Soil microbial genomic responses to aridity-driven elemental imbalance across global biomes

Soil microbial genomic responses to aridity-driven elemental imbalance across global biomes | 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 Soil microbial genomic responses to aridity-driven elemental imbalance across global biomes Youzhi Feng, Chunkai Li, Tadeo Sáez Sandino, Chao Xiong, David Eldridge, and 79 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6656511/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Aridification is known to disrupt soil biogeochemical cycles by altering the stoichiometry of key elements, such as carbon (C), nitrogen (N) and phosphorus (P). However, how soil microbiome metabolically responds to these imbalances, and the resulting implications for terrestrial ecosystem services, remain virtually unknown. By analyzing soil samples from 200 ecosystems across globally distributed biomes, our results revealed an overall microbial genomic adjustment to elemental imbalances driven by aridity. Genes involved in microbial anabolism were promoted with aridification, whereas those associated with catabolism were suppressed. Thus, while genes encoding extracellular enzymes responsible for the degradation of litter and organic matter decreased, genes related to RNA transcription and the synthesis and transport of intracellular proteins essential for microbial resistance and growth increased. Collectively, our findings highlighted that aridity-driven soil elemental imbalances are strongly associated with shifts in soil microbial metabolism from catabolism to anabolism, reflecting a microbial survival strategy that prioritizes growth over energy and nutrient acquisition under global-scale aridification. Earth and environmental sciences/Ecology/Microbial ecology Earth and environmental sciences/Biogeochemistry/Carbon cycle Biological sciences/Ecology/Climate-change ecology/Phenology Soil stoichiometry Terrestrial ecosystems Metagenome Extracellular enzymes Nutrient cycles Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global-scale changes in aridification have been widely recognized as disrupting the fundamental stoichiometric relationships that sustain terrestrial ecosystems 1 – 3 , leading to worldwide elemental imbalances in the cycling of soil organic carbon (SOC), nitrogen (N), and phosphorus (P) (e.g., altered soil C:N, N:P, and C:P ratios) 1 , 2 , 4 . For example, low precipitation in arid ecosystems often results in both limited plant productivity (reducing litter-derived organic C and N inputs) and a greater relative availability of P, resulting in low soil C:P and N:P ratios 5 . In contrast, the predominantly abiotic origin of P from bedrock 6 , unlike the biological fixation of C and N from the atmosphere, contributes to the higher soil C:P and N:P ratios in wetter ecosystems. These ecological imbalances should be particularly impactful for soil microbes because of their dependence on cellular elemental homeostasis and their intimate link to soil biogeochemistry 7 . Although soil microbes are among the most diverse and abundant organisms on the planet and provide essential ecosystem services that are crucial for human well-being (e.g., food security and climate action) 8 , 9 , little is known about the genomic responses of the soil microbiome under aridity driven elemental imbalances 10 . A deeper understanding of how the soil microbiome respond to aridity-driven changes in soil biogeochemistry is essential for better predicting the fate of soil microbes in a changing world. Two primary scientific barriers contribute to the current uncertainties regarding the responses of the soil microbiome to climate-driven elemental imbalances. First, there is a significant lack of a global-scale standardized database that simultaneously integrates information on stocks and pools of soil organic C, N, and P with high-resolution functional genomic data such as metagenomes. Most studies have been limited to local scales; however, a global-scale approach is essential because soil elemental imbalances occur at the macroecological level across global biomes, from deserts to tropical forests 11 . Second, changes in soil C:N:P ratios and/or elemental imbalances may result in important alterations in microbiome metabolism 12 . Recent advances in trait-based microbial ecology have suggested potential trade-offs between anabolism and catabolism 13 , 14 . For example, a recent study from a successional chronosequence in a subtropical region of China demonstrated that P limitation over time increases the abundance of genes involved in the degradation of recalcitrant compounds, such as lignin and its aromatic derivatives, to acquire P resources 15 . Yet, empirical evidence for such potential responses and itheir consequences on various soil processes, including soil organic C sequestration and nutrient cycling, remains limited on a global scale 16 . Previous studies have highlighted the need for generalizable frameworks for microbial responses to disturbances to enhance our understanding and predictability of ecosystem stability 17 . Understanding these microbial shifts is vital as they may reshape the delivery of terrestrial ecosystem services in the context of climate change 18 . Here we put together a global topsoil survey encompassing 200 terrestrial ecosystems was conducted across all major biomes, from deserts to tropical regions, and across a diverse array of vegetation types, including forests, grasslands, and shrublands (Figs. 1 a and 1 b). Our objective was to investigate how the soil microbiome responds to aridity-driven elemental imbalances across global environmental gradients 2 . To achieve this, we integrated data on soil C, N, and P stocks as well as stoichiometric ratios with shotgun metagenomic sequencing. This metagenomic approach enabled us to characterize microbial metabolism—both anabolic and catabolic—by analyzing key functional genes and metabolic pathways (a detailed list of genes involved in each metabolism is available in Table S1 ). The aridity index (mean annual precipitation (MAP) / potential evapotranspiration) was utilized as a proxy for water availability 19 , given its capacity to account for both the natural input of water into the ecosystem and potential losses resulting from evapotranspiration. We opted for water availability for simplicity because the aridity index can be counterintuitive (i.e., a higher aridity index indicates greater water availability). Initially, we identified the primary environmental factors associated with soil C:N:P ratios and pinpointed the tipping points in stoichiometric ratios using machine-learning approach. Subsequently, we identified the key anabolic and catabolic genes involved in soil elemental imbalances and examined how these genes are associated with organic C, N, and P contents on a global scale. Based on the Metabolic Theory of Ecology 12 , we hypothesized that soil microbes respond to aridity-driven elemental imbalances by shifting their metabolism to enhance anabolism (e.g., resistance to drought stress and sustained growth) while suppressing catabolism (e.g., synthesizing extracellular enzymes for energy and nutrient acquisition). Results and discussion Our study offers new insights into the capacity of microbiomes to respond to global-scale soil elemental imbalances driven by changes in aridity. Consistent with existing literature, ecosystems with lower water availability (i.e., a lower aridity index or higher aridity) demonstrated reduced net primary productivity (NPP) and lower soil C:P and N:P ratios (Fig. 1 ). Moreover, the random forest analysis indicated that both anabolic and catabolic genes were significant predictors of soil elemental ratios (Fig. 2 ). This suggests that soil microbiomes may prioritize specific metabolic strategies for survival under soil elemental imbalances resulting from aridification. For example, in arid environments characterized by lower soil C:N and N:P ratios, likely due to diminished organic matter inputs under less productive conditions, the abundance of genes associated with anabolism, such as those involved in RNA transcription and the synthesis and transport of intracellular proteins were increased. This response aims to sustain microbial growth under stressed conditions. Lower soils C:N and N:P ratios were associated with suppressed catabolism, as indicated by the reduced abundance of genes responsible for the degradation of litter and organic matter, processes that facilitate the extraction of N and P (Figs. 2 and 3 ). Collectively, our findings suggest that the microbiome employs metabolic strategies that involve shifts between catabolism and anabolism in response to worldwide soil elemental imbalances. These microbial metabolic shifts, necessary for survival under stoichiometric imbalances, can diminish the capacity of terrestrial ecosystems to provide multiple ecosystem services such as declining C reservoirs 20 , and may further compromise ecosystem stability. For instance, the suppression of genes involved in catabolism result in decreased degradation and transformation of external organic matter into the soil 21 , which could impair soil aggregate stability and moisture retention, making the soil more susceptible to erosion and subsequent nutrient depletion. This understanding is critical to better anticipate changes in the soil microbiome, the most abundant and diverse organism on the planet, and their ecological functions in the context of climate change. Aridity-driven soil elemental imbalances As anticipated, global-scale aridification— and its impact on ecosystem productivity— played a central role in driving soil elemental imbalances 2 . This finding is crucial for addressing our novel research question: how does the microbiome responds to soil elemental imbalances driven by changes in water availability across global environmental gradients? Decreases in water availability were associated with a relative deficiency of organic C compared with N and P in soils, resulting in declines in soil C:N and C:P ratios, particularly in low-productivity ecosystems. Random forest analysis identified climate variables as strong predictors of variation in soil C, N and P contents (Fig. S1 ). When combined with other environmental factors,— such as soil physicochemical properties, geography, and vegetation type—, these variables accounted for up to 73% of the observed variation (Figs. S1 and S2). Further analysis revealed that water availability and NPP were the most influential drivers of global variation in the soil C:N, N:P, and C:P ratios (Fig. 1 c). Reduced plant productivity can lead to lower soil N:P ratios as N becomes relatively deficient in more arid regions owing to the constrained microbial decomposition of plant litter (Figs. 1 d and S3) 22 and decreased biotic N fixation activities 23 . Although most P fractions remained relatively stable with changing water availability, the content of mineral P (represented by primary P) increased significantly with decreasing NPP ( P < 0.05), likely due to reduced plant uptake (Fig. S4) 24 . Notably, in these biologically inactive ecosystems, diminished plant productivity contributes to lower soil organic C accumulation through decreased inputs of litter and root exudates, further exacerbating elemental imbalances (i.e., lower soil C:N and C:P ratios) (Figs. 1 d and S4) 25 . Beyond the conclusion that both C and N stocks and pools are the primary limiting factors for soil microbial growth in drylands 2 , these results provide evidence that their stoichiometric balance will become increasingly significant in light of climate change consequences, such as heightened aridity. Soil microbiome responses to aridity-driven element imbalances We further demonstrated that soil microbiomes support a diverse array of taxonomic and genetic responses to elemental imbalances driven by global-scale changes in aridity. First, we explored the correlation between variations in C:N:P and water availability with soil microbial taxa. As expected, soil microbial taxa largely respond to water availability and element imbalances across environmental gradients. For example, the relative abundance of Chloroflexi is known to be greater under low water availability and C:P and N:P ratios (Fig. S5) 26 , 27 . Similarly, the relative abundance of Acidobacteria, Verrucomicrobia, and Rhizaria were positively correlated with water availability and C:P and N:P ratios (P < 0.05) (Fig. S5). Although microbial taxonomic responses to nutrient imbalances have been previously investigated at a local and regional scales, our study provides novel insights on their responses across global biomes. Moreover, much less was known about the responses of microbial functional genes to aridity-driven elemental imbalances. Here, we showed that functional microbial genes and metabolic pathways were associated with the soil elemental stoichiometry (Fig. 2 ). For example, functional genes and metabolic pathways annotated in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, along with carbohydrate hydrolases from the Carbohydrate-Active Enzymes (CAZy) database, accounted for up to 51% of the observed variation in soil C, N, and P content (Figs. S6 and S7). In addition, functional genes and metabolic pathways categorized as cellular processes, as well as genetic and environmental information processes (among others), collectively explained up to 24% of the observed variation in the soil C:N, N:P, and C:P ratios (Fig. 2 ). Specifically, the abundance of genes associated with transcription, protein synthesis and microbial growth increased in soils with lower C:N and C:P ratios, primarily because of the reduced SOC (Figs. S8 and S9). Regarding the soil C:P ratio, microbial metabolism exhibited a lower 28 , yet still significant explanatory power (2–5% of the variation), with environmental variables accounting for up to 50% (Figs. 1 c and 2 ). Collectively, our findings indicate that the community composition, diversity and metabolism of the soil microbiome are strongly correlated with aridity-driven soil elemental imbalances, particularly the deficiency of organic C. Soil elemental imbalances, particularly the relative scarcity of organic C compared with N and P, associated with shifting climatic conditions in areas with low water availability, were associated with changes in microbial metabolism from catabolic to anabolic processes. As the soil C:N, N:P, and C:P ratios decreased, we observed significant increases in genes and pathways related to anabolism, such as nucleotide replication, RNA transcription, and protein folding, sorting, and transport, under conditions of relative soil organic matter deficiency (characterized by reduced organic C and N content; P < 0.05) (Figs. S8 and S9). These findings suggest that arid environments, which lead to relative organic C deficiency, limit microbial investment in organic P mineralization and chitin degradation (as indicated by the gene clusters of phn and chi ). Conversely, this conditions accelerated inorganic P transports (as indicated by the pstA gene) 29 and N assimilation, including the synthesis of glutamic acid (as indicated by the glt gene cluster) (Fig. 3 ) 30 . This outcome is theoretically expected because microbial cellular processes, such as biomass production, require substantial amounts of N- and P-rich molecules including proteins and ATP 31 . Further analyses consistently revealed increased relative abundance of genes associated with the synthesis and transport of proteins (as indicated by secG , pcnB , and cysM ) under arid conditions with lower soil C:N ratios (P < 0.05; Figs. S8 and S10a). Consequently, phospholipid synthesis appears to be compromised compared with the synthesis of proteins and/or carbohydrates under these organic C-limited conditions (Figs. S8 and S9), as microorganisms require more ribosomal RNA to synthesize osmolytes, biofilms, and cell walls to withstand drought 32 . As such, genes encoding intracellular protein and polysaccharide synthesis (e.g., protein processing in the endoplasmic reticulum, phosphoglycerate synthase, and murein polymerase) also exhibited negative correlations with the soil C:P and/or N:P ratios (P < 0.05; Figs. S8 and S10b). Murein and phosphoglycerate are critical intermediates in bacterial cell wall synthesis 33 , 34 , whereas protein synthesis in the endoplasmic reticulum is essential for microbial survival in stressed environments 35 . Collectively, these results support our hypothesis and indicate that aridity-driven soil elemental imbalances shift microbial metabolism by activating anabolic processes, that are crucial for microbial resistance and survival in arid environments. Conversely, in wetter regions with higher organic C content (i.e., elevated soil C:P and C:N ratios), an increased abundance of genes associated with catabolism was observed. Higher organic C resources can stimulate soil microbiome activity, which in turn requires more N and P 36 . As N and P become less available, soil microbes must mine organic matter using extracellular enzymes to access the limiting nutrients 37 , 38 . We found that genes encoding extracellular enzymes that degrade recalcitrant soil organic matter, such as terpenoids, polyketides 39 , 40 , and arylesters 41 , were relatively increased (P < 0.05) in soils with relative N and P deficiencies (Figs. S8). For example, the abundance of genes associated with the degradation of aromatic compounds (e.g., aliA and tfdB ), and root exudates such as terpenoids (e.g., mlhB and chnC ) increased in soils with higher organic C (Figs. S11a, S11b and S12). Consistently, we observed an increased abundance of genes associated with signal transduction and membrane transport in soils with higher C:P ratios, which are essential for the production and secretion of extracellular enzymes (Figs. 2 a and 2 b) 7 . Microorganisms with well-developed synthesis and secretion systems for extracellular enzymes can survive better in nutrient deficient soils 42 because catabolically