with soil properties, moisture status, and the re-use of cover crop root biopores, thereby elucidating microbial functional responses. \u003c/p\u003e\n\u003cp\u003eTo assess the impact of environmental stress following root channel re-use, fold changes in LFQ intensities under drought conditions were calculated relative to rainfall-fed controls. Proteins were considered differentially abundant when |log₂ fold change| \u0026gt;0.6 and \u003cbr\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt;0.05 \u003csup\u003e72\u003c/sup\u003e. In addition, to quantify the effects of cover crop root channel re-use, differential abundances were determined by subtracting protein intensities measured under cover crop treatments (\u003cem\u003eBrassicaceae/Poaceae\u003c/em\u003e and \u003cem\u003eFabaceae/Poaceae\u003c/em\u003e rotations) from those observed under fallow conditions.\u003c/p\u003e\n\n\u003ch2\u003eStatistical data analysis\u003c/h2\u003e\n\u003cp\u003eWe used R (v4.3.1)\u003csup\u003e71\u003c/sup\u003e to perform all statistical analyses of the sequencing and the metaproteomics data. All measures of significance were calculated using permutational multivariate analysis of variance (PERMANOVA), followed by Tukey\u0026rsquo;s range post-hoc test (TukeyHSD) with package \u003cem\u003estats \u003c/em\u003e(v3.6.2)\u003csup\u003e73\u003c/sup\u003e. In the ITS2 gene sequencing analysis, the ASV abundance tables were filtered with total-frequency-based filtering based on 95% sequence identity (via q2-feature-table summarize) and rarefied at 30,000 sequences to ensure equal sampling depth and sorting in the maximum number of samples for diversity analyses. Alpha and beta diversity metrics were calculated using the packages \u003cem\u003ephyloseq\u003c/em\u003e\u003csup\u003e74\u003c/sup\u003e and \u003cem\u003emetacoder\u003c/em\u003e from R (v4.3.1)\u003csup\u003e71\u003c/sup\u003e. Observed ASV richness measured for each cover crop variation at different sampling sites and conditions was used for estimating alpha diversity richness. Pielou\u0026rsquo;s evenness is the most widely used diversity evenness index in the ecological literature\u003csup\u003e75\u003c/sup\u003e. For beta diversity, we used Bray-Curtis dissimilarity\u003csup\u003e76\u003c/sup\u003e and visualised differences via Principal Coordinate Analysis (PCoA) using the \u003cem\u003evegan\u003c/em\u003e package (v2.6-4)\u003csup\u003e77\u003c/sup\u003e. Using a four-way permutational multivariate analysis of variance (PERMANOVA), we evaluated the significantly different cover crop variations using the sampling sites, sampling conditions and sampling depths as random effects and cover crop variations as the fixed effect. This was followed by Tukey\u0026rsquo;s HSD for evaluating MANOVA test outcomes with parameters of cover crop variations, depth, sampling sites, soil moisture conditions and fungal phyla. For fungal abundances under different parameters, the significance between the parameters was represented using the Compact Letter Display (CLD)\u003csup\u003e78\u003c/sup\u003e since we can represent multiple pairwise significances using linear models. For metaproteomics, the significantly different cover crop variations or proteins of different metabolic pathways or fungal phyla were calculated using MANOVA, using cover crop variations, sampling depths, soil moisture conditions, sampling sites and fungal phyla as fixed factors. Upon determination, they were represented by significant stars based on the adjusted \u003cem\u003ep\u003c/em\u003e-values (*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). All figures were generated in RStudio. Other integrated packages used for statistical analyses and figure generation were \u003cem\u003etidyverse\u003c/em\u003e (v2.0.0), and \u003cem\u003edplyr\u003c/em\u003e (v1.1.3).\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe metaproteomics datasets generated during the current study are available in the PRIDE data repository with the sample metadata, vide PRIDE dataset identifier PXD062138 (https://www.ebi.ac.uk/pride). The raw sequencing data and the respective metadata generated from this study are available under the NCBI BioProject ID PRJNA1240274, which can be accessed using the link: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1240274. The raw qPCR data, along with the sample metadata, are available on Zenodo under the DOI: https://doi.org/10.5281/zenodo.18889777.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eD.G. would like to take this opportunity to thank Helmholtz Centre for Environmental Research \u0026ndash; UFZ GmbH, especially the UFZ-funded ProMetheus platform for metaproteomics and support. We acknowledge Kathleen Eismann for her help in sample preparation for metaproteomic assessments; Madlen Schubert for \u003cem\u003eTrametes\u003c/em\u003e \u003cem\u003eversicolor\u003c/em\u003e DSM 11269 strain; Habibu Aliyu, Florian Lenk and David Thiele for their assistance during Illumina NextSeq\u0026trade; sequencing; Stephan Schreiber for advice during NextSeq\u0026trade; sample preparations; Katja Holzhauser and Tobias St\u0026uuml;rzebecher for assistance in fieldwork; Iris M. Zimmermann and Sandra Spielvogel with the project administration and management; and Matthias Bernt for his advises during data analysis and as the administrator of the Galaxy UFZ computational workbench.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNJ has received funding from the project 2020-RootWayS-BMBF under the section of Rhizo4Bio (FKZ: 031B0911B, Phase 1), sanctioned by the Federal Ministry of Education and Research (BMBF), Germany. N.J., J.A.M., M.v.B. and A.-K.K. were supported by the Helmholtz Association of German Research Centers through its research program \u0026ldquo;PoF IV\u0026rdquo;. The amplicon sequencing and metaproteomics data were computed on Galaxy UFZ and the High-Performance Computing (HPC) Cluster EVE, a joint effort of Helmholtz Centre for Environmental Research \u0026ndash; UFZ GmbH and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig.