High-emission socioeconomic pathways threaten phoD-harboring bacterial communities in cold ecosystems

High-emission socioeconomic pathways threaten phoD-harboring bacterial communities in cold ecosystems | 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 High-emission socioeconomic pathways threaten phoD-harboring bacterial communities in cold ecosystems Yongping Kou, Lin Xu, Chaonan Li, Xiangzhen Li, Minjie Yao, Bo Tu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6055015/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alkaline phosphatase gene ( phoD ) harboring microbial communities drive organic phosphorus (P) mineralization, regulating plant P availability and ecosystem productivity. However, their global distribution pattern, key environmental drivers, and responses to climate change remain poorly understood. Here, we conducted a meta-analysis of phoD amplicon sequences from 3,175 samples spanning diverse ecosystems worldwide, revealing higher diversity in colder and more arid ecosystems. Climate (temperature, humidity) and pH emerged as key determinants, structuring distinct ecological clusters. Random forest models predicted that under high-emission scenarios (SSP585, + 3.8 to + 8.6°C increment of air temperature), warm-, humid-, and alkaline-associated clusters will expand, while cold-adapted clusters may decline by 84.3%, particularly in vulnerable cold grassland and alpine desert soils. Comparative genomic analysis further revealed higher P-starvation response and inorganic P-solubilization gene frequencies in warm-adapted taxa. These findings provide new insights into the ecological adaptation of phoD -harboring communities and highlight potential disruptions to microbial P cycling under climate change, emphasizing the need for conservation strategies to protect cold-adapted functional microbial communities. Biological sciences/Ecology/Microbial ecology Biological sciences/Ecology/Biogeography Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Biography signifies the diversity and spatial distribution of organisms across various geographic scales, as well as the underlying mechanisms shaping these distributions[ 1 ]. Traditionally, research has primarily focused on the biogeography of plants and animals[ 2 ]. However, over the past two decades, there has been a growing interest in understanding the biogeographic patterns of microbial communities[ 3 ], including bacteria[ 4 , 5 ], archaea[ 6 ], viruses[ 7 ], fungi[ 8 ], protists[ 9 ] and functional microbial taxa. The latter refers to microbial groups categorized based on their involvement in specific biogeochemical processes, such as nitrogen fixation (e.g., nitrogenase-encoding bacteria)[ 10 ] and organic phosphorus (P) mineralization (e.g., alkaline phosphatase-encoding bacteria)[ 11 ]. Previous studies have explored microbial biogeography at both regional and global scales. For instance, a global survey mapped the worldwide distribution of dominant soil bacterial taxa, identifying soil pH as the primary determinant of bacterial community composition[ 4 ]. This survey also revealed that Ascomycota dominate global soil fungal communities[ 8 ], while soil protists are primarily composed of consumers[ 9 ]. Unlike bacterial communities, however, fungal and protistan distributions are largely driven by precipitation[ 8 , 9 ]. In addition, meta-analyses have assessed the influence of global change factors on microbial diversity and the abundance of major microbial taxa at a global scale[ 12 , 13 ]. However, these studies primarily focused on taxonomic diversity, overlooking microbial community structure and assembly processes. To address this limitation, researchers have proposed integrating sequencing data from independent studies to investigate microbial community dynamics at broader spatial scales[ 14 , 15 ]. While these findings offer important ecological insights, a critical knowledge gap remains: the global biogeography of functional microbial communities has not been systematically examined. It remains unclear whether the distribution patterns and key drivers of functional microbial taxa resemble those of total bacteria, fungi, and protists, and how these communities will respond to climate change. This gap constrains our understanding of microbial functional capacities on a global scale. Orthophosphate, the only bioavailable form of P for plants and microbes, is a key limiting factor for primary productivity in terrestrial and freshwater ecosystems[ 16 , 17 ]. To alleviate P scarcity, extracellular phosphatases catalyze the hydrolysis of organic P into orthophosphate [ 18 , 19 ]. These enzymes include acid and alkaline phosphatases, with alkaline phosphatases—mainly of microbial origin—exhibiting broader substrate specificity and higher catalytic efficiency[ 18 , 20 ]. Among the genes encoding alkaline phosphatases ( phoA , phoD , and phoX ), phoD is widely used as a biomarker for functional microbial communities involved in organic P mineralization due to its high abundance across diverse environments[ 20 – 23 ]. High-throughput sequencing has significantly improved our understanding of phoD -harboring microbial diversity and ecology at local and regional scales. For example, phoD -harboring Streptomyces and Nocardiopsis dominate agricultural soils[ 23 , 24 ], while Bradyrhizobium and Streptomyces are highly abundant in Chinese grassland soils[ 11 , 21 ]. Similarly, Nostoc and Gloeocapsa dominate desert biocrusts[ 22 ], whereas Rhodoplanes and Bradyrhizobium are prevalent in subalpine forest soils[ 20 ]. In aquatic ecosystems, such as freshwater lakes, Cobetia and Calothrix are the dominant phoD -harboring taxa[ 25 ]. These studies have identified various environmental factors, such as precipitation[ 22 , 24 ], soil C:P or N:P ratios[ 20 , 23 ], and soil pH[ 21 ], as key drivers of phoD -harboring community distributions across ecosystems. However, despite these advances, there remains an urgent need to investigate functional microbial communities at larger spatial scales, particularly through integrative analyses spanning from genes to global ecosystems[ 26 , 27 ]. A systematic, global assessment of the diversity, taxonomy, ecology, and distribution of phoD -harboring microbial communities—and their responses to climate change—is still lacking. Climate change, particularly rising air temperatures, is expected to restructure microbial communities, including phoD -harboring taxa[ 28 ]. Understanding the global distribution of this functional community and its response to climate change is essential for predicting shifts in plant P availability, primary productivity, and ecosystem carbon storage. Moreover, investigating the adaptive strategies of phoD -harboring communities in response to climate change will enhance our ability to model future ecosystem dynamics. To elucidate the environmental drivers of microbial biogeography and the mechanisms governing biodiversity and coexistence, previous studies have suggested focusing on a subset of dominant taxa rather than attempting to analyze the entire microbial community[ 4 , 8 ]. This approach simplifies microbial complexity by identifying abundant and widespread taxa that serve as proxies for the broader community. Similarly, when analyzing the global distribution of phoD-harboring communities, prioritizing dominant taxa may reduce computational resource demands while still providing valuable insights. For example, certain phoD -harboring taxa, such as Streptomyces and Bradyrhizobium , are highly abundant across diverse ecosystems despite significant spatial separation[ 11 , 20 , 21 , 23 ]. Furthermore, recognizing that microbial taxa exhibit distinct environmental preferences, researchers have proposed clustering them into ecologically meaningful groups based on shared habitat characteristics[ 4 , 5 , 8 ]. Applying this clustering strategy to phoD -harboring communities may facilitate a more mechanistic understanding of their global distribution patterns. In this study, we conducted a comprehensive meta-analysis, integrating phoD amplicon sequencing data from 60 published studies, encompassing 3,175 samples from diverse habitats—including sediments, farmlands, deserts, grasslands, and forests. This large-scale dataset allowed us to reanalyze phoD -harboring community distributions worldwide. Specifically, we (i) explored the biogeographic patterns and environmental drivers of phoD -harboring communities, (ii) characterized the taxonomic composition of dominant phoD -harboring phylotypes, (iii) classified dominant phoD -harboring phylotypes into ecological clusters based on key environmental factors, and assessed their genomic potential by mapping representative sequences to the Genome Taxonomy Database (GTDB) to estimate the abundance of key P-cycling genes, and (iv) predicted the relative abundance shifts of these clusters under future climate scenarios, i.e., under sustainable (SSP126, + 0.6 to + 1.8°C) and high-emission (SSP585, + 3.8 to + 8.6°C) socioeconomic pathways. We hypothesized that (i) climate variables (e.g., temperature and humidity), rather than local habitat conditions, are the primary determinants of phoD -harboring community distributions, as climate influences plant biomass and, consequently, the organic matter available for microbial P mineralization; (ii) Proteobacteria (e.g., Bradyrhizobium ) and Actinobacteria (e.g., Streptomyces ) would represent the most dominant phoD -harboring taxa on a global scale; (iii) taxa adapted to warm climates harbor higher abundances of P-cycling genes than those in cold environments due to greater microbial metabolic activity; and (iv) future climate change will favor the expansion of warm- and humid-adapted phoD -harboring taxa while suppressing cold- and arid-adapted clusters. Results Diversity patterns and key drivers of phoD -harboring communities To gain a comprehensive understanding of the entire phoD -harboring community, we examined its diversity patterns and identified the key environmental drivers. The α-diversity of phoD -harboring communities varied significantly across different habitats, with greater species richness observed in colder and more arid environments (Supplementary Fig. S2). For example, phoD richness was higher in cold grasslands compared to temperate grasslands (Supplementary Fig. S2a–b), indicating the dominant influence of climate on diversity patterns. Random forest models further corroborated this, explaining over 62% of the variance in α-diversity and identifying temperature (mean annual temperature, MAT), humidity (aridity index, AI), and habitat pH as the key drivers. Specifically, α-diversity exhibited negative correlations with temperature and humidity but a positive correlation with pH (Fig. 1 a–d). Similarly, β-diversity, which reflects community compositional differences, varied significantly among ecosystem types (Supplementary Fig. S2c). To cross-validate the key factors shaping β-diversity, we employed both random forest and distance-based redundancy analysis (db-RDA). These models explained up to 87% and 61% of the variance in β-diversity, respectively, further reinforcing the dominant role of temperature, humidity, and pH (Fig. 1 e–g). Distinct separation of phoD -harboring communities was observed along these environmental gradients, with cold, arid, and alkaline samples clustering on the right, whereas warm, humid, and acidic samples clustered on the left of the db-RDA plot (Fig. 1 h–j). Taxonomic composition and key determinants of dominant phoD -harboring phylotypes To distill meaningful insights from the extensive diversity of phoD -harboring taxa—comprising 669,521 OTUs across 3,115 samples—we focused on a subset of widespread species that provide a representative snapshot of the entire community. In total, 19,194 dominant phoD -harboring phylotypes were identified in more than 100 samples (Fig. 2 a, Supplementary Fig. S3). Although these phylotypes accounted for only ~ 3% of the total, they represented approximately 57% of the overall abundance (Fig. 2 b), closely mirroring the diversity patterns observed across the entire phoD -harboring community (Supplementary Fig. S4). The