degrading enzymes release nutrients and energy for microbial uptake (Figs. 2 c) 43 . For example, in nutrient-stressed environments, such as those found in areas with high water availability 44 , the soil microbiome exhibits overflow metabolism of energy to mineralize organic matter 45 . As evidence, the relative abundance of phn gene clusters, which mediate the catabolism of organic P (i.e., phosphonates 46 ) increased in soils with high SOC content and elevated C:P and C:N ratios (Fig. 3 c). These results align with those of a study demonstrating microbiome-driven coupling of soil C and P in agroecosystems 47 . Furthermore, we observed positive correlations between the C:P ratio and the relative abundance of genes encoding gcd , which are involved in the production of organic acids and subsequent mineral P solubilization 48 . However, we did not find any correlation with the phosphatase gene ( phoD ) (Fig. 3 ). These results underscore the significance of P solubilization processes in addressing nutrient imbalances caused by climatic conditions, suggesting that P solubilization can be a potential response to P deficiency in comparison to mining organic matter at the global scale. Similarly, we found that nutrient deficiency associated with the increased abundance of genes regulating bacterial flagellar assembly (i.e., the flg gene cluster) that are related to motility (Fig. S11c and S12). Enhanced motility increases opportunities for microorganisms to access nutrient-rich environmental patches 49 . The promotion of extracellular enzyme production and motility requires significant energy derived from the consumption of organic C 50 . In this context, increases catabolism of organic matter degradation was observed (Figs. S11a, S11b and S12). Collectively, our findings support our hypothesis that enhanced catabolism at the expense of organic matter represents a potential strategy for the microbiome to cope with elevated C:N and C:P ratios in the environment. Previous studies have reported increased microbial catabolism in stressful environments 51 , whereas microbial catabolism tends to decrease in fertile soils, which are typically characterized by higher organic C content 52 . Intriguingly, in this study, we found that microbial catabolism is still elevated in fertile soils, albeit with elemental imbalances. Potential consequences of shifting from catabolism to anabolism Our study suggests that aridity-driven soil elemental imbalances may reshape microbial metabolic strategies, with possible cascading consequences on terrestrial ecosystem services. Specifically, elevated abundance of genes involved in anabolism (e.g., microbial growth) may enhance soil microbial resistance and survival under drought stress. Conversely, catabolic processes (e.g., extracellular enzyme production) are suppressed, resulting in diminished degradation of external organic matter and its transformation to soil organic C pools and stocks 20 . For instance, decreases in SOC content were accompanied by a reduction in the abundance of genes encoding the transport and synthesis of xylanase, alpha- glucosidase, and beta-glucosidase (P < 0.05; Fig. S9). These opposing effects can destabilize the C-climate feedback loop, particularly in C-limited ecosystems. However, the negative correlations between SOC contents and the abundance of genes encoding chitosanase (P < 0.05; Fig. S9) suggest that microbial anabolism can, to some extent, fuel long-term C stabilization through mineral-reactive compounds (e.g., cell wall residues and extracellular polymers 53 ). The adoption of anabolic processes as a survival strategy in response to aridity-driven elemental imbalances may compromise ecosystem stability. The resulting decline in the abundance of genes associated with anabolic processes is intrinsically linked to a reduced capacity to degrade and transform external organic matter into soil organic C pools and stocks 53 . This can lead to diminished soil aggregate formation and water retention, resulting in more erodible soils and nutrient loss. Furthermore, alterations in microbial metabolic processes can reshape the nutrient cycling in terrestrial ecosystems. For instance, studies have demonstrated that increased N availability through fertilization can enrich genes involved in catabolism 53 . To address resource imbalances, soil microbes may release a greater proportion of organic N as ammonium, thereby modifying N mineralization and terrestrial nutrient cycling 54 . An interesting question remains as to whether changes in soil structure and nutrient availability, driven by metabolic shifts, could affect soil microbial composition and diversity. It is postulated that microorganisms specializing in rapid resource degradation through catabolic genes (e.g., saprophytic fungi) may be favored over other microbial groups with higher energy demands, such as N-fixing bacteria, whose activity relies on energy-demanding anabolic processes. This finding supports those of previous studies, indicating shifts in the fungal:bacterial ratio relative to the stoichiometric ratios of soil elements 26 . If confirmed, it is essential to understand how soil food webs and their functional diversity change, as well as the implications for key ecosystem services when catabolism transitions to anabolism. Our study lays the groundwork for future research to validate these hypotheses and underscores the necessity of expanding sampling efforts across larger and more diverse regions to derive more robust and global patterns. Conclusion Taken together, our findings provide novel insights into global-scale responses of the soil microbiome to soil elemental imbalances caused by global-scale changes in aridity. As water availability decreases, ecosystems become less productive and support reduced input of soil organic C, whereas soil N and P become comparatively more available. These soil elemental imbalances were significantly associated with increases in the relative abundance of functional genes and metabolic pathways associated with microbial anabolism, as well as decreases in genes associated with catabolism, particularly those involved in the degradation of external organic matter, especially in recalcitrant forms (Fig. 4 ). The shift in microbial metabolism from catabolism to anabolism may further influence the capacity of soils to sequester C and provide nutrients, thereby compromising ecosystem services. These findings are crucial for anticipating the future of the soil microbiome under climate change and suggest that soil microbiomes possess a wide array of genetic tools to acclimate to aridification; thus, they prioritize drought resistance and survival over energy and nutrient acquisition. Methods Global field survey We collected topsoil samples (~ 0–10 cm) from 200 ecosystems across all continents to investigate the adaptation strategies of the soil microbiomes in response to climate-driven elemental imbalances. These sampling sites encompassed a variety of ecosystem types, including temperate seasonal forests, tropical seasonal forests, temperate grasslands, tundra grasslands, subtropical woodlands, and shrublands, providing a comprehensive representation of the terrestrial environmental conditions found on Earth (Fig. 1 a). The latitude, elevation, and slope of each sampling site were recorded in situ using a portable GPS unit. Vegetation types were classified for each location based on the dominant species. A portion of the soil samples was frozen for molecular analyses at -20°C, whereas the remaining samples were air-dried for biogeochemical analyses. Climate and ecosystem productivity The MAP, which ranges from 81 to 2,161 mm; mean annual temperature (MAT), ranging from − 3.5 to 24.8℃; seasonality of precipitation (PSEA); seasonality of temperature (TSEA); and the mean diurnal range of temperature (MDR) for the sample sites were obtained from interpolations provided by WorldClim ( http://www.worldclim.org/ ). We also measured NPP and plant cover as proxies for the functional traits of the vegetated ecosystems across all sampling sites. Perennial vegetation cover was measured in situ at each site using the line-intercept method. We used the normalized difference vegetation index (NDVI) as a proxy for NPP, which was obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra satellites ( http://daac.ornl.gov/index.shtml ) at a resolution of 250 m. We believe that the choice of productivity period will not affect our results, as we used average values derived from normalized difference vegetation index measurements taken during the month before, during, and after the sampling dates at each surveyed plot. Determination of soil C, N and P content The contents of each component of C, including total organic C, mineral-associated organic C, particulate organic C and total inorganic C; N, comprising total N and dissolved inorganic N; and P, which includes total P, primary P, occluded and non-occluded P, organic P, residual P, and available P, were measured. The total P content of the soil was determined using sodium hydroxide fusion method 55 . Particulate organic C and mineral-associated organic C were separated using a combined density and particle size fractionation method 56 . Inorganic (plant-available) N was measured using a continuous-flow stream autoanalyzer (SEAL-AA3, Norderstedt, Germany). Soil non-occluded P, occluded P, and organic P were quantified using the sequential extraction procedure developed by Hedley et al. in 1982 57 and modified by Guppy et al. in 2000 58 . Soil organic C and total N were analyzed using a combination of dichromate oxidation, the Kjeldahl method, and a Flash EA 1112 Series Elemental Analyzer connected via a Conflo III to a DeltaPlus XP isotope ratio mass spectrometer (all Thermo Finnigan, Bremen, Germany). In this study, the soil C:N, N:P and C:P ratios were calculated based on the ratios of soil organic C to total N, total N to total P, and organic C to total P, respectively. Soil microbial composition and diversity measures In this study, the diversities of soil bacteria, fungi, protists and invertebrates were measured using an Illumina MiSeq platform for amplicon sequencing. Soil DNA was extracted with the PowerSoil DNA Isolation Kit (MoBio Laboratories) following the manufacturer’s instructions. The 16S rRNA gene for bacteria and the 18S rRNA gene for eukaryotes were sequenced using the 341F/805R 59 and Euk1391f/EukBr 60 primer sets, respectively. The bioinformatic analyses were performed using a combination of QIIME 61 , USEARCH 62 and UNOISE3 63 . The sequences were clustered into soil phylo-types—zOTUs—at a 100% identity level. The representative sequences of zOTUs were annotated against the SILVA (16S rRNA gene) 64 as well as PR2 and SILVA (18S rRNA gene) 65 databases. Before analyses, the zOTU abundance tables were rarefied at 10,000 (for bacteria) and 4,000 (for eukaryotes) sequences per sample to ensure an even sampling depth within each microbial group. Protists were defined as eukaryotic taxa, except fungi, Metazoa (invertebrates and vertebrates) and vascular and non-vascular plants (Streptophyta). Fungal guilds were characterized using the FUNGuild 66 . In this study, we used species richness, which represents the number of phylotypes, as a measure of soil microbial diversity since it is widely used and straightforward. Metagenomic sequencing and bioinformatic analyses Metagenomic sequencing was performed at the Next Generation Sequencing (NGS) Facility (Western Sydney University, Australia) using the Illumina NovaSeq platform, which generated approximately 10 Gb of high-quality 150 bp paired-end reads per sample. Clean reads were obtained after quality trimming, using fastp (version 0.20.1) to remove low-quality reads. Metagenome assembly was conducted using Megahit (v1.2.9) 67 , retaining only contigs ≥ 1000 bp in length. Open reading frames of the assembled contigs were predicted using the Prodigal software (version 2.6.3). All open reading frames were compiled into a set of unique genes after clustering with CD-HIT at 95% sequence identity to create a non-redundant gene catalog 68 . Read counts and reads per million for each predicted non-redundant gene were calculated using Salmon (v1.10.3) 69 . To investigate how soil microbial metabolism respond to climate-driven elemental imbalances, the predicted genes were annotated against the eggNOG databases (v5.0) using eggnog-mapper (v2.1.12) 70 , which provided KEGG Orthology (KO), KEGG pathway, and CAZy family profiles 71 . Before the analysis, genes associated with human diseases and those related to plant and macro-organism systems were excluded. The details of the three levels of KEGG annotation classification are presented in Table S1 . The categories of KO-annotated genes involved in soil C degradation, as well as the N and P cycles, are detailed in Table S2. Statistical modeling Nonlinear regressions and threshold identification. In this study, we used the Akaike Information Criterion (AIC) in two steps to identify the most appropriate model for the relationship between soil C, N, and P content and environmental gradients in water availability and NPP 72 . The first step involved fitting both linear and nonlinear models, including quadratic models (the simplest form of nonlinear trends) and generalized additive models (GAMs) (which capture more complex trends through smoothing parameters). The lower AIC value for the nonlinear model suggests the existence of a threshold for water availability and NPP. In the second step, to investigate the threshold in each nonlinear regression, we defined thresholds as the points along the water availability and NPP gradients, where the soil C, N, and P contents changed abruptly (i.e., discontinuous thresholds or breakpoints) in relation to water availability and NPP (i.e., continuous thresholds). In the latter case, although an abrupt change in slope was observed, more subtle and continuous changes that may involve high-degree polynomials were also considered. These changes are typically better represented by the GAM, where the changes in slope do not correspond to a sudden breakpoint, but rather indicate a point of maximum curvature. In both scenarios, despite being continuous, it remains possible to identify thresholds, and these values are reported, as they may provide critical information for their management. Continuous thresholds can be effectively detected using segmented regression, which isa linear regression that modifies the slope at a threshold or through a more complex GAM regression. In contrast, discontinuous thresholds require an overall change in both intercept and slope. These can be accurately modeled using either step regression, which alters only the intercept at the threshold, or a combination of step and segmented regressions, termed segmented because they involve changes in both the intercept and slope at the threshold. The optimal model was selected based on AIC values. We performed 1,000 bootstrap samplings for each case to identify a set of 1,000 plausible thresholds from which we calculated the mean value as the final threshold value. These analyses were conducted using R packages chngpt and gam . Correlation analyses. Spearman’s correlation analysis was used to assess the relationships among individual variables along environmental gradients, including soil C, N, and P content, and the abundance of genes (modules) and their associated metabolic pathways. The strength and direction of these relationships are indicated by the correlation coefficients and P values. All P values were adjusted using the false discovery rate (FDR) 73 . Correlation analyses were conducted using the R package psych . Random forest analyses. To identify potential functional gene modules that respond to variations in soil C, N, and P content, a predictive model utilizing a random forest machine learning method was established with 500 trees and 1,000 permutations implemented using the R package, RandomForest 74 . Cross-validation was conducted using the rfcv function to select the most appropriate features. The rfPermute function was used to estimate the P value for the significance of each predictor included in the random forest prediction results, whereas the importance of each predictor was represented by the percentage of the IncMSE value. In this study, only well-classified genes that changed significantly (P < 0.05) with soil elemental contents as well as their ratios were represented (Table S1 ). In addition, the tested parameters were log10 transformed before the analyses to normalize the variance when necessary. All statistical analyses were performed using R, version 4.3.0. Declarations Acknowledgements Y.Z.F. and C.K.L. were supported by the National Key R&D Program (2024YFD1300800) and the National Natural Science Foundation of China (42177297 and 42407401). S.U. and B.B. were supported by the Taylor Family-Asia Foundation Endowed Chair in Ecology and Conservation Biology. T.G. and T.U.N. were supported by Slovenian Research and Innovation Agency projects J4-3098, J4-4547 and Research group P4-0107. M.B. is supported by a Ramón y Cajal Fellowship from the Spanish Ministry of Science (RYC2021-031797-I). G.Y.Z. acknowledges the supports from Ramón y Cajal grant (RYC2022-035226-I) and AYUDAS DE EXCELENCIA RYC-MAX 2023 project from Spanish National Research Council. S.C.H. was supported, in part, by US National Science Foundation grant EAR-2012878 during the writing of this manuscript. R.R. was supported by the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie IF-GF Actions (MICROBIOCLIM, GA nr. 101028323). B.K.S. and T.S-S. works are supported by Australian Research Council (DP230101448). M.D-B. and F.B. acknowledges support from the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-115813RA-I00 and PID2020‐114942RB‐I00 funded by MCIN/AEI/10.13039/501100011033. J.A.S. acknowledges the support of the grant RYC2023-044125-I funded by MICIU/AEI/10.13039/501100011033 and by ESF+. T.S-S and B.K.S are supported by Australian Research Council (DP230101448). Author contributions Conceptualization: M.D.