\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Molecular Toxicology, Helmholtz Centre for Environmental Research \u0026ndash; UFZ GmbH, Leipzig, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDebjyoti Ghosh, Nico Jehmlich, Martin von Bergen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitute of Biological Interfaces, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJochen A. M\u0026uuml;ller, Anne-Kristin Kaster\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Bio- and Environmental Sciences, International Institute Zittau, Dresden Institute of Technology, Zittau, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHarald Kellner\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitute of Plant Nutrition and Soil Science, Kiel University, Kiel, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYijie Shi, Iris M. Zimmermann, Sandra Spielvogel\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitute of Crop Science and Plant Breeding, Kiel University, Kiel, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKatja Holzhauser\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiogeochemistry of Agroecosystems, University of G\u0026ouml;ttingen, G\u0026ouml;ttingen, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTobias St\u0026uuml;rzebecher\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeo-Biosphere Interactions, University of T\u0026uuml;bingen, T\u0026uuml;bingen, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMichaela A. Dippold\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitute for Biochemistry, University of Leipzig, Leipzig, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMartin von Bergen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMartin von Bergen\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eN.J., J.A.M., M.A.D., and S.S. conceived and designed the study; D.G., Y.S., I.M.Z., T.S. and K.H. organised and coordinated fieldwork; D.G. performed the amplicon sequencing and metaproteomics experiments; Y.S. performed the soil physicochemical characterisations; D.G. analysed and interpreted all experimental observations; D.G., N.J., H.K and J.A.M wrote the paper with inputs from all authors; all authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCorresponding authors\u003c/p\u003e\n\u003cp\u003eCorrespondence to Nico Jehmlich ([email protected]) and Debjyoti Ghosh ([email protected]).\u003c/p\u003e\n\u003cp\u003eEthical declarations\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing interests that could have influenced the work being reported in this manuscript.\u003c/p\u003e\n\u003cp\u003eSupplementary information\u003c/p\u003e\n\u003cp\u003eAdditional supplementary information and extended data can be found in the Supplemental Text, Figures S1-S10 and Tables S1-S14.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGebrechorkos SH et al (2025) Warming accelerates global drought severity. 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J Comput Graph Stat 13:456\u0026ndash;466\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Cover crop, root channel re-use, fungal community, drought, soil types, metaproteomics","lastPublishedDoi":"10.21203/rs.3.rs-9052155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9052155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRoot channels formed by winter cover crop can enhance subsoil water and nutrient access for subsequent crops such as maize (\u003cem\u003eZea mays\u003c/em\u003e L.) yet their fungal inhabitants remain poorly understood under drought. Here, we assessed drought-induced shifts in maize rhizosphere fungal communities within reused cover crop root channels across three contrasting soil types in northern Germany (Luvisol, Podzol and Phaeozem). Using a multi-omics approach combining ITS2 amplicon sequencing, quantitative PCR and metaproteomics, we linked community composition with functional responses. Drought consistently restructured fungal communities, with increased relative abundances of \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eZoopagomycota\u003c/em\u003e and declines in \u003cem\u003eChytridiomycota\u003c/em\u003e and \u003cem\u003eMucoromycota\u003c/em\u003e. Taxa within the same subkingdom occupied complementary niches, indicating functional differentiation beyond higher-level taxonomy. At the protein level, drought responses were characterised either by enhanced antioxidant defence mechanisms including catalase\u0026ndash;glutathione peroxidase systems, superoxide dismutase, fatty acid synthesis and the methionine cycle\u0026ndash;transsulfuration pathway or by reduced carbon and nitrogen metabolic activity, suggesting energy conservation strategies. Together, our results demonstrate substantial structural and functional plasticity of rhizosphere fungal communities in reused root channels under water limitation, highlighting their potential role in microbiome-mediated drought resilience in agroecosystems.\u003c/p\u003e","manuscriptTitle":"Fungal response to drought in the maize rhizosphere after reusing cover crop root channels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 07:05:33","doi":"10.21203/rs.3.rs-9052155/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":"d1d2864b-d368-4825-a63d-82419406e767","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64137011,"name":"Biological sciences/Ecology/Microbial ecology"},{"id":64137012,"name":"Biological sciences/Microbiology/Fungi/Fungal ecology"},{"id":64137013,"name":"Biological sciences/Microbiology/Environmental microbiology/Soil microbiology"},{"id":64137014,"name":"Biological sciences/Ecology/Climate-change ecology"},{"id":64137015,"name":"Earth and environmental sciences/Ecology/Microbial ecology"}],"tags":[],"updatedAt":"2026-03-16T03:56:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 07:05:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9052155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9052155","identity":"rs-9052155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}