dominant phylotypes were primarily affiliated with the phyla Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, and Planctomycetes (Fig. 2 c). The most abundant genera included Bradyrhizobium , Bacillus , Pseudomonas , Nostoc , Streptomyces , Paludisphaera , Frankia , Gloeocapsa , Gloeobacter , and Pirellula (Supplementary Fig. S5a). The relative abundances of these key phoD -harboring taxa exhibited trends similar to α-diversity, showing negative correlations with temperature and humidity but a positive correlation with pH (Fig. 2 d–f). Moreover, their distribution across ecosystem types paralleled α-diversity patterns, with higher relative abundances observed in colder and more arid environments (Supplementary Fig. S5b). Our random forest models consistently highlighted temperature as the most influential factor driving the relative abundances of dominant phoD -harboring phylotypes, followed by humidity and pH. This finding remained robust regardless of whether we assessed variable importance based on the mean increase in MSE% or the frequency of a factor being the best predictor (Supplementary Fig. S6). Ecological clusters of dominant phoD -harboring phylotypes To further refine our understanding of how environmental factors structure phoD -harboring communities, we conducted semi-partial correlation and clustering analyses, which revealed six primary ecological clusters based on habitat preferences for temperature, humidity, and pH (Supplementary Table S3, Fig. S7). These included cold and warm clusters (temperature), arid and humid clusters (humidity), and acidic and alkaline clusters (pH) (Fig. 3 a).The relative abundances of phylotypes within these ecological clusters exhibited distinct environmental responses: cold-cluster taxa declined monotonically with increasing temperature, peaking at MAT ≈ -4 to 0°C; warm-cluster taxa followed a unimodal distribution, peaking at MAT ≈ 16 to 19°C; arid-cluster taxa showed a negative correlation with humidity, peaking at AI ≈ 0.1 to 0.3.Humid-cluster taxa exhibited a unimodal relationship with AI, peaking at AI ≈ 1.2 to 1.4; acidic-cluster taxa declined monotonically with increasing pH, peaking at pH ≈ 4 to 5; alkaline-cluster taxa followed a unimodal pattern, peaking at pH ≈ 7.5 to 8.5 (Fig. 3 a). To validate these ecological clusters, we performed co-occurrence network analyses, which revealed that phoD -harboring taxa with similar habitat preferences were more likely to co-occur within the same ecological cluster, as indicated by distinct clustering patterns in the co-occurrence network (Fig. 3 b). Additionally, they were likely to co-occur more than expected by chance (Supplementary Fig. S8). Each ecological cluster exhibited distinct phylum-level composition, with Actinobacteria and Cyanobacteria being more prevalent in cold and arid clusters, whereas Proteobacteria dominated in warm and humid clusters (Supplementary Fig. S9). Variations in P-cycling genes across ecological clusters To assess whether different ecological clusters harbor distinct P-cycling potentials, we examined the genomic attributes of the dominant phylotypes within each cluster. However, genomic data were available for only a subset of these phylotypes: 4 out of 1,664 phylotypes (4/1,664) for the cold cluster, 25/419 for the warm cluster, 16/1,051 for the arid cluster, 14/207 for the humid cluster, 6/92 for the acidic cluster, and 9/411 for the alkaline cluster (Supplementary Fig. S10). By classifying P-cycling genes into four major categories based on microbial P metabolic pathways (Supplementary Table S4), we found that warm-cluster taxa exhibited significantly higher frequencies of genes involved in P-starvation response regulation and inorganic P solubilization compared to cold-cluster taxa (Fig. 4 ). However, no significant differences were observed in P-cycling gene frequencies between humid vs. arid clusters or alkaline vs. acidic clusters (Fig. 4 ). Projected shifts in ecological clusters under future climate scenarios Finally, we investigated whether future climate change would significantly alter the relative abundances of these ecological clusters. Model validation showed strong correlations between observed and predicted relative abundances (Spearman’s rho ≥ 0.77, Supplementary Fig. S11), demonstrating the reliability of our prediction models. Under the sustainable (SSP126; +0.6 to + 1.8°C) scenario, no significant changes were predicted for any ecological cluster. However, under the high-emission (SSP585; +3.8 to + 8.6°C) scenario, substantial shifts were projected by the end of the century: cold-cluster taxa are expected to decline by 84.3%; acidic-cluster taxa are projected to decrease by 13.3%; warm-cluster taxa are predicted to increase by 14.0%, humid-cluster taxa by 6.0%, and alkaline-cluster taxa by 36.3%. The magnitude of these changes varied by ecosystem type. For example, warm-cluster taxa were predicted to increase by 500% in arid forests, whereas cold-cluster taxa were projected to decline by 43.8% in cold grasslands and 95.5% in deserts (Supplementary Fig. S12–17).These findings suggest that future climate change will favor warm- and humid-adapted phoD -harboring taxa while suppressing cold- and arid-adapted taxa, with potential consequences for soil P cycling and ecosystem nutrient dynamics. Discussion The ecological theory of diversity and biogeography, well-established through studies on the global distribution of plants and animals[ 2 ], prokaryotes[ 4 ], fungi[ 8 , 15 ] and protists[ 9 ], has provided a foundation for biodiversity conservation and management. However, the global distribution of functional microbial communities remains largely unexplored, leaving a critical knowledge gap in understanding whether these communities follow similar biogeographical patterns as total prokaryotes and eukaryotes, and how climate change may reshape them. Given the challenges of conducting a global-scale survey spanning diverse ecosystem types, climates, and continents, we addressed this gap by re-analyzing amplicon phoD sequence data from 60 studies across various biogeographical regions, compiling a comprehensive dataset of phoD -harboring microbial communities. Despite this effort, our understanding of the global phoD -harboring community distribution remains limited, particularly due to the scarcity of data from the Americas and Africa (Supplementary Fig. S1 ). The underrepresentation of tropical ecosystems further restricts our ability to construct a comprehensive global map of phoD diversity and distribution. Moreover, methodological biases introduced by different primer sets[ 29 ] may have influenced the amplification of specific taxonomic groups. Given the relatively small number of samples for each primer set, we combined both datasets to maximize coverage. However, future studies integrating data from underrepresented regions and employing a unified primer approach will be essential to refine global distribution models of phoD diversity and ecological clusters. Despite these limitations, this study provides valuable insights into the global distribution and environmental drivers of phoD -harboring communities, offering a foundation for their informed management and the optimization of their functional potential in diverse ecosystems. By elucidating the environmental drivers shaping the distribution of phoD -harboring communities, this study enhances our ability to predict and mitigate the impacts of global change on ecosystem processes governed by this functional microbial communities. Our findings consistently underscore the critical role of climate variables and habitat pH in structuring both the overall diversity patterns and the relative abundances of dominant phylotypes, highlighting their fundamental influence on the biogeographical distribution of this functional community (Fig. 1 , Supplementary Fig. S6). Unlike total prokaryotic communities, which are predominantly shaped by soil pH [ 4 ], and eukaryotic communities, which are largely driven by climate variables[ 8 , 9 ], phoD -harboring communities are simultaneously regulated by both climatic and edaphic factors. This dual dependence suggests that these microbial communities may be more vulnerable to climate change than total prokaryotic and eukaryotic communities. Two key mechanisms may explain this observation: (1) as bacteria, phoD -harboring taxa are influenced by soil pH that mediates global distribution soil bacteria, and (2) their primary function—organic P mineralization—relies on plant and animal detritus, whose availability is governed by temperature and water dynamics [ 8 , 30 , 31 ]. These findings have significant implications, suggesting that future climate change and increasing landscape fragmentation could potentially alter dominance hierarchies in phoD -harboring communities and consequently impact their ecological functions. Considering temperature as the primary determinant of this functional community, we assessed how rising air temperatures may reshape their global distribution. Our predictive models suggest that a rise in air temperatures of 0.6–1.8°C under sustainable-emission scenarios (SSP126) would generally have minimal impact on this functional community. However, under high-emission scenarios (SSP585), as air temperatures increase by 3.8–8.6°C, the relative abundances of warm, humid, and alkaline clusters are projected to rise by 14.0%, 6.0%, and 36.3%, respectively. In contrast, cold clusters are particularly vulnerable, with only 15.7% of their original population expected to persist by the end of the century (Fig. 5 ). Three mechanisms may help explain these shifts under increasing air temperatures. First, warming adaptation: increasing temperatures favor microbial taxa adapted to warmer conditions (warm-cluster), while cold-cluster taxa face habitat loss as protective ice layers thaw[ 28 ]. Second, temperature-humidity-pH feedback: combined warming and moisture increases enhance global soil alkalinity[ 32 ], promoting alkaline-cluster dominance (Fig. 5 ). Third, P-cycling gene trade-offs: cold-cluster taxa harbor fewer genes related to P-starvation response and inorganic P solubilization (Fig. 4 ), limiting their adaptability under warming conditions[ 27 ]. As climatic zones shift northward, high-altitude regions warm, and soil moisture increases, inorganic P cycling may intensify in cold habitats, while organic P mineralization could decline[ 33 ], potentially disrupting P cycling dynamics. The decline of cold-cluster phoD -harboring taxa in highly sensitive ecosystems—such as alpine deserts, arctic regions, and cold grasslands—raises ecological concerns, as their recovery may be highly challenging. Moreover, permafrost thawing could accelerate soil warming[ 34 ], further amplifying these shifts. At the taxonomic level, the responses of key microbial phyla (e.g., Proteobacteria, Actinobacteria, and Acidobacteria) to warming remain complex, depending on habitat-specific conditions[ 35 , 36 ]. Our results indicate that while certain phyla are enriched in distinct ecosystems and ecological clusters, most phyla occur across multiple ecosystem (or) clusters (Fig. 2 , Supplementary Figs. S5 and S9), suggesting that at coarse taxonomic (phyla) levels, habitat preferences cannot be predicted solely by phylogeny. Instead, functional traits and adaptation strategies likely dictate microbial resilience under climate change. For example, genera such as Nostoc (Cyanobacteria) and Streptomyces (Actinobacteria) are widely distributed in stressful environments like drylands and cold grasslands[ 37 , 38 ]. These taxa have evolved diverse strategies to withstand environmental stressors, including temperature fluctuations, UV radiation, nutrient scarcity, and osmotic stress. Notably, Nostoc and Streptomyces produce cold- and heat-shock proteins, enabling survival under extreme temperatures[ 39 , 40 ]. Moreover, representative genera such as Bradyrhizobium , Nostoc , and Streptomyces possess nitrogen fixation capabilities[ 41 ]. These adaptive traits likely contribute to the success of species belonging to these genera in stressful habitats and highlight their ecological significance