-B., B.K.S., F.T.M, and Y.F.; Investigation: C.L., Y.F., T.S.S., C.X., D.E., N.G., Y.L.B.P., V.O., B.G., E.G., M.G.G., E.V., M.B., S.A., J.M.-V., B.J.M., A.A.B., N.A.C., S.A., J.A., F.A., A.I.A., M.B., F.B., N.B., B.B., M.B., C.B., S.C.H, B.D., J.D., C.I.E., A.F., L.F., A.G., L.G.V., K.G., T.G., E.G.M., L.K., M.K., L.L., P.C.l.R., P.L., J.L., A.L., M.A.L., P.M., G.M.-K., T.M., A.M., E.M., D.M., J.P.M., G.M., M.M.-R., A.M., T.U.N., G.N., S.N., C.P., Y.P., P.J.R., A.R., M.A.R.M., A.R., B.R.L., R.R., J.R., A.S., J.S., J.A.S., J.S., S.T., S.U., O.V., M.V., L.W., M.W., E.Z., G.Z., F.T.M., B.K.S., M.D.-B.; Methodology and visualization: C.L., C.X., Y.F. and M.D.-B.; Writing-review & editing: Y.F., C.L., M.D.-B., B.K.S., T.S.S, F.T.M. and C.X. with contributions of all authors. Conflict of interest The authors declare no competing interests. Data availability All raw sequencing data used in this study have been submitted to the NCBI Sequence Read Archive (SRA) database under the accession numbers PRJNA1162941 (shotgun metagenomics), PRJNA1249113 (16S rRNA gene amplicon), and PRJNA1249114 (18S rRNA gene amplicon). All the materials and protocols used in the article are available upon request and without restriction. Code availability All R codes supporting the conclusions of this article are included in figshare at https://doi.org/10.6084/m9.figshare.28829420. References Alewell, C. et al. Global phosphorus shortage will be aggravated by soil erosion. Nature Communications 11 , 4546, doi:10.1038/s41467-020-18326-7 (2020). Delgado-Baquerizo, M. et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature 502 , 672-676, doi:10.1038/nature12670 (2013). Stevenson, F. J. & Cole, M. A. Cycles of soils: carbon, nitrogen, phosphorus, sulfur, micronutrients . (John Wiley & Sons, 1999). Ewing, S. A. et al. Rainfall limit of the N cycle on Earth. Global Biogeochemical Cycles 21 , GB3009, doi:10.1029/2006gb002838 (2007). Lajtha, K. & Schlesinger, W. H. The Biogeochemistry of Phosphorus Cycling and Phosphorus Availability Along a Desert Soil Chronosequence. Ecology 69 , 24-39, doi:10.2307/1943157 (1988). Bi, B. et al. Enhanced rock weathering increased soil phosphorus availability and altered root phosphorus-acquisition strategies. Global Change Biology 30 , e17310, doi:10.1111/gcb.17310 (2024). Sinsabaugh, R. L. & Follstad Shah, J. J. Ecoenzymatic Stoichiometry and Ecological Theory. Annual Review of Ecology, Evolution, and Systematics 43 , 313-343, doi:10.1146/annurev-ecolsys-071112-124414 (2012). Banerjee, S. & van der Heijden, M. G. A. Soil microbiomes and one health. Nature Reviews Microbiology 21 , 6-20, doi:10.1038/s41579-022-00779-w (2022). Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528 , 69-76, doi:10.1038/nature15744 (2015). Sinsabaugh, R. L., Manzoni, S., Moorhead, D. L. & Richter, A. Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecology Letters 16 , 930-939, doi:10.1111/ele.12113 (2013). Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560 , 233-237, doi:10.1038/s41586-018-0386-6 (2018). Sinsabaugh, R. L., Hill, B. H. & Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462 , 795-798, doi:10.1038/nature08632 (2009). Larsson, C., von Stockar, U., Marison, I. & Gustafsson, L. Metabolic uncoupling in Saccharomyces cerevisiae. Thermochimica Acta 251 , 99-110, doi:10.1016/0040-6031(94)02055-S (1995). Russell, J. B. & Cook, G. M. Energetics of bacterial growth: Balance of anabolic and catabolic reactions. Microbiological Reviews 59 , 48-62, doi:10.1128/mr.59.1.48-62.1995 (1995). Wang, H. et al. Metagenomic insights into inhibition of soil microbial carbon metabolism by phosphorus limitation during vegetation succession. ISME Communications 4 , doi:10.1093/ismeco/ycae128 (2024). Rappuoli, R. et al. Microbes could help address climate change - why aren't we using them? Nature 639 , 864-866, doi:d41586-025-00875-w (2025). Ranheim Sveen, T., Hannula, S. E. & Bahram, M. Microbial regulation of feedbacks to ecosystem change. Trends in Microbiology 32 , 68-78, doi:10.1016/j.tim.2023.06.006 (2024). Zhang, Z. et al. Adaptations of soil microbes to stoichiometric imbalances in regulating their carbon use efficiency under a range of different grazing intensities. Applied Soil Ecology 193 , 105141, doi:https://doi.org/10.1016/j.apsoil.2023.105141 (2024). Slessarev, E. et al. Water balance creates a threshold in soil pH at the global scale. Nature 540 , 567-569, doi:doi.org/10.1038/nature20139 (2016). Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nature Microbiology 2 , 17105, doi:10.1038/nmicrobiol.2017.105 (2017). Cotrufo, M. F. et al. Formation of soil organic matter via biochemical and physical pathways of litter mass loss. Nature Geoscience 8 , 776-779, doi:10.1038/ngeo2520 (2015). Wang, C. et al. Aridity threshold in controlling ecosystem nitrogen cycling in arid and semi-arid grasslands. Nature Communications 5 , 4799, doi:10.1038/ncomms5799 (2014). Lei, S. et al. Effect of aridity on the β-diversity of alpine soil potential diazotrophs: insights into community assembly and co-occurrence patterns. mSystems 9 , e01042-01023, doi:10.1128/msystems.01042-23 (2023). Lambers, H. Phosphorus Acquisition and Utilization in Plants. Annual Review of Plant Biology 73 , 17-42, doi:10.1146/annurev-arplant-102720-125738 (2022). Chen, S. et al. Plant diversity enhances productivity and soil carbon storage. Proceedings of the National Academy of Sciences of the United States of America 115 , 4027-4032, doi:10.1073/pnas.1700298114 (2018). Delgado-Baquerizo, M. et al. It is elemental: soil nutrient stoichiometry drives bacterial diversity. Environmental Microbiology 19 , 1176-1188, doi:10.1111/1462-2920.13642 (2017). Maestre, F. T. et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proceedings of the National Academy of Sciences of the United States of America 112 , 15684-15689, doi:10.6084/ (2015). Mooshammer, M. et al. Decoupling of microbial carbon, nitrogen, and phosphorus cycling in response to extreme temperature events. Science Advances 3 , e1602781, doi:10.1126/sciadv.1602781 (2017). Gillooly, J. F. et al. The metabolic basis of whole-organism RNA and phosphorus content. Proc Natl Acad Sci U S A 102 , 11923-11927, doi:10.1073/pnas.0504756102 (2005). Luo, Z. et al. Microbial synthesis of poly-γ-glutamic acid: current progress, challenges, and future perspectives. Biotechnology for Biofuels 9 , doi:10.1186/s13068-016-0537-7 (2016). Isanta‐Navarro, J. et al. Revisiting the growth rate hypothesis: Towards a holistic stoichiometric understanding of growth. Ecology Letters 25 , 2324-2339, doi:10.1111/ele.14096 (2022). Allison, S. D. Microbial drought resistance may destabilize soil carbon. Trends Microbiol 31 , 780-787, doi:10.1016/j.tim.2023.03.002 (2023). Rohs, P. D. A. & Bernhardt, T. G. Growth and Division of the Peptidoglycan Matrix. Annual Review of Microbiology 75 , 315-336, doi:10.1146/annurev-micro-020518-120056 (2021). Song, H. et al. Mycobacterium tuberculosis Glucosyl-3-Phosphoglycerate Synthase: Structure of a Key Enzyme in Methylglucose Lipopolysaccharide Biosynthesis. PLoS ONE 3 , e3748, doi:10.1371/journal.pone.0003748 (2008). Roy, C. R., Salcedo, S. P. & Gorvel, J.-P. E. Pathogen–endoplasmic-reticulum interactions: in through the out door. Nature Reviews Immunology 6 , 136-147, doi:10.1038/nri1775 (2006). Kuzyakov, Y. & Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytologist 198 , 656-669, doi:10.1111/nph.12235 (2013). Barquero, M. B. et al. Contrasting fertilization and phenological stages shape microbial-mediated phosphorus cycling in a maize agroecosystem. Science of The Total Environment 951 , 175571, doi:10.1016/j.scitotenv.2024.175571 (2024). Siles, J. A. et al. Distribution of phosphorus cycling genes across land uses and microbial taxonomic groups based on metagenome and genome mining. Soil Biology and Biochemistry 174 , 108826, doi:10.1016/j.soilbio.2022.108826 (2022). Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. & Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. Nature Geoscience 12 , 989-994, doi:10.1038/s41561-019-0484-6 (2019). Salem, H. M., Abdel-Salam, A., Abdel-Salam, M. A. & Seleiman, M. F. in Xenobiotics in the Soil Environment: Monitoring, Toxicity and Management (eds Muhammad Zaffar Hashmi, Vivek Kumar, & Ajit Varma) 267-280 (Springer International Publishing, 2017). Wang, L. et al. Mining bacterial genomes for novel arylesterase activity. Microbial Biotechnology 3 , 677-690, doi:10.1111/j.1751-7915.2010.00185.x (2010). Wang, C. et al. Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient. Nature Communications 14 , 7437, doi:10.1038/s41467-023-43297-w (2023). Dong, Y. et al. Eco-evolutionary strategies for relieving carbon limitation under salt stress differ across microbial clades. Nature Communications 15 , 6013, doi:10.1038/s41467-024-50368-z (2024). Sinsabaugh, R. L. et al. Stoichiometry of soil enzyme activity at global scale. Ecology Letters 11 , 1252-1264, doi:10.1111/j.1461-0248.2008.01245.x (2008). Basan, M. et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528 , 99-104, doi:10.1038/nature15765 (2015). Stosiek, N., Talma, M. & Klimek-Ochab, M. Carbon-Phosphorus Lyase—the State of the Art. Applied Biochemistry and Biotechnology 190 , 1525-1552, doi:10.1007/s12010-019-03161-4 (2020). Jing, Z. et al. Response and feedback of C mineralization to P availability driven by soil microorganisms. Soil Biology and Biochemistry 105 , 111-120, doi:10.1016/j.soilbio.2016.11.014 (2017). Mendes, G. d. O. et al. Chemical and Physical Mechanisms of Fungal Bioweathering of Rock Phosphate. Geomicrobiology Journal 38 , 384-394, doi:10.1080/01490451.2020.1863525 (2021). Gude, S. et al. Bacterial coexistence driven by motility and spatial competition. Nature 578 , 588-592, doi:10.1038/s41586-020-2033-2 (2020). Keegstra, J. M., Carrara, F. & Stocker, R. The ecological roles of bacterial chemotaxis. Nature Reviews Microbiology 20 , 491-504, doi:10.1038/s41579-022-00709-w (2022). Piton, G. et al. Life history strategies of soil bacterial communities across global terrestrial biomes. Nature Microbiology 8 , 2093-2102, doi:10.1038/s41564-023-01465-0 (2023). Butler, O. M., Manzoni, S. & Warren, C. R. Community composition and physiological plasticity control microbial carbon storage across natural and experimental soil fertility gradients. The ISME Journal 17 , 2259-2269, doi:10.1038/s41396-023-01527-5 (2023). Qin, S., Zhang, D., Wei, B. & Yang, Y. Dual roles of microbes in mediating soil carbon dynamics in response to warming. Nature Communications 15 , doi:10.1038/s41467-024-50800-4 (2024). Mooshammer, M. et al. Adjustment of microbial nitrogen use efficiency to carbon:nitrogen imbalances regulates soil nitrogen cycling. Nat Commun 5 , 3694, doi:10.1038/ncomms4694 (2014). Kowalenko, C. G. & and Babuin, D. Use of Lithium Metaborate to Determine Total Phosphorus and Other Element Concentrations in Soil, Plant, and Related Materials. Communications in Soil Science and Plant Analysis 45 , 15-28, doi:10.1080/00103624.2013.848884 (2014). Witzgall, K. et al. Particulate organic matter as a functional soil component for persistent soil organic carbon. Nature Communications 12 , 4115, doi:10.1038/s41467-021-24192-8 (2021). Hedley, M. J., Stewart, J. W. B. & Chauhan, B. S. Changes in inorganic and organic soil phosphorus fractions induced by cultivation practices and by laboratory incubation. Soil Science Society Of America Journal 46 , 970–975, doi:10.2136/sssaj1982.03615995004600050017x (1982). Guppy, C. N., Menzies, N. W., Moody, P. W., Compton, B. L. & Blamey, F. P. C. A simplified, sequential phosphorus fractionation method. Communications in Soil Science and Plant Analysis 31 , 1981–1991, doi:10.1080/00103620009370556 (2000). Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci U S A 109 , 21390-21395 (2012). Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City's Central Park are similar to those observed globally. Proc Biol Sci 281 , doi:10.1098/rspb.2014.1988 (2014). Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7 , 335-336, doi:10.1038/nmeth.f.303 (2010). Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26 , 2460-2461, doi:10.1093/bioinformatics/btq461 (2010). Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10 , 996-998, doi:10.1038/nmeth.2604 (2013). Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41 , D590-596, doi:10.1093/nar/gks1219 (2013). Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res 41 , D597-604, doi:10.1093/nar/gks1160 (2013). Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20 , 241-248, doi:10.1016/j.funeco.2015.06.006 (2016). Li, D., Liu, C., Luo, R., Sadakane, K. & Lam, T. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31 , 1674–1676, doi:10.1093/bioinformatics/btv033 (2015). Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28 , 3150–3152, doi:10.1093/bioinformatics/bts565 (2012). Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14 , 417-419, doi:10.1038/nmeth.4197 (2017). Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47 , D309-d314, doi:10.1093/nar/gky1085 (2019). Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45 , D353-d361, doi:10.1093/nar/gkw1092 (2017). Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367 , 787-790, doi:10.1126/science.aay5958 (2020). Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. The Annuals of Statistics 29 , 1165-1188, doi:http://www.jstor.org/stable/2674075 (2001). Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2 , 18-22, doi:http://www.stat.berkeley.edu/users/breiman/ (2002). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.pdf Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6656511","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456494392,"identity":"980dd713-8f10-44d4-8ed6-80f3bb14f651","order_by":0,"name":"Youzhi Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBAC9gYgwdggIWcA5hpYENbCcwCixdiAgRmkRYJoLQyJG8BaGIjRIpF8TPLnDov07ez9Rzf8KJBg4G/vTiCgJS1NQvKMRO7OnsNsN3uADpM4c3YDXi32EjlmEoZtErkbbiSz3eABajEAsgnYAtSS2CaRbgDUcvMP0VoOtkkkgLTcJs4WnmfJlo1tEoYbzhw2uy1jIMFD0C887MkHb/5sq5M3ON747OabPzZy/O29+LUwCCSgmYFfOQjwHyCsZhSMglEwCkY4AACK7ELgH8IFPQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8519-841X","institution":"College of Chemical Engineering, Nanjing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Youzhi","middleName":"","lastName":"Feng","suffix":""},{"id":456494393,"identity":"31232d1c-5512-4177-b3ef-46d206f563bf","order_by":1,"name":"Chunkai Li","email":"","orcid":"","institution":"College of Chemical Engineering, Nanjing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Chunkai","middleName":"","lastName":"Li","suffix":""},{"id":456494394,"identity":"a42ca6cf-2367-4cf7-9502-ab77214a06f5","order_by":2,"name":"Tadeo Sáez Sandino","email":"","orcid":"https://orcid.org/0000-0001-9539-4716","institution":"Western Sydney University","correspondingAuthor":false,"prefix":"","firstName":"Tadeo","middleName":"Sáez","lastName":"Sandino","suffix":""},{"id":456494395,"identity":"67e4375a-a7be-4ebf-9614-902d75e8376f","order_by":3,"name":"Chao Xiong","email":"","orcid":"","institution":"Hawkesbury Institute for the Environment, Western Sydney University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Xiong","suffix":""},{"id":456494396,"identity":"5bd9f044-60c0-41a5-87f5-ba817f7a8879","order_by":4,"name":"David Eldridge","email":"","orcid":"https://orcid.org/0000-0002-2191-486X","institution":"University of NSW Sydney","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Eldridge","suffix":""},{"id":456494397,"identity":"07e3069f-1bd4-4015-a5b9-98abea1a82f9","order_by":5,"name":"Nicolas Gross","email":"","orcid":"https://orcid.org/0000-0001-9730-3240","institution":"INRAE","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Gross","suffix":""},{"id":456494398,"identity":"47046946-8b71-4a0a-80f8-03f346b9e40c","order_by":6,"name":"Yoann Le Bagousse-Pinguet","email":"","orcid":"https://orcid.org/0000-0002-5615-5541","institution":"Aix Marseille Univ, CNRS, Avignon Université, IRD","correspondingAuthor":false,"prefix":"","firstName":"Yoann","middleName":"Le","lastName":"Bagousse-Pinguet","suffix":""},{"id":456494399,"identity":"40b91a2c-5877-4eaa-9669-752c4bc8c801","order_by":7,"name":"Victoria Ochoa","email":"","orcid":"https://orcid.org/0000-0002-2055-2094","institution":"Centro Superior de Investigaciones Científicas","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Ochoa","suffix":""},{"id":456494400,"identity":"f6bd7c11-d447-4b48-8ed3-cd28c759a486","order_by":8,"name":"Beatriz Gozalo","email":"","orcid":"","institution":"Universidad Rey Juan Carlos","correspondingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"","lastName":"Gozalo","suffix":""},{"id":456494401,"identity":"c7eb5c8c-7ff1-4e7c-86bd-9f661bd6adaa","order_by":9,"name":"Emilio Guirado","email":"","orcid":"https://orcid.org/0000-0001-5348-7391","institution":"Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante","correspondingAuthor":false,"prefix":"","firstName":"Emilio","middleName":"","lastName":"Guirado","suffix":""},{"id":456494402,"identity":"d5f4673b-6fad-4da6-8a2f-b79d711ca17d","order_by":10,"name":"Miguel García-Gómez","email":"","orcid":"","institution":"Universidad Politécnica de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"García-Gómez","suffix":""},{"id":456494403,"identity":"ba0e3908-9c2c-4727-ac87-764775e2384a","order_by":11,"name":"Enrique Valencia","email":"","orcid":"https://orcid.org/0000-0003-3359-0759","institution":"Universidad Complutense de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Enrique","middleName":"","lastName":"Valencia","suffix":""},{"id":456494404,"identity":"3a2f13d5-1fc9-4ac1-899f-2a9d92be869c","order_by":12,"name":"Miguel Berdugo","email":"","orcid":"https://orcid.org/0000-0003-1053-8907","institution":"Complutense University","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Berdugo","suffix":""},{"id":456494405,"identity":"5c0cd1b6-9750-4b6b-b28e-04a7e0c5476b","order_by":13,"name":"Sergio Asensio","email":"","orcid":"https://orcid.org/0000-0003-4376-2964","institution":"University