in nutrient cycling and ecosystem functioning. Our findings underscore that climate change may fundamentally restructure global phoD -harboring communities, promoting the expansion of warm, humid, and alkaline-associated taxa while imposing significant threats to cold-adapted phoD -harboring communities. The projected loss of these psychrophilic taxa, particularly in ecologically fragile cold environments, highlights an urgent need for strategies to mitigate climate change impacts on functional microbial groups. Given the central role of phoD -harboring communities in organic phosphorus mineralization, these shifts could have cascading effects on nutrient cycling and ecosystem stability. Future climate policies should prioritize measures to limit global temperature rise, particularly in regions where functional microbial groups are key players in biogeochemical processes. Methods Literature search To compile a comprehensive dataset of phoD -harboring microbial communities analyzed via next-generation sequencing, we conducted a literature search in Web of Science (Core Collection) and Google Scholar between October 15 and December 15, 2024. The search keywords included: TS = (“phoD” OR “alkaline phosphatase” OR “phoD-harb” OR “phoD harb” OR “phosphorus minera*” OR “P mineral*” OR “phosphorus transformation” OR “P transformation”) AND TS = (“amplicon” OR “sequencing” OR “high-throughput” OR “next generation”) AND TS = (“soil” OR “sediment”). A total of 369 publications were initially retrieved. Studies were filtered based on the following criteria: (1) samples must be collected in situ, excluding those subjected to incubation experiments (except for control samples without treatments); (2) only soil and sediment samples were considered to ensure comparability of environmental data. (3) precise geographic coordinates must be provided; (4) phoD-harboring bacterial communities must have been identified via amplicon sequencing; (5) raw sequence data must be publicly available or obtainable from the authors; (6) sequences must be accurately assigned to specific samples. Additionally, we incorporated four of our own datasets, covering alpine forests, Chinese grasslands across the Qinghai-Tibet Plateau, and biocrusts from five Chinese deserts (Supplementary Table S1 ). In total, 60 studies met our selection criteria, yielding 3,175 samples from 870 sites, comprising 327 sediment samples and 2,848 soil samples (Supplementary Fig. S1 , Tables S1-S2). Data extraction Land-use types were recorded based on descriptions provided in each study. Climate classifications were assigned according to the Köppen-Geiger climate classification [ 42 ] using geographic coordinates. Ecosystem types were defined based on a combination of Köppen climate zones[ 43 ] and land use types. Specifically, arid, cold, and temperate croplands were grouped as croplands, while polar ecosystems included all land-use types classified under polar climates. Climate variables, including mean annual temperature (MAT) and mean annual precipitation (MAP), were extracted from WorldClim (v2.1) [ 44 ], while the aridity index (AI) was obtained from the Global Aridity Index Database [ 45 ] at a resolution of 30 arcseconds. AI was used as a proxy for potential water availability, as it accounts for evapotranspiration effects [ 45 ], making it a more reliable metric than MAP. Soil properties such as pH, total organic carbon (TOC), and total nitrogen (TN) were recorded. Given the study’s focus on global patterns of phoD-harboring communities, we also extracted phosphorus-related parameters, including total phosphorus (TP), available phosphorus (AP), and the C:P ratio. These values were obtained directly from tables and datasets or extracted from figures using GetData Graph Digitizer (v2.24). For sediment samples, pH, TOC, TN, and TP were recorded as well. If environmental data were reported as means and standard deviations (SD), we generated sample-level values using an R function (“acquire_env”) that simulates normally distributed random data based on specified means, SDs, and sample sizes (see Code Availability). When studies reported standard errors (SE) instead of SD, we converted SE to SD using SD = SE × √n [ 24 ]. The final compiled dataset of environmental variables is available in Supplementary Table S2. Sequencing data procession The phoD sequences analyzed in this study were primarily generated using two primer sets: ALPS-F733/R1083[ 46 ] and ALPS-F730/R1101[ 47 ]. Despite potential primer biases, both datasets were included due to their comparable amplicon lengths (~ 370 bp), maximizing sample retention. Bioinformatics processing was conducted using Quantitative Insights into Microbial Ecology 2 (QIIME2)[ 48 ]. Initial quality control was performed using the “quality-filter” plugin with default settings, resulting in 217,098,720 high-quality sequences across 3,175 samples. Chimeric sequences were removed using VSEARCH [ 49 ] plugin in denovo mode. Additionally, RDP FRAMEBOT[ 50 ] was used to correct frameshift mutation of phoD -encoding sequences. Given potential variations in phoD sequences due to distinct primers and sequencing platforms across studies, instead of using denoising algorithm to generate amplicon sequence variants (ASVs), we clustered all high quality phoD sequences into Operational Taxonomic Units (OTUs, here represents the phylotypes) at a similarity threshold of 97% by VSEARCH, thus minimizing the potential impact of primers and sequencing platforms on phoD -harboring community diversity[ 24 ]. Singletons with fewer than 10 total reads were removed. For diversity estimation, all samples were rarefied to 1,000 sequences per sample, leading to the exclusion of 78 samples with insufficient reads. Taxonomic assignments for phoD-harboring phylotypes were performed using the RDP FunGene database[ 51 ]. Diversity and community composition analyses of phoD-harboring communities A To assess α-diversity, we calculated Chao1 and Shannon indices using the “diversity alpha” plugin in QIIME2[ 48 ]. Community compositional differences were examined via nonmetric multidimensional scaling (NMDS), based on rarefied OTU tables and Bray-Curtis dissimilarity matrices. NMDS dimensions was obtained using the “metaMDS” function in the vegan R package[ 52 ]. To identify key environmental drivers, we employed random forest models[ 53 ] to assess the influence of environmental variables and ecosystem types on both α- and β-diversity patterns. The importance of each variable was evaluated by randomly permuting its values and calculating the percent increase in mean squared error (MSE), where a higher MSE% indicated greater model significance. Ecosystem types were treated as categorical variables with nine levels, allowing comparisons of their influence on phoD-harboring community composition. Random forest analyses were conducted using the “rfPermute” R package [ 54 ]. To further validate the results, we performed distance-based redundancy analysis (db-RDA) using Bray-Curtis dissimilarity matrices, with variable importance assessed via the “rdacca.hp” function in the rdacca.hp package[ 55 ]. Differences in α-diversity among ecosystem types were tested using non-parametric Kruskal-Wallis analyses. Additionally, we constructed linear, quadratic, and logarithmic models to evaluate the precise relationships between α-diversity and key environmental variables. The three most important predictors (ranked by MSE%) were used as independent variables, and the model with the lowest AIC was selected for final interpretation. Identification of dominant phoD -harboring phylotypes According to the ubiquity distribution of the phylotypes (Supplementary Fig. S3 and Table S3), we identified the most prevalent and widespread phoD -harboring phylotypes across the dataset by retaining those found in more than 100 of the 3,115 samples[ 15 ]. In total 19,194 phylotypes were identified as dominant phoD -harboring taxa (Supplementary Table S3). These phylotypes were widely distributed across samples and can be considered reasonably ubiquitous. Differences in the relative abundances of major groups of dominant phoD -harboring phylotypes among ecosystem types were also estimated using non-parametric Kruskal-Wallis analyses. Determining habitat preferences of dominant phoD -harboring phylotypes We used random forest models to determine the environmental preferences of dominant phoD-harboring phylotypes. Predictors included MAT, AI, pH, TOC, TN, TP, AP, and C:P ratio, while the response variable was phylotype relative abundance. In total, 19,194 random forest models were established, with predictive power defined as > 30% explained variance, following previous studies [ 4 ]. Subsequently, Spearman’s rho-based semi-partial correlations were used to identify the unique contribution of each predictor in elucidating the distribution of phylotypes exhibiting more than 30% random forest explanatory power. These semi-partial correlations were performed using the “ppcor” R package[ 56 ]. Unlike standard correlations, semi-partial correlations assess the relationship between a given response variable (e.g., phylotype relative abundance) and a specific predictor while controlling for the effects of all other variables [ 57 ]. Dominant phoD phylotypes exhibiting significant semi-partial rho values (P < 0.05) were used to define habitat clusters, where the strongest predictor (highest |rho|) determined cluster labels. Clusters containing ≥ 30 phylotypes were considered ecologically meaningful and visualized via heatmaps (Supplementary Fig. S7). Construction of co-occurrence network of dominant phoD -harboring phylotypes To examine the co-occurrence patterns of dominant phoD-harboring phylotypes, we constructed co-occurrence networks based on Spearman’s rank correlations. Prior to network construction, phylotype relative abundances were Min-Max standardized to ensure comparability across ecological clusters. Network edges were defined using Spearman’s rho ≥ 0.65 with FDR-adjusted P < 0.001. Network construction was performed in the “igraph” R package[ 58 ] and visualized using Gephi ( https://gephi.org ). To assess whether phylotypes co-occurred more frequently than expected by chance, we generated 1,000 null-model networks using the Erdős–Rényi model, preserving node and edge counts. Comparing whole genome attributes to identify differences in P-cycling genes of dominant phylotypes among ecological clusters To evaluate differences in P-cycling gene content among ecological clusters, we matched the phoD sequences of dominant phylotypes against the Genome Taxonomy Database (GTDB, Release 214.1)[ 59 ]. Out of the 19,194 dominant phylotypes, the phoD sequences of 310 phylotypes matched those in GTDB (Supplementary Table S3). Genomes were selected based on a 97% identity threshold and a 90% coverage threshold, resulting in a total of 216 unique genomes being selected (Supplementary Table S3). However, only 74 out of the 216 genomes were precisely associated with phylotypes falling into specific ecological clusters. To annotate P-cycling genes, we used DIAMOND software to search protein sequences against PCycDB, a curated database of microbial P-cycling genes[ 60 ]. Based on specific metabolic processes related to P-cycling, we classified the P-cycling genes into four main groups: P starvation response regulation, inorganic P solubilization, organic P mineralization, and P transportation. Detailed KO numbers, functional descriptions, and metabolic pathways of P-cycling genes are provided in Supplementary Table S4. To compare gene abundances among clusters with contrasting environmental preferences (e.g., warm vs. cold clusters), we computed log2 fold-changes using the “DESeq2” R package[ 61 ]. The complete gene frequency dataset is available in Supplementary Table S5. Predicting relative abundances of ecological clusters under climate change To assess the potential impact of climate change on the relative abundances of phoD-harboring ecological clusters, we obtained future climate projections from WorldClim (v2.1). Specifically, climate data of two scenarios, namely sustainable (SSP126) and high-emission (SSP585) socioeconomic pathways and four periods (2021 ~ 2040, 2041 ~ 2060, 2061 ~ 2080, 2081 ~ 2100) were extracted considering the BCC-CSM2-MR climatic model from Beijing Climate center[ 62 ]. The estimated increments of air temperature regarding the two selected socioeconomic pathways were 0.6 to 1.8 ℃ for SSP126, and 3.8 to 8.6 ℃ for SSP585 by the end of this century[ 63 ]. To predict future shifts in ecological cluster abundances, we constructed six random forest models, treating the relative abundances of ecological clusters as response variables and current climate and environmental factors as predictors. Model training was performed using the “train” function in the caret R package, with 70% of the samples used for training and 30% reserved for testing. Future relative abundances of ecological clusters were predicted by applying these models to future climate projections while keeping other environmental factors constant. The random forest models were run separately for each SSP scenario and time period to capture scenario-specific and temporal dynamics. Differences in the predicted relative abundances of ecological clusters across time periods were assessed using non-parametric Kruskal-Wallis tests. Declarations Data availability All data used in this study are publicly available. The sources of phoD amplicon sequences are listed in Supplementary Table S1, while the corresponding environmental variables are detailed in Supplementary Table S2. The OTU table generated in this study has been deposited on Figshare and can be accessed at https://doi.org/10.6084/m9.figshare.25706067. Code availability All Shell and R scripts used in this study are available on GitHub at: https://github.com/YangYanghei0818/Codes_availability_Xu_2025_GlobalphoD. Acknowledgements This work was supported by the National Natural Science Foundation of China [32100076, 42477117]; China Postdoctoral Science Foundation [2023M742511]; Sichuan Science and Technology Program [2023NSFSC1191, 2023NSFSC1165, 2024YFNH0028]; and Talent-Recruiting Program of Sichuan Agricultural University [2222996049]. Opinions expressed in this paper are those of the authors and not necessarily of authors’ affiliations. Author contributions LX and YPK conceived the project. LX, YPK, CNL and BT collected data. LX, CNL and BT carried out bioinformatic and statistical analyses. LX , YPL and CNL prepared the original draft. LX and XZL acquired funding. All authors read, edited, and approved the final manuscript. Competing interests The authors declare no competing interests. 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Supplementary Files 2501Supplementarydata1TableS1S6.xlsx Supplementary Dataset-Supplementary tables 2502SupplementaryInformation.pdf Supplementary Information1 NCOMMS2512926rs.pdf Reporting Summary Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-6055015","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":417716728,"identity":"c9185e06-a9fd-44ba-8e11-3e1981e0e8ec","order_by":0,"name":"Yongping Kou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYBACCTBZwcBjQKKWMyRrYWxjYCBei2R7j5l04Tw7GXMG5ocfGGruENYizXPGTHrmtmQeywY2YwmGY88Ia5GTyDGT5t12gMfgAIMZA2PDYWK1zAFpYf9GnBZpsJYGkBYeIm2R7DlWbD3jGNAvzTzFEgnHiNAicbx54+2CGjt7c/b2jR8+1BChBQhYpMEUMxAnEKUBqPYzkQpHwSgYBaNgpAIA44gvh0xfjXIAAAAASUVORK5CYII=","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":true,"prefix":"","firstName":"Yongping","middleName":"","lastName":"Kou","suffix":""},{"id":417716729,"identity":"476293b1-84c5-4978-b356-69efc1958398","order_by":1,"name":"Lin Xu","email":"","orcid":"","institution":"Chengdu Institute of Biology","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xu","suffix":""},{"id":417716730,"identity":"7d43a75b-a3da-44dc-a319-3835beb6968e","order_by":2,"name":"Chaonan Li","email":"","orcid":"","institution":"Mianyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Chaonan","middleName":"","lastName":"Li","suffix":""},{"id":417716731,"identity":"8cc38175-9c23-43fb-9aa6-f4c503849ac2","order_by":3,"name":"Xiangzhen Li","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xiangzhen","middleName":"","lastName":"Li","suffix":""},{"id":417716732,"identity":"751f9e1b-be1c-43a1-80b3-0166036edade","order_by":4,"name":"Minjie Yao","email":"","orcid":"https://orcid.org/0000-0001-7501-0519","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Minjie","middleName":"","lastName":"Yao","suffix":""},{"id":417716733,"identity":"beb5ae1e-fc56-425b-8e8b-ca427159e489","order_by":5,"name":"Bo Tu","email":"","orcid":"","institution":"Key Laboratory of Environmental and Applied Microbiology, CAS; 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TOC, soil (or sediment) total organic carbon; TN, soil (or sediment) total nitrogen; TP, soil (or sediment) total phosphorus, AP soil (or sediment) available phosphorus. Significance levels for MSE% in the random forest models: ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003eo\u003c/sup\u003e \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.1.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/e54e6ff9f15d92767f102ec7.png"},{"id":78322240,"identity":"8356a66d-b04b-4d11-8d23-008333ca16e8","added_by":"auto","created_at":"2025-03-12 05:27:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxonomic composition of dominant phoD-harboring phylotypes. a\u003c/strong\u003eProportion of dominant vs. other \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes based on phylotype counts.\u003cstrong\u003e b\u003c/strong\u003e Relative abundances of dominant vs. non-dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes. \u003cstrong\u003ec\u003c/strong\u003e Representative phyla of dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes.\u003cstrong\u003e d–f \u003c/strong\u003eLinear relationships between the relative abundances (%) of the three most dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phyla and key environmental predictors identified by random forest and db-RDA models. Detailed taxonomic information on dominant \u003cem\u003ephoD\u003c/em\u003e-harboring communities is provided in Supplementary Table S3.\u003c/p\u003e","description":"","filename":"Figure2apieplot.png","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/f0acbce45e665d3269f52955.png"},{"id":78322561,"identity":"ada6ffe8-80a2-40c9-ac53-86e2e481dd52","added_by":"auto","created_at":"2025-03-12 05:35:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHabitat preferences of dominant\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e phoD\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-harboring phylotypes. a \u003c/strong\u003eRelationships between the relative abundance (min-max standardized) of the phylotypes within each ecological cluster and their primary environmental predictors. \u003cstrong\u003eb\u003c/strong\u003eCo-occurrence networks with nodes (dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes) colored by each of the six well-identified ecological clusters with more than 30 phylotypes.\u003c/p\u003e","description":"","filename":"Figure3Cluster.png","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/684d29dbe8f5853322c744eb.png"},{"id":78322560,"identity":"5a069ef6-6688-4068-88b1-72f215a51209","added_by":"auto","created_at":"2025-03-12 05:35:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in P-cycling gene frequencies among genomes assigned to distinct ecological clusters. \u003c/strong\u003eLog\u003csub\u003e2\u003c/sub\u003e fold changes in gene frequencies for \u003cem\u003ephoD\u003c/em\u003e-harboring genomes associated with warm vs. cold, humid vs. arid, and alkaline vs. acidic clusters. Bars represent log\u003csub\u003e2\u003c/sub\u003e fold changes in genes related to P-starvation response regulation, inorganic P solubilization, organic P mineralization, and P transportation. Detailed gene classifications and their frequencies are provided in Supplementary Tables S4 and S5, respectively. Significance levels for log\u003csub\u003e2 \u003c/sub\u003efold changes: ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003ens\u003c/sup\u003e \u003cem\u003eP \u003c/em\u003e\u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"Figure4Pcycgenedifference.png","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/652354d8d7e790d60b154cb9.png"},{"id":78322241,"identity":"7a3accbb-c352-492b-a187-4721094fc74f","added_by":"auto","created_at":"2025-03-12 05:27:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":261099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted changes in the relative abundances of ecological clusters across four future time periods under sustainable (SSP126) and high-emission (SSP585) scenarios. \u003c/strong\u003eBoxplots illustrate projected shifts in the relative abundances of ecological clusters over four future time periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100) under SSP126 (sustainable) and SSP585 (high-emission) socioeconomic pathways. Boxes represent the interquartile range (IQR), with the horizontal line indicating the median, and whiskers extending to 1.5× IQR. Individual data points are overlaid as semi-transparent dots to depict data distribution. Different lowercase letters indicate statistically significant differences according to the Kruskal-Wallis test (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure5ClusterRAfuture.png","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/ba54147cbfb3c5ca072d4fe8.png"},{"id":80405996,"identity":"5e66065a-2b5e-4860-8c0c-5ef0aa0e93a6","added_by":"auto","created_at":"2025-04-11 14:43:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1953326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/772fe5f9-ca2d-47c8-9b52-fbb71987b5fb.pdf"},{"id":78322248,"identity":"0fb221ec-e3e0-432c-942d-bf7ee48e4ba4","added_by":"auto","created_at":"2025-03-12 05:27:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2773048,"visible":true,"origin":"","legend":"Supplementary Dataset-Supplementary tables","description":"","filename":"2501Supplementarydata1TableS1S6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/57b250dea2e675eb06aea3b2.xlsx"},{"id":78322254,"identity":"9cf84df3-aa6a-4359-82c1-16f94df83df8","added_by":"auto","created_at":"2025-03-12 05:27:45","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13157653,"visible":true,"origin":"","legend":"Supplementary Information1","description":"","filename":"2502SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/c29601fbfdd3fb551f7cc8ac.pdf"},{"id":78322250,"identity":"78abe5e4-fe6d-4c00-9a73-13f7c0dc3866","added_by":"auto","created_at":"2025-03-12 05:27:44","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2833244,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"NCOMMS2512926rs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6055015/v1/fdb37a16e2904c39a56b626c.