of Alicante","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Asensio","suffix":""},{"id":456494406,"identity":"faababf4-7f49-438b-a22e-bff6e6355221","order_by":14,"name":"Jaime Martínez-Valderrama","email":"","orcid":"https://orcid.org/0000-0001-5859-5674","institution":"Universidad de Alicante","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Martínez-Valderrama","suffix":""},{"id":456494407,"identity":"f3c3b793-a13f-41e8-9219-7480b44c8f82","order_by":15,"name":"Betty Mendoza","email":"","orcid":"","institution":"Universidad Rey Juan Carlos","correspondingAuthor":false,"prefix":"","firstName":"Betty","middleName":"","lastName":"Mendoza","suffix":""},{"id":456494408,"identity":"4f9ec275-7945-4244-a213-703d81244eff","order_by":16,"name":"Asmeret Berhe","email":"","orcid":"","institution":"Ted \u0026 Jan Falasco Chair in Earth Sciences and Geology, Department of Life and Environmental Sciences, University of California","correspondingAuthor":false,"prefix":"","firstName":"Asmeret","middleName":"","lastName":"Berhe","suffix":""},{"id":456494409,"identity":"df337b0a-a4fc-41e9-8749-4330356f4fac","order_by":17,"name":"Nick Cutler","email":"","orcid":"https://orcid.org/0000-0003-1746-7769","institution":"School of Geography, Politics and Sociology, Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Nick","middleName":"","lastName":"Cutler","suffix":""},{"id":456494410,"identity":"faa0055e-c900-4962-b43f-79404701847c","order_by":18,"name":"Sebastian Abades","email":"","orcid":"","institution":"GEMA Center for Genomics, Ecology \u0026 Environment. Universidad Mayor","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Abades","suffix":""},{"id":456494411,"identity":"65339b46-cae2-4e87-9235-7d19c90f96cf","order_by":19,"name":"Julio M. Alcántara","email":"","orcid":"https://orcid.org/0000-0002-8003-7844","institution":"Universidad de Jaén","correspondingAuthor":false,"prefix":"","firstName":"Julio","middleName":"M.","lastName":"Alcántara","suffix":""},{"id":456494412,"identity":"7517df99-ce5a-4918-9c0e-cc78f0112ea5","order_by":20,"name":"Fernando Alfaro","email":"","orcid":"","institution":"Universidad Mayor","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Alfaro","suffix":""},{"id":456494413,"identity":"75822fd0-d01f-4146-b0b1-3072fefede1c","order_by":21,"name":"Antonio I. Arroyo","email":"","orcid":"","institution":"Instituto Pirenaico de Ecología (IPE), CSIC","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"I.","lastName":"Arroyo","suffix":""},{"id":456494414,"identity":"8428246e-c413-4e9f-baf6-0763d4927c7f","order_by":22,"name":"Matt Barrett","email":"","orcid":"","institution":"Australian Tropical Herbarium - College of Science and Engineering, James Cook University - Cairns Campus","correspondingAuthor":false,"prefix":"","firstName":"Matt","middleName":"","lastName":"Barrett","suffix":""},{"id":456494415,"identity":"a221957f-409c-4a1b-b5a2-ccbf41e4e71d","order_by":23,"name":"Felipe Bastida","email":"","orcid":"https://orcid.org/0000-0001-9958-7099","institution":"CSIC","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"","lastName":"Bastida","suffix":""},{"id":456494416,"identity":"74e8d3ed-7443-4568-a69f-d820374932b8","order_by":24,"name":"Niels Blaum","email":"","orcid":"https://orcid.org/0000-0001-6807-5162","institution":"University of Potsdam","correspondingAuthor":false,"prefix":"","firstName":"Niels","middleName":"","lastName":"Blaum","suffix":""},{"id":456494417,"identity":"97abaae2-b8da-4a23-b7df-8086a180d31e","order_by":25,"name":"Bazartseren Boldgiv","email":"","orcid":"https://orcid.org/0000-0003-0015-8142","institution":"National University of Mongolia","correspondingAuthor":false,"prefix":"","firstName":"Bazartseren","middleName":"","lastName":"Boldgiv","suffix":""},{"id":456494418,"identity":"6ea5d559-a029-4d70-880b-0b8a7d1048cb","order_by":26,"name":"Matthew Bowker","email":"","orcid":"","institution":"Northern Arizona University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Bowker","suffix":""},{"id":456494419,"identity":"54bb8a7e-7a7f-4777-8226-91afa27adfa0","order_by":27,"name":"Cristina Branquinho","email":"","orcid":"","institution":"University of Lisbon","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Branquinho","suffix":""},{"id":456494420,"identity":"aafb5422-3821-4bcd-b3c3-94ef80ce5410","order_by":28,"name":"Stephen Hart","email":"","orcid":"https://orcid.org/0000-0002-9023-6943","institution":"University of California-Merced","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Hart","suffix":""},{"id":456494421,"identity":"43f7faed-50ac-4b53-9af1-04232180e19f","order_by":29,"name":"Balázs Deák","email":"","orcid":"https://orcid.org/0000-0001-6938-1997","institution":"Institute of Ecology and Botany, Centre for Ecological Research","correspondingAuthor":false,"prefix":"","firstName":"Balázs","middleName":"","lastName":"Deák","suffix":""},{"id":456494422,"identity":"83b7c7fa-0703-4251-b20f-7bbba74cf650","order_by":30,"name":"Jorge Durán","email":"","orcid":"https://orcid.org/0000-0002-7375-5290","institution":"Misión Biológica de Galicia - CSIC","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Durán","suffix":""},{"id":456494423,"identity":"cc54c5f6-91ab-4fdc-8fde-913740924b1b","order_by":31,"name":"Carlos Espinosa","email":"","orcid":"https://orcid.org/0000-0002-5330-4505","institution":"Universidad Técnica Particular de Loja","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Espinosa","suffix":""},{"id":456494424,"identity":"4a802d82-59b8-496b-ae8a-1d6c79ffe037","order_by":32,"name":"Alex Fajardo","email":"","orcid":"https://orcid.org/0000-0002-2202-6207","institution":"Centro de Investigación en Ecosistemas de la Patagonia (CIEP) / Universidad Austral de Chile","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Fajardo","suffix":""},{"id":456494425,"identity":"a1919772-eefd-4920-aa06-d0df95f77444","order_by":33,"name":"Lauchlan Fraser","email":"","orcid":"https://orcid.org/0000-0003-3998-5540","institution":"Department of Natural Resource Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lauchlan","middleName":"","lastName":"Fraser","suffix":""},{"id":456494426,"identity":"cbd202a4-eca9-434a-8987-266a8b183a69","order_by":34,"name":"Antonio Gallardo","email":"","orcid":"","institution":"Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Gallardo","suffix":""},{"id":456494427,"identity":"53e5646d-5680-426b-a5eb-a44d843c60c4","order_by":35,"name":"Laura García-Velázquez","email":"","orcid":"","institution":"Universidad Pablo de Olavide","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"García-Velázquez","suffix":""},{"id":456494428,"identity":"7caf3ed4-ed03-42ea-a7a5-537b38ddf0f4","order_by":36,"name":"Katja Geissler","email":"","orcid":"","institution":"University of Potsdam, Plan Ecology and Nature Conservation","correspondingAuthor":false,"prefix":"","firstName":"Katja","middleName":"","lastName":"Geissler","suffix":""},{"id":456494429,"identity":"96c46a32-c486-4fe2-bdd3-849b293759b3","order_by":37,"name":"Tine Grebenc","email":"","orcid":"https://orcid.org/0000-0003-4035-8587","institution":"Slovenian Forestry Institute","correspondingAuthor":false,"prefix":"","firstName":"Tine","middleName":"","lastName":"Grebenc","suffix":""},{"id":456494430,"identity":"1d84ef3e-931b-4727-8cfe-5b2626d9fe7e","order_by":38,"name":"Elizabeth Gusmán Montalván","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"Gusmán","lastName":"Montalván","suffix":""},{"id":456494431,"identity":"2655a323-8141-4652-9229-b878319db943","order_by":39,"name":"Liana Kindermann","email":"","orcid":"https://orcid.org/0000-0002-8126-2576","institution":"University of Potsdam","correspondingAuthor":false,"prefix":"","firstName":"Liana","middleName":"","lastName":"Kindermann","suffix":""},{"id":456494432,"identity":"2b2678f9-8ba6-4c59-8f03-cb61480ffce7","order_by":40,"name":"Melanie Köbel","email":"","orcid":"","institution":"cE3c - Centre for Ecology, Evolution and Environmental Changes \u0026 CHANGE – Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Köbel","suffix":""},{"id":456494433,"identity":"6d1d1af9-dfeb-4fd0-a256-7afdd6f3d115","order_by":41,"name":"Lauri Laanisto","email":"","orcid":"https://orcid.org/0000-0003-2215-7298","institution":"Estonian University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lauri","middleName":"","lastName":"Laanisto","suffix":""},{"id":456494434,"identity":"85ae2757-7e42-495b-bb54-7df0b01f44bf","order_by":42,"name":"Peter le Roux","email":"","orcid":"https://orcid.org/0000-0002-7941-7444","institution":"University of Pretoria","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"le","lastName":"Roux","suffix":""},{"id":456494435,"identity":"594c891a-5dd0-4d2a-ba4f-4279d3ca8873","order_by":43,"name":"Pierre Liancourt","email":"","orcid":"","institution":"Institute Botany Department. State Museum of Natural History Stuttgart","correspondingAuthor":false,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Liancourt","suffix":""},{"id":456494436,"identity":"a07d1951-7363-4f08-90c8-b72081296cf8","order_by":44,"name":"Jinsong Liang","email":"","orcid":"","institution":"School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China","correspondingAuthor":false,"prefix":"","firstName":"Jinsong","middleName":"","lastName":"Liang","suffix":""},{"id":456494437,"identity":"2ad086c4-6f48-4b01-9ac9-29a240bad994","order_by":45,"name":"Anja Linstädter","email":"","orcid":"","institution":"University of Potsdam","correspondingAuthor":false,"prefix":"","firstName":"Anja","middleName":"","lastName":"Linstädter","suffix":""},{"id":456494438,"identity":"c11f35fa-ab10-4d91-a4f8-ab199f5962a4","order_by":46,"name":"Michelle A. Louw","email":"","orcid":"","institution":"Department of Plant and Soil Sciences, University of Pretoria, Pretoria","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"A.","lastName":"Louw","suffix":""},{"id":456494439,"identity":"b72969c1-3dd3-4e13-a5e9-acd671831638","order_by":47,"name":"Petr Macek","email":"","orcid":"https://orcid.org/0000-0002-4792-9461","institution":"Biology Centre of Czech Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Petr","middleName":"","lastName":"Macek","suffix":""},{"id":456494440,"identity":"de49170f-640f-463b-ba64-828571391aa5","order_by":48,"name":"Gillian Maggs-Kölling","email":"","orcid":"","institution":"Gobabeb–Namib Research Institute, Walvis Bay","correspondingAuthor":false,"prefix":"","firstName":"Gillian","middleName":"","lastName":"Maggs-Kölling","suffix":""},{"id":456494441,"identity":"f37bc8ba-c6c2-440d-9c43-f549fc2ff44e","order_by":49,"name":"Thulani Makhalanyane","email":"","orcid":"https://orcid.org/0000-0002-8173-1678","institution":"School for Data Science and Computational Thinking, Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Thulani","middleName":"","lastName":"Makhalanyane","suffix":""},{"id":456494442,"identity":"398deca8-7fb0-44d4-b71b-fa0fccbf836f","order_by":50,"name":"Antonio Manzaneda","email":"","orcid":"","institution":"University of Jaen","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Manzaneda","suffix":""},{"id":456494443,"identity":"31c4f95f-1db6-4c3d-9b08-825146d83051","order_by":51,"name":"Eugene Marais","email":"","orcid":"https://orcid.org/0000-0001-7155-9942","institution":"Gobabeb-Namib Research Institute, Walvis Bay","correspondingAuthor":false,"prefix":"","firstName":"Eugene","middleName":"","lastName":"Marais","suffix":""},{"id":456494444,"identity":"261e1142-cd7c-42c7-b5f6-1fbbe0b3fd11","order_by":52,"name":"Daniel Montesinos","email":"","orcid":"https://orcid.org/0000-0003-2893-0878","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Montesinos","suffix":""},{"id":456494445,"identity":"16832b78-a86c-4894-8789-bad88e5bfa76","order_by":53,"name":"Juan P Mora","email":"","orcid":"","institution":"Doctoral Program in Sciences mention in Plant Biology and Biotechnology, Institute of Biological Sciences, Universidad de Talca, Talca","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"P","lastName":"Mora","suffix":""},{"id":456494446,"identity":"701e59ff-6a2b-4442-813b-0df3d3f4325a","order_by":54,"name":"Gerardo Moreno","email":"","orcid":"https://orcid.org/0000-0001-8053-2696","institution":"Forestry School University of Extremadura","correspondingAuthor":false,"prefix":"","firstName":"Gerardo","middleName":"","lastName":"Moreno","suffix":""},{"id":456494447,"identity":"d63c7c66-9cec-4d2a-949c-98a314ae2f72","order_by":55,"name":"Miriam Muñoz-Rojas","email":"","orcid":"","institution":"Laboratorio de Biodiversidad y Funcionamiento Ecosistémico. Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Av. Reina Mercedes 10, E-41012, Sevilla, Spain","correspondingAuthor":false,"prefix":"","firstName":"Miriam","middleName":"","lastName":"Muñoz-Rojas","suffix":""},{"id":456494448,"identity":"34ec9a48-773f-4b02-a4eb-95ad3221f0f4","order_by":56,"name":"Amir Mussery","email":"","orcid":"","institution":"Agricultural Research Organization, Ministry of Agriculture, Israel","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Mussery","suffix":""},{"id":456494449,"identity":"69882b95-d27f-4217-aae4-c513cd99e4f4","order_by":57,"name":"Tina Unuk Nahberger","email":"","orcid":"https://orcid.org/0000-0001-9808-1643","institution":"Department of Forest Physiology and Genetics. Slovenian Forestry Institute. Ljubljana, Slovenia.","correspondingAuthor":false,"prefix":"","firstName":"Tina","middleName":"Unuk","lastName":"Nahberger","suffix":""},{"id":456494450,"identity":"145fff3f-b86c-4b20-ad7c-d7aef257d4bd","order_by":58,"name":"Girish Nair","email":"","orcid":"","institution":"Department of Microbiology, Faculty of Science, Stellenbosch University, Stellenbosch","correspondingAuthor":false,"prefix":"","firstName":"Girish","middleName":"","lastName":"Nair","suffix":""},{"id":456494451,"identity":"04beccf4-734a-4072-a7de-b33c7deb4c5c","order_by":59,"name":"Sigrid Neuhauser","email":"","orcid":"https://orcid.org/0000-0003-0305-1615","institution":"University of Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Sigrid","middleName":"","lastName":"Neuhauser","suffix":""},{"id":456494452,"identity":"052371a3-3a19-43f6-a4c8-5377d1b38931","order_by":60,"name":"César Plaza","email":"","orcid":"https://orcid.org/0000-0001-8616-7001","institution":"Consejo Superior de Investigaciones Científicas","correspondingAuthor":false,"prefix":"","firstName":"César","middleName":"","lastName":"Plaza","suffix":""},{"id":456494453,"identity":"2a104ee3-ce9b-41a5-8e75-bdd71e6948b5","order_by":61,"name":"Yolanda Pueyo","email":"","orcid":"","institution":"Instituto Pirenaico de Ecología (IPE), CSIC; Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Yolanda","middleName":"","lastName":"Pueyo","suffix":""},{"id":456494454,"identity":"6392dd89-9c72-4558-a1ac-20c55055c809","order_by":62,"name":"Pedro J. Rey","email":"","orcid":"https://orcid.org/0000-0001-5550-0393","institution":"Universidad de Jaén","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"J.","lastName":"Rey","suffix":""},{"id":456494455,"identity":"a243d26f-9dbc-436b-b3cc-b21b56e94998","order_by":63,"name":"Ana Rey","email":"","orcid":"https://orcid.org/0000-0003-0394-101X","institution":"MNCN-CSIC, Spanish Scientific Council","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Rey","suffix":""},{"id":456494456,"identity":"529d32f0-b0be-4ce3-9e6f-ff4a6875e4eb","order_by":64,"name":"Asunción de los Ríos","email":"","orcid":"https://orcid.org/0000-0002-0266-3516","institution":"Agencia Consejo Superior de Investigaciones Científicas, Museo Nacional de Ciencias Naturales","correspondingAuthor":false,"prefix":"","firstName":"Asunción","middleName":"de los","lastName":"Ríos","suffix":""},{"id":456494457,"identity":"344f08bd-304e-4393-aa89-d3d04591211f","order_by":65,"name":"Alexandra Rodríguez","email":"","orcid":"https://orcid.org/0000-0001-5849-8778","institution":"CSIC","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Rodríguez","suffix":""},{"id":456494458,"identity":"9a6d7d9e-088e-4a40-9bc2-d214befee18f","order_by":66,"name":"Borja Lozano","email":"","orcid":"","institution":"Centre for Applied Ecology ‘Prof. Baeta Neves' (CEABN-InBIO), School of Agriculture, University of Lisbon, Tapada da Ajuda, Lisbon, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Borja","middleName":"","lastName":"Lozano","suffix":""},{"id":456494459,"identity":"c6273eaf-c5b8-4663-bc68-7725326b3e6c","order_by":67,"name":"Raul Roman","email":"","orcid":"","institution":"School of Life Sciences, University of Nevada-Las Vegas, 4505 S. Maryland Pkwy., Las Vegas, NV, 89154, USA","correspondingAuthor":false,"prefix":"","firstName":"Raul","middleName":"","lastName":"Roman","suffix":""},{"id":456494460,"identity":"5c55f413-635f-4d36-b599-92286b5bb78e","order_by":68,"name":"Jan Ruppert","email":"","orcid":"","institution":"Plant Ecology Group, University of Tübingen; Tübingen","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Ruppert","suffix":""},{"id":456494461,"identity":"4ef3f57f-c307-4559-8728-fe81376935e7","order_by":69,"name":"Ayman Salah","email":"","orcid":"","institution":"Al-Quds University, Jerusalem","correspondingAuthor":false,"prefix":"","firstName":"Ayman","middleName":"","lastName":"Salah","suffix":""},{"id":456494462,"identity":"ed3d410a-97cb-42ed-bbea-846805d5c828","order_by":70,"name":"Joana Serôdio","email":"","orcid":"","institution":"University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Joana","middleName":"","lastName":"Serôdio","suffix":""},{"id":456494463,"identity":"7825b33e-b826-44c7-9830-b9bc5b55274c","order_by":71,"name":"José Siles","email":"","orcid":"","institution":"Department of Soil and Water Conservation, CEBAS-CSIC, Campus Universitario de Espinardo, 30100 Murcia, Spain","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Siles","suffix":""},{"id":456494464,"identity":"efd2c5c9-fe2b-44f0-b68f-cad22ff64fa2","order_by":72,"name":"Jay Singh","email":"","orcid":"","institution":"Department of Natural Resource Science, Thompson Rivers University, Kamloops, British Columbia, Canada, V2C 0C8","correspondingAuthor":false,"prefix":"","firstName":"Jay","middleName":"","lastName":"Singh","suffix":""},{"id":456494465,"identity":"d60b280c-9458-431a-be06-5143f4b0714a","order_by":73,"name":"Samantha Travers","email":"","orcid":"","institution":"Department of Climate Change, Energy, the Environment \u0026 Water, Level 2, 32 Mann Street , Gosford NSW 2250, Australia","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"Travers","suffix":""},{"id":456494466,"identity":"d5c6c50e-90ab-46f6-8588-184efe6e247f","order_by":74,"name":"Sainbileg Undrakhbold","email":"","orcid":"","institution":"National University of Mongolia","correspondingAuthor":false,"prefix":"","firstName":"Sainbileg","middleName":"","lastName":"Undrakhbold","suffix":""},{"id":456494467,"identity":"45e5c035-ead5-428d-ada2-3fb79612ed80","order_by":75,"name":"Orsolya Valkó","email":"","orcid":"https://orcid.org/0000-0001-7919-6293","institution":"University of Debrecen, Department of Ecology","correspondingAuthor":false,"prefix":"","firstName":"Orsolya","middleName":"","lastName":"Valkó","suffix":""},{"id":456494468,"identity":"e8da270f-4a1d-44af-b208-a5c372f2af43","order_by":76,"name":"María Vivas","email":"","orcid":"https://orcid.org/0000-0003-2712-417X","institution":"INDEHESA, Universidad de Extremadura, Plasencia 10600, Spain","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Vivas","suffix":""},{"id":456494469,"identity":"bf4259b8-6587-4d59-9818-bba12f58735c","order_by":77,"name":"Lixin Wang","email":"","orcid":"","institution":"Indiana University Indianapolis","correspondingAuthor":false,"prefix":"","firstName":"Lixin","middleName":"","lastName":"Wang","suffix":""},{"id":456494470,"identity":"26e843e9-9365-496d-a7e9-226eea74175c","order_by":78,"name":"Mark Williams","email":"","orcid":"https://orcid.org/0000-0002-4766-9399","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Williams","suffix":""},{"id":456494471,"identity":"2d59d368-adb7-4f64-a1e5-16e01e9f2f30","order_by":79,"name":"Eli Zaady","email":"","orcid":"","institution":"Institute of Plant Sciences, Gilat Research Center","correspondingAuthor":false,"prefix":"","firstName":"Eli","middleName":"","lastName":"Zaady","suffix":""},{"id":456494472,"identity":"d50eee37-54b9-4e89-b7ac-bbb22adec654","order_by":80,"name":"Guiyao Zhou","email":"","orcid":"https://orcid.org/0000-0002-1385-3913","institution":"Instituto de Recursos Naturales y Agrobiología de Sevilla","correspondingAuthor":false,"prefix":"","firstName":"Guiyao","middleName":"","lastName":"Zhou","suffix":""},{"id":456494473,"identity":"0e9fd4af-f534-4001-84bd-7ff4fd8496b0","order_by":81,"name":"Fernando Maestre","email":"","orcid":"https://orcid.org/0000-0002-7434-4856","institution":"King Abdullah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Maestre","suffix":""},{"id":456494474,"identity":"ce6e3e10-fcb0-436d-a90a-b12a1d212afe","order_by":82,"name":"Brajesh Singh","email":"","orcid":"https://orcid.org/0000-0003-4413-4185","institution":"Hawkesbury Institute for the Environment, Western Sydney University, Penrith,","correspondingAuthor":false,"prefix":"","firstName":"Brajesh","middleName":"","lastName":"Singh","suffix":""},{"id":456494475,"identity":"faca9b7f-8685-43d7-aab7-cac5a5501c30","order_by":83,"name":"Manuel Delgado-Baquerizo","email":"","orcid":"https://orcid.org/0000-0002-6499-576X","institution":"Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS)","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Delgado-Baquerizo","suffix":""}],"badges":[],"createdAt":"2025-05-13 14:33:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6656511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6656511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82778532,"identity":"91f9a8c9-42a5-4350-8287-8aa78b55a79d","added_by":"auto","created_at":"2025-05-15 07:44:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1142568,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of the 200 sampling sites studied (a). Distribution of sampling sites among climate (sub)zones based on Whittaker biomes (b). Random forest analyses identifying the main environmental factors predicting soil C:N, C:P and N:P ratios (c). Changes of soil C:N, C:P and N:P ratios along main environmental factors such as water availability and net primary productivity (d). Spearman’s correlation coefficient is presented in each panel.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6656511/v1/b5106b578dd07b822f3c237f.png"},{"id":82778533,"identity":"7f94869e-dbbe-4e36-afb5-675a11be49a4","added_by":"auto","created_at":"2025-05-15 07:44:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":929342,"visible":true,"origin":"","legend":"\u003cp\u003eRandom forest analyses identifying the key KO-annotated individual genes (a), KEGG-annotated metabolic pathways (b) and Carbohydrate-Active Enzymes -annotated carbohydrates hydrolysis gene modules (c) in response to changes in soil C:N, N:P and C:P ratios. Only well-classified genes that significantly changed (P\u0026lt;0.05) with soil elemental imbalances were represented in each panel.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6656511/v1/7cea880aa3a5ee97e42c99c9.png"},{"id":82778534,"identity":"68cb51f5-e9fe-453e-afd3-7f232d9ae22c","added_by":"auto","created_at":"2025-05-15 07:44:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":528419,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between the individual KO-annotated genes involved in soil C, N and P pools and contents.Red boxes represent significantly positive relationships (ρ \u0026gt; 0, P \u0026lt; 0.05), blue boxes represent significantly negative relationships (ρ \u0026lt; 0, P \u0026lt; 0.05). The blank boxes represent non-significant relationships.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6656511/v1/4154817489864375e3fdeea4.png"},{"id":82778541,"identity":"e1bb0839-7ae9-4c50-9566-361346f6a1b7","added_by":"auto","created_at":"2025-05-15 07:44:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1184082,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual diagram summarizing the shift from catabolism to anabolism in terrestrial ecosystems subjected to aridity-driven elemental imbalances. In brief, when water availability and NPP decrease, soil C becomes relatively less available because of the decoupled C, N, and P cycles (represented by decreased soil C:N and C:P ratios). Under such circumstances, the soil microbiome must inhibit catabolic processes by reducing extracellular enzymes to acquire nutrients and energy from organic matter degradation. Instead, their anabolic processes related to drought resistance and survival are promoted. Subsequently, microbial-driven soil nutrient cycling and C sequestration are weakened, thereby threatening ecosystem services under climate change.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6656511/v1/e0e1700777664638c67f55ed.png"},{"id":88914258,"identity":"fa91d15d-6ffd-4fab-b42a-40673cd6c523","added_by":"auto","created_at":"2025-08-12 15:59:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4831004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656511/v1/b8ee3271-90cd-4264-a8a5-a9d54d0fcd86.pdf"},{"id":82778958,"identity":"880fc63d-d173-4c85-bf4f-dff5e2a0a98f","added_by":"auto","created_at":"2025-05-15 07:52:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22785562,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656511/v1/41598f0c1cee5eb24b2107b6.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Soil microbial genomic responses to aridity-driven elemental imbalance across global biomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal-scale changes in aridification have been widely recognized as disrupting the fundamental stoichiometric relationships that sustain terrestrial ecosystems \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, leading to worldwide elemental imbalances in the cycling of soil organic carbon (SOC), nitrogen (N), and phosphorus (P) (e.g., altered soil C:N, N:P, and C:P ratios) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. For example, low precipitation in arid ecosystems often results in both limited plant productivity (reducing litter-derived organic C and N inputs) and a greater relative availability of P, resulting in low soil C:P and N:P ratios \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In contrast, the predominantly abiotic origin of P from bedrock \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, unlike the biological fixation of C and N from the atmosphere, contributes to the higher soil C:P and N:P ratios in wetter ecosystems. These ecological imbalances should be particularly impactful for soil microbes because of their dependence on cellular elemental homeostasis and their intimate link to soil biogeochemistry \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Although soil microbes are among the most diverse and abundant organisms on the planet and provide essential ecosystem services that are crucial for human well-being (e.g., food security and climate action) \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, little is known about the genomic responses of the soil microbiome under aridity driven elemental imbalances \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. A deeper understanding of how the soil microbiome respond to aridity-driven changes in soil biogeochemistry is essential for better predicting the fate of soil microbes in a changing world.\u003c/p\u003e \u003cp\u003eTwo primary scientific barriers contribute to the current uncertainties regarding the responses of the soil microbiome to climate-driven elemental imbalances. First, there is a significant lack of a global-scale standardized database that simultaneously integrates information on stocks and pools of soil organic C, N, and P with high-resolution functional genomic data such as metagenomes. Most studies have been limited to local scales; however, a global-scale approach is essential because soil elemental imbalances occur at the macroecological level across global biomes, from deserts to tropical forests \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Second, changes in soil C:N:P ratios and/or elemental imbalances may result in important alterations in microbiome metabolism \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Recent advances in trait-based microbial ecology have suggested potential trade-offs between anabolism and catabolism \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. For example, a recent study from a successional chronosequence in a subtropical region of China demonstrated that P limitation over time increases the abundance of genes involved in the degradation of recalcitrant compounds, such as lignin and its aromatic derivatives, to acquire P resources \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Yet, empirical evidence for such potential responses and itheir consequences on various soil processes, including soil organic C sequestration and nutrient cycling, remains limited on a global scale \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Previous studies have highlighted the need for generalizable frameworks for microbial responses to disturbances to enhance our understanding and predictability of ecosystem stability \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Understanding these microbial shifts is vital as they may reshape the delivery of terrestrial ecosystem services in the context of climate change \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere we put together a global topsoil survey encompassing 200 terrestrial ecosystems was conducted across all major biomes, from deserts to tropical regions, and across a diverse array of vegetation types, including forests, grasslands, and shrublands (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Our objective was to investigate how the soil microbiome responds to aridity-driven elemental imbalances across global environmental gradients \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. To achieve this, we integrated data on soil C, N, and P stocks as well as stoichiometric ratios with shotgun metagenomic sequencing. This metagenomic approach enabled us to characterize microbial metabolism\u0026mdash;both anabolic and catabolic\u0026mdash;by analyzing key functional genes and metabolic pathways (a detailed list of genes involved in each metabolism is available in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The aridity index (mean annual precipitation (MAP) / potential evapotranspiration) was utilized as a proxy for water availability \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, given its capacity to account for both the natural input of water into the ecosystem and potential losses resulting from evapotranspiration. We opted for water availability for simplicity because the aridity index can be counterintuitive (i.e., a higher aridity index indicates greater water availability). Initially, we identified the primary environmental factors associated with soil C:N:P ratios and pinpointed the tipping points in stoichiometric ratios using machine-learning approach. Subsequently, we identified the key anabolic and catabolic genes involved in soil elemental imbalances and examined how these genes are associated with organic C, N, and P contents on a global scale. Based on the Metabolic Theory of Ecology \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, we hypothesized that soil microbes respond to aridity-driven elemental imbalances by shifting their metabolism to enhance anabolism (e.g., resistance to drought stress and sustained growth) while suppressing catabolism (e.g., synthesizing extracellular enzymes for energy and nutrient acquisition).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eOur study offers new insights into the capacity of microbiomes to respond to global-scale soil elemental imbalances driven by changes in aridity. Consistent with existing literature, ecosystems with lower water availability (i.e., a lower aridity index or higher aridity) demonstrated reduced net primary productivity (NPP) and lower soil C:P and N:P ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, the random forest analysis indicated that both anabolic and catabolic genes were significant predictors of soil elemental ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests that soil microbiomes may prioritize specific metabolic strategies for survival under soil elemental imbalances resulting from aridification. For example, in arid environments characterized by lower soil C:N and N:P ratios, likely due to diminished organic matter inputs under less productive conditions, the abundance of genes associated with anabolism, such as those involved in RNA transcription and the synthesis and transport of intracellular proteins were increased. This response aims to sustain microbial growth under stressed conditions. Lower soils C:N and N:P ratios were associated with suppressed catabolism, as indicated by the reduced abundance of genes responsible for the degradation of litter and organic matter, processes that facilitate the extraction of N and P (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Collectively, our findings suggest that the microbiome employs metabolic strategies that involve shifts between catabolism and anabolism in response to worldwide soil elemental imbalances. These microbial metabolic shifts, necessary for survival under stoichiometric imbalances, can diminish the capacity of terrestrial ecosystems to provide multiple ecosystem services such as declining C reservoirs \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and may further compromise ecosystem stability. For instance, the suppression of genes involved in catabolism result in decreased degradation and transformation of external organic matter into the soil \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, which could impair soil aggregate stability and moisture retention, making the soil more susceptible to erosion and subsequent nutrient depletion. This understanding is critical to better anticipate changes in the soil microbiome, the most abundant and diverse organism on the planet, and their ecological functions in the context of climate change.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAridity-driven soil elemental imbalances\u003c/h2\u003e \u003cp\u003eAs anticipated, global-scale aridification\u0026mdash; and its impact on ecosystem productivity\u0026mdash; played a central role in driving soil elemental imbalances \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This finding is crucial for addressing our novel research question: how does the microbiome responds to soil elemental imbalances driven by changes in water availability across global environmental gradients? Decreases in water availability were associated with a relative deficiency of organic C compared with N and P in soils, resulting in declines in soil C:N and C:P ratios, particularly in low-productivity ecosystems. Random forest analysis identified climate variables as strong predictors of variation in soil C, N and P contents (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). When combined with other environmental factors,\u0026mdash; such as soil physicochemical properties, geography, and vegetation type\u0026mdash;, these variables accounted for up to 73% of the observed variation (Figs. S1 and S2). Further analysis revealed that water availability and NPP were the most influential drivers of global variation in the soil C:N, N:P, and C:P ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Reduced plant productivity can lead to lower soil N:P ratios as N becomes relatively deficient in more arid regions owing to the constrained microbial decomposition of plant litter (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed and S3) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and decreased biotic N fixation activities \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Although most P fractions remained relatively stable with changing water availability, the content of mineral P (represented by primary P) increased significantly with decreasing NPP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), likely due to reduced plant uptake (Fig. S4) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Notably, in these biologically inactive ecosystems, diminished plant productivity contributes to lower soil organic C accumulation through decreased inputs of litter and root exudates, further exacerbating elemental imbalances (i.e., lower soil C:N and C:P ratios) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed and S4) \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Beyond the conclusion that both C and N stocks and pools are the primary limiting factors for soil microbial growth in drylands \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, these results provide evidence that their stoichiometric balance will become increasingly significant in light of climate change consequences, such as heightened aridity.