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"High-emission socioeconomic pathways threaten phoD-harboring bacterial communities in cold ecosystems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBiography signifies the diversity and spatial distribution of organisms across various geographic scales, as well as the underlying mechanisms shaping these distributions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Traditionally, research has primarily focused on the biogeography of plants and animals[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, over the past two decades, there has been a growing interest in understanding the biogeographic patterns of microbial communities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], including bacteria[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], archaea[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], viruses[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], fungi[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], protists[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and functional microbial taxa. The latter refers to microbial groups categorized based on their involvement in specific biogeochemical processes, such as nitrogen fixation (e.g., nitrogenase-encoding bacteria)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and organic phosphorus (P) mineralization (e.g., alkaline phosphatase-encoding bacteria)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have explored microbial biogeography at both regional and global scales. For instance, a global survey mapped the worldwide distribution of dominant soil bacterial taxa, identifying soil pH as the primary determinant of bacterial community composition[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This survey also revealed that Ascomycota dominate global soil fungal communities[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], while soil protists are primarily composed of consumers[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Unlike bacterial communities, however, fungal and protistan distributions are largely driven by precipitation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, meta-analyses have assessed the influence of global change factors on microbial diversity and the abundance of major microbial taxa at a global scale[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, these studies primarily focused on taxonomic diversity, overlooking microbial community structure and assembly processes. To address this limitation, researchers have proposed integrating sequencing data from independent studies to investigate microbial community dynamics at broader spatial scales[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While these findings offer important ecological insights, a critical knowledge gap remains: the global biogeography of functional microbial communities has not been systematically examined. It remains unclear whether the distribution patterns and key drivers of functional microbial taxa resemble those of total bacteria, fungi, and protists, and how these communities will respond to climate change. This gap constrains our understanding of microbial functional capacities on a global scale.\u003c/p\u003e \u003cp\u003eOrthophosphate, the only bioavailable form of P for plants and microbes, is a key limiting factor for primary productivity in terrestrial and freshwater ecosystems[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To alleviate P scarcity, extracellular phosphatases catalyze the hydrolysis of organic P into orthophosphate [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These enzymes include acid and alkaline phosphatases, with alkaline phosphatases\u0026mdash;mainly of microbial origin\u0026mdash;exhibiting broader substrate specificity and higher catalytic efficiency[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among the genes encoding alkaline phosphatases (\u003cem\u003ephoA\u003c/em\u003e, \u003cem\u003ephoD\u003c/em\u003e, and \u003cem\u003ephoX\u003c/em\u003e), \u003cem\u003ephoD\u003c/em\u003e is widely used as a biomarker for functional microbial communities involved in organic P mineralization due to its high abundance across diverse environments[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. High-throughput sequencing has significantly improved our understanding of \u003cem\u003ephoD\u003c/em\u003e-harboring microbial diversity and ecology at local and regional scales. For example, \u003cem\u003ephoD\u003c/em\u003e-harboring \u003cem\u003eStreptomyces\u003c/em\u003e and \u003cem\u003eNocardiopsis\u003c/em\u003e dominate agricultural soils[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while \u003cem\u003eBradyrhizobium\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e are highly abundant in Chinese grassland soils[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, \u003cem\u003eNostoc\u003c/em\u003e and \u003cem\u003eGloeocapsa\u003c/em\u003e dominate desert biocrusts[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], whereas \u003cem\u003eRhodoplanes\u003c/em\u003e and \u003cem\u003eBradyrhizobium\u003c/em\u003e are prevalent in subalpine forest soils[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In aquatic ecosystems, such as freshwater lakes, \u003cem\u003eCobetia\u003c/em\u003e and \u003cem\u003eCalothrix\u003c/em\u003e are the dominant \u003cem\u003ephoD\u003c/em\u003e-harboring taxa[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These studies have identified various environmental factors, such as precipitation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], soil C:P or N:P ratios[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and soil pH[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], as key drivers of \u003cem\u003ephoD\u003c/em\u003e-harboring community distributions across ecosystems. However, despite these advances, there remains an urgent need to investigate functional microbial communities at larger spatial scales, particularly through integrative analyses spanning from genes to global ecosystems[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A systematic, global assessment of the diversity, taxonomy, ecology, and distribution of \u003cem\u003ephoD\u003c/em\u003e-harboring microbial communities\u0026mdash;and their responses to climate change\u0026mdash;is still lacking. Climate change, particularly rising air temperatures, is expected to restructure microbial communities, including \u003cem\u003ephoD\u003c/em\u003e-harboring taxa[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Understanding the global distribution of this functional community and its response to climate change is essential for predicting shifts in plant P availability, primary productivity, and ecosystem carbon storage. Moreover, investigating the adaptive strategies of \u003cem\u003ephoD\u003c/em\u003e-harboring communities in response to climate change will enhance our ability to model future ecosystem dynamics.\u003c/p\u003e \u003cp\u003eTo elucidate the environmental drivers of microbial biogeography and the mechanisms governing biodiversity and coexistence, previous studies have suggested focusing on a subset of dominant taxa rather than attempting to analyze the entire microbial community[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This approach simplifies microbial complexity by identifying abundant and widespread taxa that serve as proxies for the broader community. Similarly, when analyzing the global distribution of phoD-harboring communities, prioritizing dominant taxa may reduce computational resource demands while still providing valuable insights. For example, certain \u003cem\u003ephoD\u003c/em\u003e-harboring taxa, such as \u003cem\u003eStreptomyces\u003c/em\u003e and \u003cem\u003eBradyrhizobium\u003c/em\u003e, are highly abundant across diverse ecosystems despite significant spatial separation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, recognizing that microbial taxa exhibit distinct environmental preferences, researchers have proposed clustering them into ecologically meaningful groups based on shared habitat characteristics[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Applying this clustering strategy to \u003cem\u003ephoD\u003c/em\u003e-harboring communities may facilitate a more mechanistic understanding of their global distribution patterns. In this study, we conducted a comprehensive meta-analysis, integrating \u003cem\u003ephoD\u003c/em\u003e amplicon sequencing data from 60 published studies, encompassing 3,175 samples from diverse habitats\u0026mdash;including sediments, farmlands, deserts, grasslands, and forests. This large-scale dataset allowed us to reanalyze \u003cem\u003ephoD\u003c/em\u003e-harboring community distributions worldwide. Specifically, we (i) explored the biogeographic patterns and environmental drivers of \u003cem\u003ephoD\u003c/em\u003e-harboring communities, (ii) characterized the taxonomic composition of dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes, (iii) classified dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes into ecological clusters based on key environmental factors, and assessed their genomic potential by mapping representative sequences to the Genome Taxonomy Database (GTDB) to estimate the abundance of key P-cycling genes, and (iv) predicted the relative abundance shifts of these clusters under future climate scenarios, i.e., under sustainable (SSP126, +\u0026thinsp;0.6 to +\u0026thinsp;1.8\u0026deg;C) and high-emission (SSP585, +\u0026thinsp;3.8 to +\u0026thinsp;8.6\u0026deg;C) socioeconomic pathways. We hypothesized that (i) climate variables (e.g., temperature and humidity), rather than local habitat conditions, are the primary determinants of \u003cem\u003ephoD\u003c/em\u003e-harboring community distributions, as climate influences plant biomass and, consequently, the organic matter available for microbial P mineralization; (ii) Proteobacteria (e.g., \u003cem\u003eBradyrhizobium\u003c/em\u003e) and Actinobacteria (e.g., \u003cem\u003eStreptomyces\u003c/em\u003e) would represent the most dominant \u003cem\u003ephoD\u003c/em\u003e-harboring taxa on a global scale; (iii) taxa adapted to warm climates harbor higher abundances of P-cycling genes than those in cold environments due to greater microbial metabolic activity; and (iv) future climate change will favor the expansion of warm- and humid-adapted \u003cem\u003ephoD\u003c/em\u003e-harboring taxa while suppressing cold- and arid-adapted clusters.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDiversity patterns and key drivers of\u003c/b\u003e \u003cb\u003ephoD\u003c/b\u003e\u003cb\u003e-harboring communities\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo gain a comprehensive understanding of the entire \u003cem\u003ephoD\u003c/em\u003e-harboring community, we examined its diversity patterns and identified the key environmental drivers. The α-diversity of \u003cem\u003ephoD\u003c/em\u003e-harboring communities varied significantly across different habitats, with greater species richness observed in colder and more arid environments (Supplementary Fig. S2). For example, \u003cem\u003ephoD\u003c/em\u003e richness was higher in cold grasslands compared to temperate grasslands (Supplementary Fig. S2a\u0026ndash;b), indicating the dominant influence of climate on diversity patterns. Random forest models further corroborated this, explaining over 62% of the variance in α-diversity and identifying temperature (mean annual temperature, MAT), humidity (aridity index, AI), and habitat pH as the key drivers. Specifically, α-diversity exhibited negative correlations with temperature and humidity but a positive correlation with pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, β-diversity, which reflects community compositional differences, varied significantly among ecosystem types (Supplementary Fig. S2c). To cross-validate the key factors shaping β-diversity, we employed both random forest and distance-based redundancy analysis (db-RDA). These models explained up to 87% and 61% of the variance in β-diversity, respectively, further reinforcing the dominant role of temperature, humidity, and pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee\u0026ndash;g). Distinct separation of \u003cem\u003ephoD\u003c/em\u003e-harboring communities was observed along these environmental gradients, with cold, arid, and alkaline samples clustering on the right, whereas warm, humid, and acidic samples clustered on the left of the db-RDA plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh\u0026ndash;j).