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil microbiome responses to aridity-driven element imbalances\u003c/h3\u003e\n\u003cp\u003eWe further demonstrated that soil microbiomes support a diverse array of taxonomic and genetic responses to elemental imbalances driven by global-scale changes in aridity. First, we explored the correlation between variations in C:N:P and water availability with soil microbial taxa. As expected, soil microbial taxa largely respond to water availability and element imbalances across environmental gradients. For example, the relative abundance of Chloroflexi is known to be greater under low water availability and C:P and N:P ratios (Fig. S5) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Similarly, the relative abundance of Acidobacteria, Verrucomicrobia, and Rhizaria were positively correlated with water availability and C:P and N:P ratios (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. S5). Although microbial taxonomic responses to nutrient imbalances have been previously investigated at a local and regional scales, our study provides novel insights on their responses across global biomes. Moreover, much less was known about the responses of microbial functional genes to aridity-driven elemental imbalances. Here, we showed that functional microbial genes and metabolic pathways were associated with the soil elemental stoichiometry (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, functional genes and metabolic pathways annotated in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, along with carbohydrate hydrolases from the Carbohydrate-Active Enzymes (CAZy) database, accounted for up to 51% of the observed variation in soil C, N, and P content (Figs. S6 and S7). In addition, functional genes and metabolic pathways categorized as cellular processes, as well as genetic and environmental information processes (among others), collectively explained up to 24% of the observed variation in the soil C:N, N:P, and C:P ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, the abundance of genes associated with transcription, protein synthesis and microbial growth increased in soils with lower C:N and C:P ratios, primarily because of the reduced SOC (Figs. S8 and S9). Regarding the soil C:P ratio, microbial metabolism exhibited a lower \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, yet still significant explanatory power (2\u0026ndash;5% of the variation), with environmental variables accounting for up to 50% (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Collectively, our findings indicate that the community composition, diversity and metabolism of the soil microbiome are strongly correlated with aridity-driven soil elemental imbalances, particularly the deficiency of organic C.\u003c/p\u003e \u003cp\u003eSoil elemental imbalances, particularly the relative scarcity of organic C compared with N and P, associated with shifting climatic conditions in areas with low water availability, were associated with changes in microbial metabolism from catabolic to anabolic processes. As the soil C:N, N:P, and C:P ratios decreased, we observed significant increases in genes and pathways related to anabolism, such as nucleotide replication, RNA transcription, and protein folding, sorting, and transport, under conditions of relative soil organic matter deficiency (characterized by reduced organic C and N content; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figs. S8 and S9). These findings suggest that arid environments, which lead to relative organic C deficiency, limit microbial investment in organic P mineralization and chitin degradation (as indicated by the gene clusters of \u003cem\u003ephn\u003c/em\u003e and \u003cem\u003echi\u003c/em\u003e). Conversely, this conditions accelerated inorganic P transports (as indicated by the \u003cem\u003epstA\u003c/em\u003e gene) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and N assimilation, including the synthesis of glutamic acid (as indicated by the \u003cem\u003eglt\u003c/em\u003e gene cluster) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This outcome is theoretically expected because microbial cellular processes, such as biomass production, require substantial amounts of N- and P-rich molecules including proteins and ATP \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Further analyses consistently revealed increased relative abundance of genes associated with the synthesis and transport of proteins (as indicated by \u003cem\u003esecG\u003c/em\u003e, \u003cem\u003epcnB\u003c/em\u003e, and \u003cem\u003ecysM\u003c/em\u003e) under arid conditions with lower soil C:N ratios (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Figs. S8 and S10a). Consequently, phospholipid synthesis appears to be compromised compared with the synthesis of proteins and/or carbohydrates under these organic C-limited conditions (Figs. S8 and S9), as microorganisms require more ribosomal RNA to synthesize osmolytes, biofilms, and cell walls to withstand drought \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. As such, genes encoding intracellular protein and polysaccharide synthesis (e.g., protein processing in the endoplasmic reticulum, phosphoglycerate synthase, and murein polymerase) also exhibited negative correlations with the soil C:P and/or N:P ratios (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Figs. S8 and S10b). Murein and phosphoglycerate are critical intermediates in bacterial cell wall synthesis \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, whereas protein synthesis in the endoplasmic reticulum is essential for microbial survival in stressed environments \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Collectively, these results support our hypothesis and indicate that aridity-driven soil elemental imbalances shift microbial metabolism by activating anabolic processes, that are crucial for microbial resistance and survival in arid environments.\u003c/p\u003e \u003cp\u003eConversely, in wetter regions with higher organic C content (i.e., elevated soil C:P and C:N ratios), an increased abundance of genes associated with catabolism was observed. Higher organic C resources can stimulate soil microbiome activity, which in turn requires more N and P \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. As N and P become less available, soil microbes must mine organic matter using extracellular enzymes to access the limiting nutrients \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. We found that genes encoding extracellular enzymes that degrade recalcitrant soil organic matter, such as terpenoids, polyketides \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, and arylesters \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, were relatively increased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in soils with relative N and P deficiencies (Figs. S8). For example, the abundance of genes associated with the degradation of aromatic compounds (e.g., \u003cem\u003ealiA\u003c/em\u003e and \u003cem\u003etfdB\u003c/em\u003e), and root exudates such as terpenoids (e.g., \u003cem\u003emlhB\u003c/em\u003e and \u003cem\u003echnC\u003c/em\u003e) increased in soils with higher organic C (Figs. S11a, S11b and S12). Consistently, we observed an increased abundance of genes associated with signal transduction and membrane transport in soils with higher C:P ratios, which are essential for the production and secretion of extracellular enzymes (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMicroorganisms with well-developed synthesis and secretion systems for extracellular enzymes can survive better in nutrient deficient soils \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e because catabolically degrading enzymes release nutrients and energy for microbial uptake (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. For example, in nutrient-stressed environments, such as those found in areas with high water availability \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, the soil microbiome exhibits overflow metabolism of energy to mineralize organic matter \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. As evidence, the relative abundance of \u003cem\u003ephn\u003c/em\u003e gene clusters, which mediate the catabolism of organic P (i.e., phosphonates \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e) increased in soils with high SOC content and elevated C:P and C:N ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). These results align with those of a study demonstrating microbiome-driven coupling of soil C and P in agroecosystems \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Furthermore, we observed positive correlations between the C:P ratio and the relative abundance of genes encoding \u003cem\u003egcd\u003c/em\u003e, which are involved in the production of organic acids and subsequent mineral P solubilization \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, we did not find any correlation with the phosphatase gene (\u003cem\u003ephoD\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results underscore the significance of P solubilization processes in addressing nutrient imbalances caused by climatic conditions, suggesting that P solubilization can be a potential response to P deficiency in comparison to mining organic matter at the global scale. Similarly, we found that nutrient deficiency associated with the increased abundance of genes regulating bacterial flagellar assembly (i.e., the \u003cem\u003eflg\u003c/em\u003e gene cluster) that are related to motility (Fig. S11c and S12). Enhanced motility increases opportunities for microorganisms to access nutrient-rich environmental patches \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The promotion of extracellular enzyme production and motility requires significant energy derived from the consumption of organic C \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In this context, increases catabolism of organic matter degradation was observed (Figs. S11a, S11b and S12). Collectively, our findings support our hypothesis that enhanced catabolism at the expense of organic matter represents a potential strategy for the microbiome to cope with elevated C:N and C:P ratios in the environment. Previous studies have reported increased microbial catabolism in stressful environments \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, whereas microbial catabolism tends to decrease in fertile soils, which are typically characterized by higher organic C content \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Intriguingly, in this study, we found that microbial catabolism is still elevated in fertile soils, albeit with elemental imbalances.\u003c/p\u003e\n\u003ch3\u003ePotential consequences of shifting from catabolism to anabolism\u003c/h3\u003e\n\u003cp\u003eOur study suggests that aridity-driven soil elemental imbalances may reshape microbial metabolic strategies, with possible cascading consequences on terrestrial ecosystem services. Specifically, elevated abundance of genes involved in anabolism (e.g., microbial growth) may enhance soil microbial resistance and survival under drought stress. Conversely, catabolic processes (e.g., extracellular enzyme production) are suppressed, resulting in diminished degradation of external organic matter and its transformation to soil organic C pools and stocks \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For instance, decreases in SOC content were accompanied by a reduction in the abundance of genes encoding the transport and synthesis of xylanase, alpha- glucosidase, and beta-glucosidase (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig. S9). These opposing effects can destabilize the C-climate feedback loop, particularly in C-limited ecosystems. However, the negative correlations between SOC contents and the abundance of genes encoding chitosanase (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig. S9) suggest that microbial anabolism can, to some extent, fuel long-term C stabilization through mineral-reactive compounds (e.g., cell wall residues and extracellular polymers \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe adoption of anabolic processes as a survival strategy in response to aridity-driven elemental imbalances may compromise ecosystem stability. The resulting decline in the abundance of genes associated with anabolic processes is intrinsically linked to a reduced capacity to degrade and transform external organic matter into soil organic C pools and stocks \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. This can lead to diminished soil aggregate formation and water retention, resulting in more erodible soils and nutrient loss. Furthermore, alterations in microbial metabolic processes can reshape the nutrient cycling in terrestrial ecosystems. For instance, studies have demonstrated that increased N availability through fertilization can enrich genes involved in catabolism \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. To address resource imbalances, soil microbes may release a greater proportion of organic N as ammonium, thereby modifying N mineralization and terrestrial nutrient cycling \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. An interesting question remains as to whether changes in soil structure and nutrient availability, driven by metabolic shifts, could affect soil microbial composition and diversity. It is postulated that microorganisms specializing in rapid resource degradation through catabolic genes (e.g., saprophytic fungi) may be favored over other microbial groups with higher energy demands, such as N-fixing bacteria, whose activity relies on energy-demanding anabolic processes. This finding supports those of previous studies, indicating shifts in the fungal:bacterial ratio relative to the stoichiometric ratios of soil elements \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. If confirmed, it is essential to understand how soil food webs and their functional diversity change, as well as the implications for key ecosystem services when catabolism transitions to anabolism. Our study lays the groundwork for future research to validate these hypotheses and underscores the necessity of expanding sampling efforts across larger and more diverse regions to derive more robust and global patterns.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTaken together, our findings provide novel insights into global-scale responses of the soil microbiome to soil elemental imbalances caused by global-scale changes in aridity. As water availability decreases, ecosystems become less productive and support reduced input of soil organic C, whereas soil N and P become comparatively more available. These soil elemental imbalances were significantly associated with increases in the relative abundance of functional genes and metabolic pathways associated with microbial anabolism, as well as decreases in genes associated with catabolism, particularly those involved in the degradation of external organic matter, especially in recalcitrant forms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The shift in microbial metabolism from catabolism to anabolism may further influence the capacity of soils to sequester C and provide nutrients, thereby compromising ecosystem services. These findings are crucial for anticipating the future of the soil microbiome under climate change and suggest that soil microbiomes possess a wide array of genetic tools to acclimate to aridification; thus, they prioritize drought resistance and survival over energy and nutrient acquisition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGlobal field survey\u003c/h2\u003e \u003cp\u003eWe collected topsoil samples (~\u0026thinsp;0\u0026ndash;10 cm) from 200 ecosystems across all continents to investigate the adaptation strategies of the soil microbiomes in response to climate-driven elemental imbalances. These sampling sites encompassed a variety of ecosystem types, including temperate seasonal forests, tropical seasonal forests, temperate grasslands, tundra grasslands, subtropical woodlands, and shrublands, providing a comprehensive representation of the terrestrial environmental conditions found on Earth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The latitude, elevation, and slope of each sampling site were recorded \u003cem\u003ein situ\u003c/em\u003e using a portable GPS unit. Vegetation types were classified for each location based on the dominant species. A portion of the soil samples was frozen for molecular analyses at -20\u0026deg;C, whereas the remaining samples were air-dried for biogeochemical analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClimate and ecosystem productivity\u003c/h3\u003e\n\u003cp\u003eThe MAP, which ranges from 81 to 2,161 mm; mean annual temperature (MAT), ranging from \u0026minus;\u0026thinsp;3.5 to 24.8℃; seasonality of precipitation (PSEA); seasonality of temperature (TSEA); and the mean diurnal range of temperature (MDR) for the sample sites were obtained from interpolations provided by WorldClim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We also measured NPP and plant cover as proxies for the functional traits of the vegetated ecosystems across all sampling sites. Perennial vegetation cover was measured \u003cem\u003ein situ\u003c/em\u003e at each site using the line-intercept method. We used the normalized difference vegetation index (NDVI) as a proxy for NPP, which was obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA\u0026rsquo;s Terra satellites (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://daac.ornl.gov/index.shtml\u003c/span\u003e\u003cspan address=\"http://daac.ornl.gov/index.