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTaxonomic composition and key determinants of dominant\u003c/b\u003e \u003cb\u003ephoD\u003c/b\u003e\u003cb\u003e-harboring phylotypes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo distill meaningful insights from the extensive diversity of \u003cem\u003ephoD\u003c/em\u003e-harboring taxa\u0026mdash;comprising 669,521 OTUs across 3,115 samples\u0026mdash;we focused on a subset of widespread species that provide a representative snapshot of the entire community. In total, 19,194 dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes were identified in more than 100 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Fig. S3). Although these phylotypes accounted for only\u0026thinsp;~\u0026thinsp;3% of the total, they represented approximately 57% of the overall abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), closely mirroring the diversity patterns observed across the entire \u003cem\u003ephoD\u003c/em\u003e-harboring community (Supplementary Fig. S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dominant phylotypes were primarily affiliated with the phyla Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, and Planctomycetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The most abundant genera included \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eNostoc\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003ePaludisphaera\u003c/em\u003e, \u003cem\u003eFrankia\u003c/em\u003e, \u003cem\u003eGloeocapsa\u003c/em\u003e, \u003cem\u003eGloeobacter\u003c/em\u003e, and \u003cem\u003ePirellula\u003c/em\u003e (Supplementary Fig. S5a). The relative abundances of these key \u003cem\u003ephoD\u003c/em\u003e-harboring taxa exhibited trends similar to α-diversity, showing negative correlations with temperature and humidity but a positive correlation with pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed\u0026ndash;f). Moreover, their distribution across ecosystem types paralleled α-diversity patterns, with higher relative abundances observed in colder and more arid environments (Supplementary Fig. S5b). Our random forest models consistently highlighted temperature as the most influential factor driving the relative abundances of dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes, followed by humidity and pH. This finding remained robust regardless of whether we assessed variable importance based on the mean increase in MSE% or the frequency of a factor being the best predictor (Supplementary Fig. S6).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEcological clusters of dominant\u003c/b\u003e \u003cb\u003ephoD\u003c/b\u003e\u003cb\u003e-harboring phylotypes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo further refine our understanding of how environmental factors structure \u003cem\u003ephoD\u003c/em\u003e-harboring communities, we conducted semi-partial correlation and clustering analyses, which revealed six primary ecological clusters based on habitat preferences for temperature, humidity, and pH (Supplementary Table S3, Fig. S7). These included cold and warm clusters (temperature), arid and humid clusters (humidity), and acidic and alkaline clusters (pH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).The relative abundances of phylotypes within these ecological clusters exhibited distinct environmental responses: cold-cluster taxa declined monotonically with increasing temperature, peaking at MAT \u0026asymp; -4 to 0\u0026deg;C; warm-cluster taxa followed a unimodal distribution, peaking at MAT\u0026thinsp;\u0026asymp;\u0026thinsp;16 to 19\u0026deg;C; arid-cluster taxa showed a negative correlation with humidity, peaking at AI\u0026thinsp;\u0026asymp;\u0026thinsp;0.1 to 0.3.Humid-cluster taxa exhibited a unimodal relationship with AI, peaking at AI\u0026thinsp;\u0026asymp;\u0026thinsp;1.2 to 1.4; acidic-cluster taxa declined monotonically with increasing pH, peaking at pH\u0026thinsp;\u0026asymp;\u0026thinsp;4 to 5; alkaline-cluster taxa followed a unimodal pattern, peaking at pH\u0026thinsp;\u0026asymp;\u0026thinsp;7.5 to 8.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate these ecological clusters, we performed co-occurrence network analyses, which revealed that \u003cem\u003ephoD\u003c/em\u003e-harboring taxa with similar habitat preferences were more likely to co-occur within the same ecological cluster, as indicated by distinct clustering patterns in the co-occurrence network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Additionally, they were likely to co-occur more than expected by chance (Supplementary Fig. S8). Each ecological cluster exhibited distinct phylum-level composition, with Actinobacteria and Cyanobacteria being more prevalent in cold and arid clusters, whereas Proteobacteria dominated in warm and humid clusters (Supplementary Fig. S9).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eVariations in P-cycling genes across ecological clusters\u003c/h2\u003e \u003cp\u003eTo assess whether different ecological clusters harbor distinct P-cycling potentials, we examined the genomic attributes of the dominant phylotypes within each cluster. However, genomic data were available for only a subset of these phylotypes: 4 out of 1,664 phylotypes (4/1,664) for the cold cluster, 25/419 for the warm cluster, 16/1,051 for the arid cluster, 14/207 for the humid cluster, 6/92 for the acidic cluster, and 9/411 for the alkaline cluster (Supplementary Fig. S10).\u003c/p\u003e \u003cp\u003eBy classifying P-cycling genes into four major categories based on microbial P metabolic pathways (Supplementary Table S4), we found that warm-cluster taxa exhibited significantly higher frequencies of genes involved in P-starvation response regulation and inorganic P solubilization compared to cold-cluster taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, no significant differences were observed in P-cycling gene frequencies between humid vs. arid clusters or alkaline vs. acidic clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProjected shifts in ecological clusters under future climate scenarios\u003c/h3\u003e\n\u003cp\u003eFinally, we investigated whether future climate change would significantly alter the relative abundances of these ecological clusters. Model validation showed strong correlations between observed and predicted relative abundances (Spearman\u0026rsquo;s rho\u0026thinsp;\u0026ge;\u0026thinsp;0.77, Supplementary Fig. S11), demonstrating the reliability of our prediction models. Under the sustainable (SSP126; +0.6 to +\u0026thinsp;1.8\u0026deg;C) scenario, no significant changes were predicted for any ecological cluster. However, under the high-emission (SSP585; +3.8 to +\u0026thinsp;8.6\u0026deg;C) scenario, substantial shifts were projected by the end of the century: cold-cluster taxa are expected to decline by 84.3%; acidic-cluster taxa are projected to decrease by 13.3%; warm-cluster taxa are predicted to increase by 14.0%, humid-cluster taxa by 6.0%, and alkaline-cluster taxa by 36.3%. The magnitude of these changes varied by ecosystem type. For example, warm-cluster taxa were predicted to increase by 500% in arid forests, whereas cold-cluster taxa were projected to decline by 43.8% in cold grasslands and 95.5% in deserts (Supplementary Fig. S12\u0026ndash;17).These findings suggest that future climate change will favor warm- and humid-adapted \u003cem\u003ephoD\u003c/em\u003e-harboring taxa while suppressing cold- and arid-adapted taxa, with potential consequences for soil P cycling and ecosystem nutrient dynamics.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe ecological theory of diversity and biogeography, well-established through studies on the global distribution of plants and animals[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], prokaryotes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], fungi[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and protists[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], has provided a foundation for biodiversity conservation and management. However, the global distribution of functional microbial communities remains largely unexplored, leaving a critical knowledge gap in understanding whether these communities follow similar biogeographical patterns as total prokaryotes and eukaryotes, and how climate change may reshape them. Given the challenges of conducting a global-scale survey spanning diverse ecosystem types, climates, and continents, we addressed this gap by re-analyzing amplicon \u003cem\u003ephoD\u003c/em\u003e sequence data from 60 studies across various biogeographical regions, compiling a comprehensive dataset of \u003cem\u003ephoD\u003c/em\u003e-harboring microbial communities.\u003c/p\u003e \u003cp\u003eDespite this effort, our understanding of the global \u003cem\u003ephoD\u003c/em\u003e-harboring community distribution remains limited, particularly due to the scarcity of data from the Americas and Africa (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The underrepresentation of tropical ecosystems further restricts our ability to construct a comprehensive global map of \u003cem\u003ephoD\u003c/em\u003e diversity and distribution. Moreover, methodological biases introduced by different primer sets[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] may have influenced the amplification of specific taxonomic groups. Given the relatively small number of samples for each primer set, we combined both datasets to maximize coverage. However, future studies integrating data from underrepresented regions and employing a unified primer approach will be essential to refine global distribution models of \u003cem\u003ephoD\u003c/em\u003e diversity and ecological clusters. Despite these limitations, this study provides valuable insights into the global distribution and environmental drivers of \u003cem\u003ephoD\u003c/em\u003e-harboring communities, offering a foundation for their informed management and the optimization of their functional potential in diverse ecosystems.\u003c/p\u003e \u003cp\u003eBy elucidating the environmental drivers shaping the distribution of \u003cem\u003ephoD\u003c/em\u003e-harboring communities, this study enhances our ability to predict and mitigate the impacts of global change on ecosystem processes governed by this functional microbial communities. Our findings consistently underscore the critical role of climate variables and habitat pH in structuring both the overall diversity patterns and the relative abundances of dominant phylotypes, highlighting their fundamental influence on the biogeographical distribution of this functional community (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Fig. S6). Unlike total prokaryotic communities, which are predominantly shaped by soil pH [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and eukaryotic communities, which are largely driven by climate variables[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], \u003cem\u003ephoD\u003c/em\u003e-harboring communities are simultaneously regulated by both climatic and edaphic factors. This dual dependence suggests that these microbial communities may be more vulnerable to climate change than total prokaryotic and eukaryotic communities. Two key mechanisms may explain this observation: (1) as bacteria, \u003cem\u003ephoD\u003c/em\u003e-harboring taxa are influenced by soil pH that mediates global distribution soil bacteria, and (2) their primary function\u0026mdash;organic P mineralization\u0026mdash;relies on plant and animal detritus, whose availability is governed by temperature and water dynamics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings have significant implications, suggesting that future climate change and increasing landscape fragmentation could potentially alter dominance hierarchies in \u003cem\u003ephoD\u003c/em\u003e-harboring communities and consequently impact their ecological functions.\u003c/p\u003e \u003cp\u003eConsidering temperature as the primary determinant of this functional community, we assessed how rising air temperatures may reshape their global distribution. Our predictive models suggest that a rise in air temperatures of 0.6\u0026ndash;1.8\u0026deg;C under sustainable-emission scenarios (SSP126) would generally have minimal impact on this functional community. However, under high-emission scenarios (SSP585), as air temperatures increase by 3.8\u0026ndash;8.6\u0026deg;C, the relative abundances of warm, humid, and alkaline clusters are projected to rise by 14.0%, 6.0%, and 36.3%, respectively. In contrast, cold clusters are particularly vulnerable, with only 15.7% of their original population expected to persist by the end of the century (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Three mechanisms may help explain these shifts under increasing air temperatures. First, warming adaptation: increasing temperatures favor microbial taxa adapted to warmer conditions (warm-cluster), while cold-cluster taxa face habitat loss as protective ice layers thaw[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Second, temperature-humidity-pH feedback: combined warming and moisture increases enhance global soil alkalinity[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], promoting alkaline-cluster dominance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Third, P-cycling gene trade-offs: cold-cluster taxa harbor fewer genes related to P-starvation response and inorganic P solubilization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), limiting their adaptability under warming conditions[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As climatic zones shift northward, high-altitude regions warm, and soil moisture increases, inorganic P cycling may intensify in cold habitats, while organic P mineralization could decline[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], potentially disrupting P cycling dynamics. The decline of cold-cluster \u003cem\u003ephoD\u003c/em\u003e-harboring taxa in highly sensitive ecosystems\u0026mdash;such as alpine deserts, arctic regions, and cold grasslands\u0026mdash;raises ecological concerns, as their recovery may be highly challenging. Moreover, permafrost thawing could accelerate soil warming[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], further amplifying these shifts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the taxonomic level, the responses of key microbial phyla (e.g., Proteobacteria, Actinobacteria, and Acidobacteria) to warming remain complex, depending on habitat-specific conditions[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our results indicate that while certain phyla are enriched in distinct ecosystems and ecological clusters, most phyla occur across multiple ecosystem (or) clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Figs. S5 and S9), suggesting that at coarse taxonomic (phyla) levels, habitat preferences cannot be predicted solely by phylogeny. Instead, functional traits and adaptation strategies likely dictate microbial resilience under climate change. For example, genera such as \u003cem\u003eNostoc\u003c/em\u003e (Cyanobacteria) and \u003cem\u003eStreptomyces\u003c/em\u003e (Actinobacteria) are widely distributed in stressful environments like drylands and cold grasslands[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These taxa have evolved diverse strategies to withstand environmental stressors, including temperature fluctuations, UV radiation, nutrient scarcity, and osmotic stress. Notably, \u003cem\u003eNostoc\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e produce cold- and heat-shock proteins, enabling survival under extreme temperatures[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, representative genera such as \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eNostoc\u003c/em\u003e, and \u003cem\u003eStreptomyces\u003c/em\u003e possess nitrogen fixation capabilities[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These adaptive traits likely contribute to the success of species belonging to these genera in stressful habitats and highlight their ecological significance in nutrient cycling and ecosystem functioning.\u003c/p\u003e \u003cp\u003eOur findings underscore that climate change may fundamentally restructure global \u003cem\u003ephoD\u003c/em\u003e-harboring communities, promoting the expansion of warm, humid, and alkaline-associated taxa while imposing significant threats to cold-adapted \u003cem\u003ephoD\u003c/em\u003e-harboring communities. The projected loss of these psychrophilic taxa, particularly in ecologically fragile cold environments, highlights an urgent need for strategies to mitigate climate change impacts on functional microbial groups. Given the central role of \u003cem\u003ephoD\u003c/em\u003e-harboring communities in organic phosphorus mineralization, these shifts could have cascading effects on nutrient cycling and ecosystem stability. Future climate policies should prioritize measures to limit global temperature rise, particularly in regions where functional microbial groups are key players in biogeochemical processes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLiterature search\u003c/h2\u003e \u003cp\u003eTo compile a comprehensive dataset of \u003cem\u003ephoD\u003c/em\u003e-harboring microbial communities analyzed via next-generation sequencing, we conducted a literature search in Web of Science (Core Collection) and Google Scholar between October 15 and December 15, 2024. The search keywords included: TS = (\u0026ldquo;phoD\u0026rdquo; OR \u0026ldquo;alkaline phosphatase\u0026rdquo; OR \u0026ldquo;phoD-harb\u0026rdquo; OR \u0026ldquo;phoD harb\u0026rdquo; OR \u0026ldquo;phosphorus minera*\u0026rdquo; OR \u0026ldquo;P mineral*\u0026rdquo; OR \u0026ldquo;phosphorus transformation\u0026rdquo; OR \u0026ldquo;P transformation\u0026rdquo;) AND TS = (\u0026ldquo;amplicon\u0026rdquo; OR \u0026ldquo;sequencing\u0026rdquo; OR \u0026ldquo;high-throughput\u0026rdquo; OR \u0026ldquo;next generation\u0026rdquo;) AND TS = (\u0026ldquo;soil\u0026rdquo; OR \u0026ldquo;sediment\u0026rdquo;). A total of 369 publications were initially retrieved. Studies were filtered based on the following criteria: (1) samples must be collected in situ, excluding those subjected to incubation experiments (except for control samples without treatments); (2) only soil and sediment samples were considered to ensure comparability of environmental data. (3) precise geographic coordinates must be provided; (4) phoD-harboring bacterial communities must have been identified via amplicon sequencing; (5) raw sequence data must be publicly available or obtainable from the authors; (6) sequences must be accurately assigned to specific samples. Additionally, we incorporated four of our own datasets, covering alpine forests, Chinese grasslands across the Qinghai-Tibet Plateau, and biocrusts from five Chinese deserts (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In total, 60 studies met our selection criteria, yielding 3,175 samples from 870 sites, comprising 327 sediment samples and 2,848 soil samples (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Tables S1-S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eLand-use types were recorded based on descriptions provided in each study. Climate classifications were assigned according to the K\u0026ouml;ppen-Geiger climate classification [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] using geographic coordinates. Ecosystem types were defined based on a combination of K\u0026ouml;ppen climate zones[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and land use types. Specifically, arid, cold, and temperate croplands were grouped as croplands, while polar ecosystems included all land-use types classified under polar climates. Climate variables, including mean annual temperature (MAT) and mean annual precipitation (MAP), were extracted from WorldClim (v2.1) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], while the aridity index (AI) was obtained from the Global Aridity Index Database [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] at a resolution of 30 arcseconds. AI was used as a proxy for potential water availability, as it accounts for evapotranspiration effects [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], making it a more reliable metric than MAP.\u003c/p\u003e \u003cp\u003eSoil properties such as pH, total organic carbon (TOC), and total nitrogen (TN) were recorded. Given the study\u0026rsquo;s focus on global patterns of phoD-harboring communities, we also extracted phosphorus-related parameters, including total phosphorus (TP), available phosphorus (AP), and the C:P ratio. These values were obtained directly from tables and datasets or extracted from figures using GetData Graph Digitizer (v2.24). For sediment samples, pH, TOC, TN, and TP were recorded as well. If environmental data were reported as means and standard deviations (SD), we generated sample-level values using an R function (\u0026ldquo;acquire_env\u0026rdquo;) that simulates normally distributed random data based on specified means, SDs, and sample sizes (see Code Availability). When studies reported standard errors (SE) instead of SD, we converted SE to SD using SD\u0026thinsp;=\u0026thinsp;SE \u0026times; \u0026radic;n [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The final compiled dataset of environmental variables is available in Supplementary Table S2.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSequencing data procession\u003c/h3\u003e\n\u003cp\u003eThe phoD sequences analyzed in this study were primarily generated using two primer sets: ALPS-F733/R1083[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and ALPS-F730/R1101[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Despite potential primer biases, both datasets were included due to their comparable amplicon lengths (~\u0026thinsp;370 bp), maximizing sample retention. Bioinformatics processing was conducted using Quantitative Insights into Microbial Ecology 2 (QIIME2)[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Initial quality control was performed using the \u0026ldquo;quality-filter\u0026rdquo; plugin with default settings, resulting in 217,098,720 high-quality sequences across 3,175 samples. Chimeric sequences were removed using VSEARCH [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] plugin in \u003cem\u003edenovo\u003c/em\u003e mode. Additionally, RDP FRAMEBOT[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] was used to correct frameshift mutation of \u003cem\u003ephoD\u003c/em\u003e-encoding sequences. Given potential variations in \u003cem\u003ephoD\u003c/em\u003e sequences due to distinct primers and sequencing platforms across studies, instead of using denoising algorithm to generate amplicon sequence variants (ASVs), we clustered all high quality \u003cem\u003ephoD\u003c/em\u003e sequences into Operational Taxonomic Units (OTUs, here represents the phylotypes) at a similarity threshold of 97% by VSEARCH, thus minimizing the potential impact of primers and sequencing platforms on \u003cem\u003ephoD\u003c/em\u003e-harboring community diversity[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Singletons with fewer than 10 total reads were removed. For diversity estimation, all samples were rarefied to 1,000 sequences per sample, leading to the exclusion of 78 samples with insufficient reads. Taxonomic assignments for phoD-harboring phylotypes were performed using the RDP FunGene database[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDiversity and community composition analyses of phoD-harboring communities\u003c/h3\u003e\n\u003cp\u003eA To assess α-diversity, we calculated Chao1 and Shannon indices using the \u0026ldquo;diversity alpha\u0026rdquo; plugin in QIIME2[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Community compositional differences were examined via nonmetric multidimensional scaling (NMDS), based on rarefied OTU tables and Bray-Curtis dissimilarity matrices. NMDS dimensions was obtained using the \u0026ldquo;metaMDS\u0026rdquo; function in the vegan R package[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo identify key environmental drivers, we employed random forest models[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] to assess the influence of environmental variables and ecosystem types on both α- and β-diversity patterns. The importance of each variable was evaluated by randomly permuting its values and calculating the percent increase in mean squared error (MSE), where a higher MSE% indicated greater model significance. Ecosystem types were treated as categorical variables with nine levels, allowing comparisons of their influence on phoD-harboring community composition. Random forest analyses were conducted using the \u0026ldquo;rfPermute\u0026rdquo; R package [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo further validate the results, we performed distance-based redundancy analysis (db-RDA) using Bray-Curtis dissimilarity matrices, with variable importance assessed via the \u0026ldquo;rdacca.hp\u0026rdquo; function in the rdacca.hp package[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Differences in α-diversity among ecosystem types were tested using non-parametric