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at a resolution of 250 m. We believe that the choice of productivity period will not affect our results, as we used average values derived from normalized difference vegetation index measurements taken during the month before, during, and after the sampling dates at each surveyed plot.\u003c/p\u003e\n\u003ch3\u003eDetermination of soil C, N and P content\u003c/h3\u003e\n\u003cp\u003eThe contents of each component of C, including total organic C, mineral-associated organic C, particulate organic C and total inorganic C; N, comprising total N and dissolved inorganic N; and P, which includes total P, primary P, occluded and non-occluded P, organic P, residual P, and available P, were measured. The total P content of the soil was determined using sodium hydroxide fusion method \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Particulate organic C and mineral-associated organic C were separated using a combined density and particle size fractionation method \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Inorganic (plant-available) N was measured using a continuous-flow stream autoanalyzer (SEAL-AA3, Norderstedt, Germany). Soil non-occluded P, occluded P, and organic P were quantified using the sequential extraction procedure developed by Hedley et al. in 1982 \u003csup\u003e57\u003c/sup\u003e and modified by Guppy et al. in 2000 \u003csup\u003e58\u003c/sup\u003e. Soil organic C and total N were analyzed using a combination of dichromate oxidation, the Kjeldahl method, and a Flash EA 1112 Series Elemental Analyzer connected via a Conflo III to a DeltaPlus XP isotope ratio mass spectrometer (all Thermo Finnigan, Bremen, Germany). In this study, the soil C:N, N:P and C:P ratios were calculated based on the ratios of soil organic C to total N, total N to total P, and organic C to total P, respectively.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSoil microbial composition and diversity measures\u003c/h2\u003e \u003cp\u003eIn this study, the diversities of soil bacteria, fungi, protists and invertebrates were measured using an Illumina MiSeq platform for amplicon sequencing. Soil DNA was extracted with the PowerSoil DNA Isolation Kit (MoBio Laboratories) following the manufacturer\u0026rsquo;s instructions. The 16S rRNA gene for bacteria and the 18S rRNA gene for eukaryotes were sequenced using the 341F/805R \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e and Euk1391f/EukBr \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e primer sets, respectively. The bioinformatic analyses were performed using a combination of QIIME \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, USEARCH \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and UNOISE3 \u003csup\u003e63\u003c/sup\u003e. The sequences were clustered into soil phylo-types\u0026mdash;zOTUs\u0026mdash;at a 100% identity level. The representative sequences of zOTUs were annotated against the SILVA (16S rRNA gene) \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e as well as PR2 and SILVA (18S rRNA gene) \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e databases. Before analyses, the zOTU abundance tables were rarefied at 10,000 (for bacteria) and 4,000 (for eukaryotes) sequences per sample to ensure an even sampling depth within each microbial group. Protists were defined as eukaryotic taxa, except fungi, Metazoa (invertebrates and vertebrates) and vascular and non-vascular plants (Streptophyta). Fungal guilds were characterized using the FUNGuild \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In this study, we used species richness, which represents the number of phylotypes, as a measure of soil microbial diversity since it is widely used and straightforward.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic sequencing and bioinformatic analyses\u003c/h2\u003e \u003cp\u003eMetagenomic sequencing was performed at the Next Generation Sequencing (NGS) Facility (Western Sydney University, Australia) using the Illumina NovaSeq platform, which generated approximately 10 Gb of high-quality 150 bp paired-end reads per sample. Clean reads were obtained after quality trimming, using fastp (version 0.20.1) to remove low-quality reads. Metagenome assembly was conducted using Megahit (v1.2.9) \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, retaining only contigs\u0026thinsp;\u0026ge;\u0026thinsp;1000 bp in length. Open reading frames of the assembled contigs were predicted using the Prodigal software (version 2.6.3). All open reading frames were compiled into a set of unique genes after clustering with CD-HIT at 95% sequence identity to create a non-redundant gene catalog \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Read counts and reads per million for each predicted non-redundant gene were calculated using Salmon (v1.10.3) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. To investigate how soil microbial metabolism respond to climate-driven elemental imbalances, the predicted genes were annotated against the eggNOG databases (v5.0) using eggnog-mapper (v2.1.12) \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, which provided KEGG Orthology (KO), KEGG pathway, and CAZy family profiles \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Before the analysis, genes associated with human diseases and those related to plant and macro-organism systems were excluded. The details of the three levels of KEGG annotation classification are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The categories of KO-annotated genes involved in soil C degradation, as well as the N and P cycles, are detailed in Table S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical modeling\u003c/h2\u003e \u003cp\u003e \u003cb\u003eNonlinear regressions and threshold identification.\u003c/b\u003e In this study, we used the Akaike Information Criterion (AIC) in two steps to identify the most appropriate model for the relationship between soil C, N, and P content and environmental gradients in water availability and NPP \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. The first step involved fitting both linear and nonlinear models, including quadratic models (the simplest form of nonlinear trends) and generalized additive models (GAMs) (which capture more complex trends through smoothing parameters). The lower AIC value for the nonlinear model suggests the existence of a threshold for water availability and NPP. In the second step, to investigate the threshold in each nonlinear regression, we defined thresholds as the points along the water availability and NPP gradients, where the soil C, N, and P contents changed abruptly (i.e., discontinuous thresholds or breakpoints) in relation to water availability and NPP (i.e., continuous thresholds). In the latter case, although an abrupt change in slope was observed, more subtle and continuous changes that may involve high-degree polynomials were also considered. These changes are typically better represented by the GAM, where the changes in slope do not correspond to a sudden breakpoint, but rather indicate a point of maximum curvature. In both scenarios, despite being continuous, it remains possible to identify thresholds, and these values are reported, as they may provide critical information for their management.\u003c/p\u003e \u003cp\u003eContinuous thresholds can be effectively detected using segmented regression, which isa linear regression that modifies the slope at a threshold or through a more complex GAM regression. In contrast, discontinuous thresholds require an overall change in both intercept and slope. These can be accurately modeled using either step regression, which alters only the intercept at the threshold, or a combination of step and segmented regressions, termed segmented because they involve changes in both the intercept and slope at the threshold. The optimal model was selected based on AIC values. We performed 1,000 bootstrap samplings for each case to identify a set of 1,000 plausible thresholds from which we calculated the mean value as the final threshold value. These analyses were conducted using R packages \u003cem\u003echngpt\u003c/em\u003e and \u003cem\u003egam\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation analyses.\u003c/b\u003e Spearman\u0026rsquo;s correlation analysis was used to assess the relationships among individual variables along environmental gradients, including soil C, N, and P content, and the abundance of genes (modules) and their associated metabolic pathways. The strength and direction of these relationships are indicated by the correlation coefficients and \u003cem\u003eP\u003c/em\u003e values. All \u003cem\u003eP\u003c/em\u003e values were adjusted using the false discovery rate (FDR) \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Correlation analyses were conducted using the R package \u003cem\u003epsych\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRandom forest analyses.\u003c/b\u003e To identify potential functional gene modules that respond to variations in soil C, N, and P content, a predictive model utilizing a random forest machine learning method was established with 500 trees and 1,000 permutations implemented using the R package, \u003cem\u003eRandomForest\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Cross-validation was conducted using the \u003cem\u003erfcv\u003c/em\u003e function to select the most appropriate features. The \u003cem\u003erfPermute\u003c/em\u003e function was used to estimate the \u003cem\u003eP\u003c/em\u003e value for the significance of each predictor included in the random forest prediction results, whereas the importance of each predictor was represented by the percentage of the IncMSE value. In this study, only well-classified genes that changed significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with soil elemental contents as well as their ratios were represented (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, the tested parameters were log10 transformed before the analyses to normalize the variance when necessary. All statistical analyses were performed using R, version 4.3.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.Z.F. and C.K.L. were supported by the National Key R\u0026amp;D Program (2024YFD1300800) and the National Natural Science Foundation of China (42177297 and 42407401). S.U. and B.B. were supported by the Taylor Family-Asia Foundation Endowed Chair in Ecology and Conservation Biology. T.G. and T.U.N. were supported by Slovenian Research and Innovation Agency projects J4-3098, J4-4547 and Research group P4-0107. M.B. is supported by a Ram\u0026oacute;n y Cajal Fellowship from the Spanish Ministry of Science (RYC2021-031797-I). G.Y.Z. acknowledges the supports from Ram\u0026oacute;n y Cajal grant (RYC2022-035226-I) and AYUDAS DE EXCELENCIA RYC-MAX 2023 project from Spanish National Research Council. S.C.H. was supported, in part, by US National Science Foundation grant EAR-2012878 during the writing of this manuscript. R.R. was supported by the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie IF-GF Actions (MICROBIOCLIM, GA nr. 101028323). B.K.S. and T.S-S. works are supported by Australian Research Council (DP230101448). M.D-B. and F.B. acknowledges support from the Spanish Ministry of Science and Innovation for the I\u0026thinsp;+\u0026thinsp;D\u0026thinsp;+\u0026thinsp;i project PID2020-115813RA-I00 and PID2020‐114942RB‐I00 funded by MCIN/AEI/10.13039/501100011033.\u0026nbsp;J.A.S. acknowledges the support of the grant RYC2023-044125-I funded by MICIU/AEI/10.13039/501100011033 and by ESF+. T.S-S and B.K.S are supported by Australian Research Council (DP230101448).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: M.D.-B., B.K.S., F.T.M, and Y.F.; Investigation: C.L., Y.F., T.S.S., C.X., D.E., N.G., Y.L.B.P., V.O., B.G., E.G., M.G.G., E.V., M.B., S.A., J.M.-V., B.J.M., A.A.B., N.A.C., S.A., J.A., F.A., A.I.A., M.B., F.B., N.B., B.B., M.B., C.B., S.C.H, B.D., J.D., C.I.E., A.F., L.F., A.G., L.G.V., K.G., T.G., E.G.M., L.K., M.K., L.L., P.C.l.R., P.L., J.L., A.L., M.A.L., P.M., G.M.-K., T.M., A.M., E.M., D.M., J.P.M., G.M., M.M.-R., A.M., T.U.N., G.N., S.N., C.P., Y.P., P.J.R., A.R., M.A.R.M., A.R., B.R.L., R.R., J.R., A.S., J.S., J.A.S., J.S., S.T., S.U., O.V., M.V., L.W., M.W., E.Z., G.Z., F.T.M., B.K.S., M.D.-B.; Methodology and visualization: C.L., C.X., Y.F. and M.D.-B.; Writing-review \u0026amp; editing: Y.F., C.L., M.D.-B., B.K.S., T.S.S, F.T.M. and C.X. with contributions of all authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw sequencing data used in this study have been submitted to the NCBI Sequence Read Archive (SRA) database under the accession numbers PRJNA1162941 (shotgun metagenomics), PRJNA1249113 (16S rRNA gene amplicon), and PRJNA1249114 (18S rRNA gene amplicon). All the materials and protocols used in the article are available upon request and without restriction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll R codes supporting the conclusions of this article are included in figshare at https://doi.org/10.6084/m9.figshare.28829420. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlewell, C.\u003cem\u003e et al.\u003c/em\u003e Global phosphorus shortage will be aggravated by soil erosion. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 4546, doi:10.1038/s41467-020-18326-7 (2020).\u003c/li\u003e\n\u003cli\u003eDelgado-Baquerizo, M.\u003cem\u003e et al.\u003c/em\u003e Decoupling of soil nutrient cycles as a function of aridity in global drylands. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e502\u003c/strong\u003e, 672-676, doi:10.1038/nature12670 (2013).\u003c/li\u003e\n\u003cli\u003eStevenson, F. J. \u0026amp; Cole, M. A. \u003cem\u003eCycles of soils: carbon, nitrogen, phosphorus, sulfur, micronutrients\u003c/em\u003e. (John Wiley \u0026amp; Sons, 1999).\u003c/li\u003e\n\u003cli\u003eEwing, S. A.\u003cem\u003e et al.\u003c/em\u003e Rainfall limit of the N cycle on Earth. \u003cem\u003eGlobal Biogeochemical Cycles\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, GB3009, doi:10.1029/2006gb002838 (2007).\u003c/li\u003e\n\u003cli\u003eLajtha, K. \u0026amp; Schlesinger, W. H. The Biogeochemistry of Phosphorus Cycling and Phosphorus Availability Along a Desert Soil Chronosequence. \u003cem\u003eEcology\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 24-39, doi:10.2307/1943157 (1988).\u003c/li\u003e\n\u003cli\u003eBi, B.\u003cem\u003e et al.\u003c/em\u003e Enhanced rock weathering increased soil phosphorus availability and altered root phosphorus-acquisition strategies. \u003cem\u003eGlobal Change Biology\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, e17310, doi:10.1111/gcb.17310 (2024).\u003c/li\u003e\n\u003cli\u003eSinsabaugh, R. L. \u0026amp; Follstad Shah, J. J. Ecoenzymatic Stoichiometry and Ecological Theory. \u003cem\u003eAnnual Review of Ecology, Evolution, and Systematics\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 313-343, doi:10.1146/annurev-ecolsys-071112-124414 (2012).\u003c/li\u003e\n\u003cli\u003eBanerjee, S. \u0026amp; van der Heijden, M. G. A. Soil microbiomes and one health. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 6-20, doi:10.1038/s41579-022-00779-w (2022).\u003c/li\u003e\n\u003cli\u003eWall, D. H., Nielsen, U. N. \u0026amp; Six, J. Soil biodiversity and human health. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e528\u003c/strong\u003e, 69-76, doi:10.1038/nature15744 (2015).\u003c/li\u003e\n\u003cli\u003eSinsabaugh, R. L., Manzoni, S., Moorhead, D. L. \u0026amp; Richter, A. Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. \u003cem\u003eEcology Letters\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 930-939, doi:10.1111/ele.12113 (2013).\u003c/li\u003e\n\u003cli\u003eBahram, M.\u003cem\u003e et al.\u003c/em\u003e Structure and function of the global topsoil microbiome. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e560\u003c/strong\u003e, 233-237, doi:10.1038/s41586-018-0386-6 (2018).\u003c/li\u003e\n\u003cli\u003eSinsabaugh, R. L., Hill, B. H. \u0026amp; Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e462\u003c/strong\u003e, 795-798, doi:10.1038/nature08632 (2009).\u003c/li\u003e\n\u003cli\u003eLarsson, C., von Stockar, U., Marison, I. \u0026amp; Gustafsson, L. Metabolic uncoupling in Saccharomyces cerevisiae. \u003cem\u003eThermochimica Acta\u003c/em\u003e \u003cstrong\u003e251\u003c/strong\u003e, 99-110, doi:10.1016/0040-6031(94)02055-S (1995).\u003c/li\u003e\n\u003cli\u003eRussell, J. B. \u0026amp; Cook, G. M. Energetics of bacterial growth: Balance of anabolic and catabolic reactions. \u003cem\u003eMicrobiological Reviews\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 48-62, doi:10.1128/mr.59.1.48-62.1995 (1995).\u003c/li\u003e\n\u003cli\u003eWang, H.\u003cem\u003e et al.\u003c/em\u003e Metagenomic insights into inhibition of soil microbial carbon metabolism by phosphorus limitation during vegetation succession. \u003cem\u003eISME Communications\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, doi:10.1093/ismeco/ycae128 (2024).\u003c/li\u003e\n\u003cli\u003eRappuoli, R.\u003cem\u003e et al.\u003c/em\u003e Microbes could help address climate change - why aren\u0026apos;t we using them? \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e639\u003c/strong\u003e, 864-866, doi:d41586-025-00875-w (2025).\u003c/li\u003e\n\u003cli\u003eRanheim Sveen, T., Hannula, S. E. \u0026amp; Bahram, M. Microbial regulation of feedbacks to ecosystem change. \u003cem\u003eTrends in Microbiology\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 68-78, doi:10.1016/j.tim.2023.06.006 (2024).