Kruskal-Wallis analyses. Additionally, we constructed linear, quadratic, and logarithmic models to evaluate the precise relationships between α-diversity and key environmental variables. The three most important predictors (ranked by MSE%) were used as independent variables, and the model with the lowest AIC was selected for final interpretation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of dominant\u003c/b\u003e \u003cb\u003ephoD\u003c/b\u003e\u003cb\u003e-harboring phylotypes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to the ubiquity distribution of the phylotypes (Supplementary Fig. S3 and Table S3), we identified the most prevalent and widespread \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes across the dataset by retaining those found in more than 100 of the 3,115 samples[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In total 19,194 phylotypes were identified as dominant \u003cem\u003ephoD\u003c/em\u003e-harboring taxa (Supplementary Table S3). These phylotypes were widely distributed across samples and can be considered reasonably ubiquitous. Differences in the relative abundances of major groups of dominant \u003cem\u003ephoD\u003c/em\u003e-harboring phylotypes among ecosystem types were also estimated using non-parametric Kruskal-Wallis analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetermining habitat preferences of dominant\u003c/b\u003e \u003cb\u003ephoD\u003c/b\u003e\u003cb\u003e-harboring phylotypes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe used random forest models to determine the environmental preferences of dominant phoD-harboring phylotypes. Predictors included MAT, AI, pH, TOC, TN, TP, AP, and C:P ratio, while the response variable was phylotype relative abundance. In total, 19,194 random forest models were established, with predictive power defined as \u0026gt;\u0026thinsp;30% explained variance, following previous studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubsequently, Spearman\u0026rsquo;s rho-based semi-partial correlations were used to identify the unique contribution of each predictor in elucidating the distribution of phylotypes exhibiting more than 30% random forest explanatory power. These semi-partial correlations were performed using the \u0026ldquo;ppcor\u0026rdquo; R package[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Unlike standard correlations, semi-partial correlations assess the relationship between a given response variable (e.g., phylotype relative abundance) and a specific predictor while controlling for the effects of all other variables [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Dominant phoD phylotypes exhibiting significant semi-partial rho values (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were used to define habitat clusters, where the strongest predictor (highest |rho|) determined cluster labels. Clusters containing\u0026thinsp;\u0026ge;\u0026thinsp;30 phylotypes were considered ecologically meaningful and visualized via heatmaps (Supplementary Fig. S7).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConstruction of co-occurrence network of dominant\u003c/b\u003e \u003cb\u003ephoD\u003c/b\u003e\u003cb\u003e-harboring phylotypes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo examine the co-occurrence patterns of dominant phoD-harboring phylotypes, we constructed co-occurrence networks based on Spearman\u0026rsquo;s rank correlations. Prior to network construction, phylotype relative abundances were Min-Max standardized to ensure comparability across ecological clusters. Network edges were defined using Spearman\u0026rsquo;s rho\u0026thinsp;\u0026ge;\u0026thinsp;0.65 with FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Network construction was performed in the \u0026ldquo;igraph\u0026rdquo; R package[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] and visualized using Gephi (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gephi.org\u003c/span\u003e\u003cspan address=\"https://gephi.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To assess whether phylotypes co-occurred more frequently than expected by chance, we generated 1,000 null-model networks using the Erdős\u0026ndash;R\u0026eacute;nyi model, preserving node and edge counts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparing whole genome attributes to identify differences in P-cycling genes of dominant phylotypes among ecological clusters\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo evaluate differences in P-cycling gene content among ecological clusters, we matched the phoD sequences of dominant phylotypes against the Genome Taxonomy Database (GTDB, Release 214.1)[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Out of the 19,194 dominant phylotypes, the \u003cem\u003ephoD\u003c/em\u003e sequences of 310 phylotypes matched those in GTDB (Supplementary Table S3). Genomes were selected based on a 97% identity threshold and a 90% coverage threshold, resulting in a total of 216 unique genomes being selected (Supplementary Table S3). However, only 74 out of the 216 genomes were precisely associated with phylotypes falling into specific ecological clusters. To annotate P-cycling genes, we used DIAMOND software to search protein sequences against PCycDB, a curated database of microbial P-cycling genes[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Based on specific metabolic processes related to P-cycling, we classified the P-cycling genes into four main groups: P starvation response regulation, inorganic P solubilization, organic P mineralization, and P transportation. Detailed KO numbers, functional descriptions, and metabolic pathways of P-cycling genes are provided in Supplementary Table S4. To compare gene abundances among clusters with contrasting environmental preferences (e.g., warm vs. cold clusters), we computed log2 fold-changes using the \u0026ldquo;DESeq2\u0026rdquo; R package[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The complete gene frequency dataset is available in Supplementary Table S5.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredicting relative abundances of ecological clusters under climate change\u003c/h2\u003e \u003cp\u003eTo assess the potential impact of climate change on the relative abundances of phoD-harboring ecological clusters, we obtained future climate projections from WorldClim (v2.1). Specifically, climate data of two scenarios, namely sustainable (SSP126) and high-emission (SSP585) socioeconomic pathways and four periods (2021\u0026thinsp;~\u0026thinsp;2040, 2041\u0026thinsp;~\u0026thinsp;2060, 2061\u0026thinsp;~\u0026thinsp;2080, 2081\u0026thinsp;~\u0026thinsp;2100) were extracted considering the BCC-CSM2-MR climatic model from Beijing Climate center[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The estimated increments of air temperature regarding the two selected socioeconomic pathways were 0.6 to 1.8 ℃ for SSP126, and 3.8 to 8.6 ℃ for SSP585 by the end of this century[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. To predict future shifts in ecological cluster abundances, we constructed six random forest models, treating the relative abundances of ecological clusters as response variables and current climate and environmental factors as predictors. Model training was performed using the \u0026ldquo;train\u0026rdquo; function in the caret R package, with 70% of the samples used for training and 30% reserved for testing. Future relative abundances of ecological clusters were predicted by applying these models to future climate projections while keeping other environmental factors constant. The random forest models were run separately for each SSP scenario and time period to capture scenario-specific and temporal dynamics. Differences in the predicted relative abundances of ecological clusters across time periods were assessed using non-parametric Kruskal-Wallis tests.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available. The sources of phoD amplicon sequences are listed in Supplementary Table S1, while the corresponding environmental variables are detailed in Supplementary Table S2. The OTU table generated in this study has been deposited on Figshare and can be accessed at https://doi.org/10.6084/m9.figshare.25706067.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll Shell and R scripts used in this study are available on GitHub at: https://github.com/YangYanghei0818/Codes_availability_Xu_2025_GlobalphoD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [32100076, 42477117]; China Postdoctoral Science Foundation [2023M742511]; Sichuan Science and Technology Program [2023NSFSC1191, 2023NSFSC1165, 2024YFNH0028]; and Talent-Recruiting Program of Sichuan Agricultural University [2222996049]. Opinions expressed in this paper are those of the authors and not necessarily of authors\u0026rsquo; affiliations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLX and YPK conceived the project. LX, YPK, CNL and BT collected data. LX, CNL and BT carried out bioinformatic and statistical analyses. LX , YPL and CNL prepared the original draft. LX and XZL acquired funding. All authors read, edited, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to Yongping Kou or Zhenfeng Xu\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHanson CA, Fuhrman JA, Horner-Devine MC, Martiny JBH (2012) Beyond biogeographic patterns: processes shaping the microbial landscape. Nat Rev Microbiol 10:497\u0026ndash;506\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHubbell SP (2001) Princeton University Press. 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Geosci Model Dev 13:3571\u0026ndash;3605\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6055015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6055015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlkaline phosphatase gene (\u003cem\u003ephoD\u003c/em\u003e) harboring microbial communities drive organic phosphorus (P) mineralization, regulating plant P availability and ecosystem productivity. However, their global distribution pattern, key environmental drivers, and responses to climate change remain poorly understood. Here, we conducted a meta-analysis of \u003cem\u003ephoD\u003c/em\u003e amplicon sequences from 3,175 samples spanning diverse ecosystems worldwide, revealing higher diversity in colder and more arid ecosystems. Climate (temperature, humidity) and pH emerged as key determinants, structuring distinct ecological clusters. Random forest models predicted that under high-emission scenarios (SSP585, +\u0026thinsp;3.8 to +\u0026thinsp;8.6\u0026deg;C increment of air temperature), warm-, humid-, and alkaline-associated clusters will expand, while cold-adapted clusters may decline by 84.3%, particularly in vulnerable cold grassland and alpine desert soils. Comparative genomic analysis further revealed higher P-starvation response and inorganic P-solubilization gene frequencies in warm-adapted taxa. These findings provide new insights into the ecological adaptation of \u003cem\u003ephoD\u003c/em\u003e-harboring communities and highlight potential disruptions to microbial P cycling under climate change, emphasizing the need for conservation strategies to protect cold-adapted functional microbial communities.\u003c/p\u003e","manuscriptTitle":"High-emission socioeconomic pathways threaten phoD-harboring bacterial communities in cold ecosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-12 05:27:30","doi":"10.21203/rs.3.rs-6055015/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cdbea995-7418-4ed4-a58e-1883fe7f1652","owner":[],"postedDate":"March 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44536542,"name":"Biological sciences/Ecology/Microbial ecology"},{"id":44536543,"name":"Biological sciences/Ecology/Biogeography"},{"id":44536544,"name":"Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction"}],"tags":[],"updatedAt":"2025-04-11T14:35:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-12 05:27:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6055015","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6055015","identity":"rs-6055015","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}