\u003c/li\u003e\n\u003cli\u003eZhang, Z.\u003cem\u003e et al.\u003c/em\u003e Adaptations of soil microbes to stoichiometric imbalances in regulating their carbon use efficiency under a range of different grazing intensities. \u003cem\u003eApplied Soil Ecology\u003c/em\u003e \u003cstrong\u003e193\u003c/strong\u003e, 105141, doi:https://doi.org/10.1016/j.apsoil.2023.105141 (2024).\u003c/li\u003e\n\u003cli\u003eSlessarev, E.\u003cem\u003e et al.\u003c/em\u003e Water balance creates a threshold in soil pH at the global scale. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e540\u003c/strong\u003e, 567-569, doi:doi.org/10.1038/nature20139 (2016).\u003c/li\u003e\n\u003cli\u003eLiang, C., Schimel, J. P. \u0026amp; Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. \u003cem\u003eNature Microbiology\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 17105, doi:10.1038/nmicrobiol.2017.105 (2017).\u003c/li\u003e\n\u003cli\u003eCotrufo, M. F.\u003cem\u003e et al.\u003c/em\u003e Formation of soil organic matter via biochemical and physical pathways of litter mass loss. \u003cem\u003eNature Geoscience\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 776-779, doi:10.1038/ngeo2520 (2015).\u003c/li\u003e\n\u003cli\u003eWang, C.\u003cem\u003e et al.\u003c/em\u003e Aridity threshold in controlling ecosystem nitrogen cycling in arid and semi-arid grasslands. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 4799, doi:10.1038/ncomms5799 (2014).\u003c/li\u003e\n\u003cli\u003eLei, S.\u003cem\u003e et al.\u003c/em\u003e Effect of aridity on the \u0026beta;-diversity of alpine soil potential diazotrophs: insights into community assembly and co-occurrence patterns. \u003cem\u003emSystems\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e01042-01023, doi:10.1128/msystems.01042-23 (2023).\u003c/li\u003e\n\u003cli\u003eLambers, H. Phosphorus Acquisition and Utilization in Plants. \u003cem\u003eAnnual Review of Plant Biology\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 17-42, doi:10.1146/annurev-arplant-102720-125738 (2022).\u003c/li\u003e\n\u003cli\u003eChen, S.\u003cem\u003e et al.\u003c/em\u003e Plant diversity enhances productivity and soil carbon storage. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, 4027-4032, doi:10.1073/pnas.1700298114 (2018).\u003c/li\u003e\n\u003cli\u003eDelgado-Baquerizo, M.\u003cem\u003e et al.\u003c/em\u003e It is elemental: soil nutrient stoichiometry drives bacterial diversity. \u003cem\u003eEnvironmental Microbiology\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1176-1188, doi:10.1111/1462-2920.13642 (2017).\u003c/li\u003e\n\u003cli\u003eMaestre, F. T.\u003cem\u003e et al.\u003c/em\u003e Increasing aridity reduces soil microbial diversity and abundance in global drylands. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 15684-15689, doi:10.6084/ (2015).\u003c/li\u003e\n\u003cli\u003eMooshammer, M.\u003cem\u003e et al.\u003c/em\u003e Decoupling of microbial carbon, nitrogen, and phosphorus cycling in response to extreme temperature events. \u003cem\u003eScience Advances\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, e1602781, doi:10.1126/sciadv.1602781 (2017).\u003c/li\u003e\n\u003cli\u003eGillooly, J. F.\u003cem\u003e et al.\u003c/em\u003e The metabolic basis of whole-organism RNA and phosphorus content. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 11923-11927, doi:10.1073/pnas.0504756102 (2005).\u003c/li\u003e\n\u003cli\u003eLuo, Z.\u003cem\u003e et al.\u003c/em\u003e Microbial synthesis of poly-\u0026gamma;-glutamic acid: current progress, challenges, and future perspectives. \u003cem\u003eBiotechnology for Biofuels\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, doi:10.1186/s13068-016-0537-7 (2016).\u003c/li\u003e\n\u003cli\u003eIsanta‐Navarro, J.\u003cem\u003e et al.\u003c/em\u003e Revisiting the growth rate hypothesis: Towards a holistic stoichiometric understanding of growth. \u003cem\u003eEcology Letters\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 2324-2339, doi:10.1111/ele.14096 (2022).\u003c/li\u003e\n\u003cli\u003eAllison, S. D. Microbial drought resistance may destabilize soil carbon. \u003cem\u003eTrends Microbiol\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 780-787, doi:10.1016/j.tim.2023.03.002 (2023).\u003c/li\u003e\n\u003cli\u003eRohs, P. D. A. \u0026amp; Bernhardt, T. G. Growth and Division of the Peptidoglycan Matrix. \u003cem\u003eAnnual Review of Microbiology\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 315-336, doi:10.1146/annurev-micro-020518-120056 (2021).\u003c/li\u003e\n\u003cli\u003eSong, H.\u003cem\u003e et al.\u003c/em\u003e Mycobacterium tuberculosis Glucosyl-3-Phosphoglycerate Synthase: Structure of a Key Enzyme in Methylglucose Lipopolysaccharide Biosynthesis. \u003cem\u003ePLoS ONE\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, e3748, doi:10.1371/journal.pone.0003748 (2008).\u003c/li\u003e\n\u003cli\u003eRoy, C. R., Salcedo, S. P. \u0026amp; Gorvel, J.-P. E. Pathogen\u0026ndash;endoplasmic-reticulum interactions: in through the out door. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 136-147, doi:10.1038/nri1775 (2006).\u003c/li\u003e\n\u003cli\u003eKuzyakov, Y. \u0026amp; Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. \u003cem\u003eNew Phytologist\u003c/em\u003e \u003cstrong\u003e198\u003c/strong\u003e, 656-669, doi:10.1111/nph.12235 (2013).\u003c/li\u003e\n\u003cli\u003eBarquero, M. B.\u003cem\u003e et al.\u003c/em\u003e Contrasting fertilization and phenological stages shape microbial-mediated phosphorus cycling in a maize agroecosystem. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e951\u003c/strong\u003e, 175571, doi:10.1016/j.scitotenv.2024.175571 (2024).\u003c/li\u003e\n\u003cli\u003eSiles, J. A.\u003cem\u003e et al.\u003c/em\u003e Distribution of phosphorus cycling genes across land uses and microbial taxonomic groups based on metagenome and genome mining. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e \u003cstrong\u003e174\u003c/strong\u003e, 108826, doi:10.1016/j.soilbio.2022.108826 (2022).\u003c/li\u003e\n\u003cli\u003eCotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. \u0026amp; Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. \u003cem\u003eNature Geoscience\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 989-994, doi:10.1038/s41561-019-0484-6 (2019).\u003c/li\u003e\n\u003cli\u003eSalem, H. M., Abdel-Salam, A., Abdel-Salam, M. A. \u0026amp; Seleiman, M. F. in \u003cem\u003eXenobiotics in the Soil Environment: Monitoring, Toxicity and Management\u003c/em\u003e (eds Muhammad Zaffar Hashmi, Vivek Kumar, \u0026amp; Ajit Varma) 267-280 (Springer International Publishing, 2017).\u003c/li\u003e\n\u003cli\u003eWang, L.\u003cem\u003e et al.\u003c/em\u003e Mining bacterial genomes for novel arylesterase activity. \u003cem\u003eMicrobial Biotechnology\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 677-690, doi:10.1111/j.1751-7915.2010.00185.x (2010).\u003c/li\u003e\n\u003cli\u003eWang, C.\u003cem\u003e et al.\u003c/em\u003e Bacterial genome size and gene functional diversity negatively correlate with taxonomic diversity along a pH gradient. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 7437, doi:10.1038/s41467-023-43297-w (2023).\u003c/li\u003e\n\u003cli\u003eDong, Y.\u003cem\u003e et al.\u003c/em\u003e Eco-evolutionary strategies for relieving carbon limitation under salt stress differ across microbial clades. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 6013, doi:10.1038/s41467-024-50368-z (2024).\u003c/li\u003e\n\u003cli\u003eSinsabaugh, R. L.\u003cem\u003e et al.\u003c/em\u003e Stoichiometry of soil enzyme activity at global scale. \u003cem\u003eEcology Letters\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1252-1264, doi:10.1111/j.1461-0248.2008.01245.x (2008).\u003c/li\u003e\n\u003cli\u003eBasan, M.\u003cem\u003e et al.\u003c/em\u003e Overflow metabolism in Escherichia coli results from efficient proteome allocation. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e528\u003c/strong\u003e, 99-104, doi:10.1038/nature15765 (2015).\u003c/li\u003e\n\u003cli\u003eStosiek, N., Talma, M. \u0026amp; Klimek-Ochab, M. Carbon-Phosphorus Lyase\u0026mdash;the State of the Art. \u003cem\u003eApplied Biochemistry and Biotechnology\u003c/em\u003e \u003cstrong\u003e190\u003c/strong\u003e, 1525-1552, doi:10.1007/s12010-019-03161-4 (2020).\u003c/li\u003e\n\u003cli\u003eJing, Z.\u003cem\u003e et al.\u003c/em\u003e Response and feedback of C mineralization to P availability driven by soil microorganisms. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 111-120, doi:10.1016/j.soilbio.2016.11.014 (2017).\u003c/li\u003e\n\u003cli\u003eMendes, G. d. O.\u003cem\u003e et al.\u003c/em\u003e Chemical and Physical Mechanisms of Fungal Bioweathering of Rock Phosphate. \u003cem\u003eGeomicrobiology Journal\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 384-394, doi:10.1080/01490451.2020.1863525 (2021).\u003c/li\u003e\n\u003cli\u003eGude, S.\u003cem\u003e et al.\u003c/em\u003e Bacterial coexistence driven by motility and spatial competition. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e578\u003c/strong\u003e, 588-592, doi:10.1038/s41586-020-2033-2 (2020).\u003c/li\u003e\n\u003cli\u003eKeegstra, J. M., Carrara, F. \u0026amp; Stocker, R. The ecological roles of bacterial chemotaxis. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 491-504, doi:10.1038/s41579-022-00709-w (2022).\u003c/li\u003e\n\u003cli\u003ePiton, G.\u003cem\u003e et al.\u003c/em\u003e Life history strategies of soil bacterial communities across global terrestrial biomes. \u003cem\u003eNature Microbiology\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 2093-2102, doi:10.1038/s41564-023-01465-0 (2023).\u003c/li\u003e\n\u003cli\u003eButler, O. M., Manzoni, S. \u0026amp; Warren, C. R. Community composition and physiological plasticity control microbial carbon storage across natural and experimental soil fertility gradients. \u003cem\u003eThe ISME Journal\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 2259-2269, doi:10.1038/s41396-023-01527-5 (2023).\u003c/li\u003e\n\u003cli\u003eQin, S., Zhang, D., Wei, B. \u0026amp; Yang, Y. Dual roles of microbes in mediating soil carbon dynamics in response to warming. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, doi:10.1038/s41467-024-50800-4 (2024).\u003c/li\u003e\n\u003cli\u003eMooshammer, M.\u003cem\u003e et al.\u003c/em\u003e Adjustment of microbial nitrogen use efficiency to carbon:nitrogen imbalances regulates soil nitrogen cycling. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 3694, doi:10.1038/ncomms4694 (2014).\u003c/li\u003e\n\u003cli\u003eKowalenko, C. G. \u0026amp; and Babuin, D. Use of Lithium Metaborate to Determine Total Phosphorus and Other Element Concentrations in Soil, Plant, and Related Materials. \u003cem\u003eCommunications in Soil Science and Plant Analysis\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 15-28, doi:10.1080/00103624.2013.848884 (2014).\u003c/li\u003e\n\u003cli\u003eWitzgall, K.\u003cem\u003e et al.\u003c/em\u003e Particulate organic matter as a functional soil component for persistent soil organic carbon. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 4115, doi:10.1038/s41467-021-24192-8 (2021).\u003c/li\u003e\n\u003cli\u003eHedley, M. J., Stewart, J. W. B. \u0026amp; Chauhan, B. S. Changes in inorganic and organic soil phosphorus fractions induced by cultivation practices and by laboratory incubation. \u003cem\u003eSoil Science Society Of America Journal\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 970\u0026ndash;975, doi:10.2136/sssaj1982.03615995004600050017x (1982).\u003c/li\u003e\n\u003cli\u003eGuppy, C. N., Menzies, N. W., Moody, P. W., Compton, B. L. \u0026amp; Blamey, F. P. C. A simplified, sequential phosphorus fractionation method. \u003cem\u003eCommunications in Soil Science and Plant Analysis\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1981\u0026ndash;1991, doi:10.1080/00103620009370556 (2000).\u003c/li\u003e\n\u003cli\u003eFierer, N.\u003cem\u003e et al.\u003c/em\u003e Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 21390-21395 (2012).\u003c/li\u003e\n\u003cli\u003eRamirez, K. S.\u003cem\u003e et al.\u003c/em\u003e Biogeographic patterns in below-ground diversity in New York City\u0026apos;s Central Park are similar to those observed globally. \u003cem\u003eProc Biol Sci\u003c/em\u003e \u003cstrong\u003e281\u003c/strong\u003e, doi:10.1098/rspb.2014.1988 (2014).\u003c/li\u003e\n\u003cli\u003eCaporaso, J. G.\u003cem\u003e et al.\u003c/em\u003e QIIME allows analysis of high-throughput community sequencing data. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 335-336, doi:10.1038/nmeth.f.303 (2010).\u003c/li\u003e\n\u003cli\u003eEdgar, R. C. Search and clustering orders of magnitude faster than BLAST. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 2460-2461, doi:10.1093/bioinformatics/btq461 (2010).\u003c/li\u003e\n\u003cli\u003eEdgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. \u003cem\u003eNat Methods\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 996-998, doi:10.1038/nmeth.2604 (2013).\u003c/li\u003e\n\u003cli\u003eQuast, C.\u003cem\u003e et al.\u003c/em\u003e The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, D590-596, doi:10.1093/nar/gks1219 (2013).\u003c/li\u003e\n\u003cli\u003eGuillou, L.\u003cem\u003e et al.\u003c/em\u003e The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, D597-604, doi:10.1093/nar/gks1160 (2013).\u003c/li\u003e\n\u003cli\u003eNguyen, N. H.\u003cem\u003e et al.\u003c/em\u003e FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. \u003cem\u003eFungal Ecology\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 241-248, doi:10.1016/j.funeco.2015.06.006 (2016).\u003c/li\u003e\n\u003cli\u003eLi, D., Liu, C., Luo, R., Sadakane, K. \u0026amp; Lam, T. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1674\u0026ndash;1676, doi:10.1093/bioinformatics/btv033 (2015).\u003c/li\u003e\n\u003cli\u003eFu, L., Niu, B., Zhu, Z., Wu, S. \u0026amp; Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 3150\u0026ndash;3152, doi:10.1093/bioinformatics/bts565 (2012).\u003c/li\u003e\n\u003cli\u003ePatro, R., Duggal, G., Love, M. I., Irizarry, R. A. \u0026amp; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. \u003cem\u003eNature Methods\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 417-419, doi:10.1038/nmeth.4197 (2017).\u003c/li\u003e\n\u003cli\u003eHuerta-Cepas, J.\u003cem\u003e et al.\u003c/em\u003e eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, D309-d314, doi:10.1093/nar/gky1085 (2019).\u003c/li\u003e\n\u003cli\u003eKanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. \u0026amp; Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, D353-d361, doi:10.1093/nar/gkw1092 (2017).\u003c/li\u003e\n\u003cli\u003eBerdugo, M.\u003cem\u003e et al.\u003c/em\u003e Global ecosystem thresholds driven by aridity. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e367\u003c/strong\u003e, 787-790, doi:10.1126/science.aay5958 (2020).\u003c/li\u003e\n\u003cli\u003eBenjamini, Y. \u0026amp; Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. \u003cem\u003eThe Annuals of Statistics\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1165-1188, doi:http://www.jstor.org/stable/2674075 (2001).\u003c/li\u003e\n\u003cli\u003eLiaw, A. \u0026amp; Wiener, M. Classification and regression by randomForest. \u003cem\u003eR News\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 18-22, doi:http://www.stat.berkeley.edu/users/breiman/ (2002).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Soil stoichiometry, Terrestrial ecosystems, Metagenome, Extracellular enzymes, Nutrient cycles","lastPublishedDoi":"10.21203/rs.3.rs-6656511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6656511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAridification is known to disrupt soil biogeochemical cycles by altering the stoichiometry of key elements, such as carbon (C), nitrogen (N) and phosphorus (P). However, how soil microbiome metabolically responds to these imbalances, and the resulting implications for terrestrial ecosystem services, remain virtually unknown. By analyzing soil samples from 200 ecosystems across globally distributed biomes, our results revealed an overall microbial genomic adjustment to elemental imbalances driven by aridity. Genes involved in microbial anabolism were promoted with aridification, whereas those associated with catabolism were suppressed. Thus, while genes encoding extracellular enzymes responsible for the degradation of litter and organic matter decreased, genes related to RNA transcription and the synthesis and transport of intracellular proteins essential for microbial resistance and growth increased. Collectively, our findings highlighted that aridity-driven soil elemental imbalances are strongly associated with shifts in soil microbial metabolism from catabolism to anabolism, reflecting a microbial survival strategy that prioritizes growth over energy and nutrient acquisition under global-scale aridification.\u003c/p\u003e","manuscriptTitle":"Soil microbial genomic responses to aridity-driven elemental imbalance across global biomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 07:44:30","doi":"10.21203/rs.3.rs-6656511/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bf793664-95be-4170-8399-cc12070f9507","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48520532,"name":"Earth and environmental sciences/Ecology/Microbial ecology"},{"id":48520533,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"},{"id":48520534,"name":"Biological sciences/Ecology/Climate-change ecology/Phenology"}],"tags":[],"updatedAt":"2025-12-05T16:31:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 07:44:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6656511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6656511","identity":"rs-6656511","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}