Oligotrophic bacteria and pathotrophic fungi moderate multitrophic interactions in semi-arid and arid environments

Oligotrophic bacteria and pathotrophic fungi moderate multitrophic interactions in semi-arid and arid environments | 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 Research Article Oligotrophic bacteria and pathotrophic fungi moderate multitrophic interactions in semi-arid and arid environments Premchand Maisnam, Thomas C Jeffries, Jerzy Szejgis, Dylan Bristol, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6575220/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Environmental Microbiome → Version 1 posted 9 You are reading this latest preprint version Abstract Background Future climate projections indicate shifts in intra-annual precipitation patterns, with intensified rainfall events and prolonged dry periods. These changes may alter soil biotic communities and their interactions within food webs, particularly in semi-arid and arid ecosystems. However, the extent to which varying rainfall regimes and semi-arid and arid conditions influence multitrophic associations remains poorly understood. Methods We leveraged a four-year rainfall manipulation experiment across six dryland sites in eastern Australia, representing arid and semi-arid ecosystems with varying, high and low levels of rainfall variability (CV) making three different climatic conditions. Rainfall treatments simulated increased (+ 65%) and reduced (-65%) precipitation relative to ambient conditions. We studied multitrophic co-occurrence network among bacteria, fungi, protists, and nematodes, representing key components of the soil food web, and assess their associated changes to varying rainfall and climatic conditions. Results Climatic differences between arid and semi-arid ecosystems were the primary drivers of soil biotic community composition, whereas rainfall treatments had minimal influence. Multitrophic co-occurrence networks varied significantly across climatic conditions, with increasing aridity promoting more positive associations among bacterial nodes. Bacteria, fungi, and their interactions were central to the belowground multitrophic network structure. In particular, stress-tolerant oligotrophic bacteria and pathotrophic fungi played key roles, with mean annual precipitation (MAP) identified as a critical determinant. Conclusions Our findings suggest that aridity-driven shifts in biotic interactions may restructure belowground food webs in dryland ecosystems. The increasing dominance of oligotrophic bacteria and fungal pathotrophs under arid conditions highlights potential consequences for soil functioning and plant-soil interactions in response to changing precipitation regimes. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global warming is expected to amplify the hydrological cycle, leading to more extreme variation in intra-annual precipitation, resulting in fewer but larger rainfall events and longer periods of dryness (IPCC, 2021). Such changes pose a significant threat to life on Earth, especially in regions that heavily rely on rainfall including arid and semi-arid areas (Austin et al. 2004 ). Soil biota play a crucial role in dryland ecological processes, such as nutrient cycling, and net ecosystem productivity (Schimel, 2010 , Liu et al., 2022 ). Substantial evidence indicates that these organisms are highly sensitive to changes in rainfall patterns and increasing aridity, which can significantly impact the structure and composition of soil biological communities (Huang et al. 2017 ; Nielsen and Ball 2015 ). While bacterial and fungal co-occurrence have been extensively examined under climate change in semi-arid and arid ecosystems, the co-occurrence patterns across multitrophic groups including bacterial, fungal, protist and nematode domains remain relatively under-investigated. Belowground communities are complex and diverse, with organisms ranging from microscopic bacteria, fungi, and protists to larger invertebrates such as nematodes (Bardgett and Putten, 2014). These organisms form intricate food webs or ecological networks that regulate ecosystem processes (Bardgett and Putten, 2014; Delgado-Baquerizo et al., 2020). The structure and composition of the soil food web play critical roles in shaping energy flux, food web stability, and carbon (C) and nitrogen (N) cycle processes. Bacteria and fungi constitute the base of the soil food web and play critical roles in soil functions, while protists and nematodes play important roles as biological regulators of prey bacteria and fungi (Jiang et al. 2023 ; Oliverio et al. 2020 ). Protists and nematodes represent multiple trophic groups, including herbivores, omnivores, predators, and microbial feeders, and thus influence microbial communities and the health of plants and soil processes (Jiang et al. 2023 ; Oliverio et al. 2020 ; Xiong et al. 2020 ). Microbes, protists and nematodes all require soil water for their activities; hence, shifts in rainfall, particularly in drylands, will have significant consequences. For instance, reduced precipitation has been shown to affect total nematode abundance, although individual feeding groups show differential responses, with bacterivores being particularly sensitive, which might influence bacterial and fungal dominance in arid conditions (Chen et al., 2021 , Dylan et al., 2023). Protists are also abundant in dryland soils and are highly sensitive to changes in soil moisture. As active feeders on bacteria and fungi, shifts in precipitation regimes can influence their role in moderating elemental cycling (Geisen et al. 2018; Oliverio et al. 2020 ). Furthermore, microbes, protists and nematodes, which represent a range of life histories and feeding habits, can respond to environmental disturbances and climate change in remarkably different ways (van den Hoogen et al. 2019 ; Huang et al. 2017 ). Future changes in precipitation patterns may have various effects on the soil biota and potentially alter the relationships and interactions between them. Hence, research that focuses on multitrophic interactions across the soil food web with changing environmental conditions is needed. The functioning of an ecosystem depends on the complex interactions among biological, physical and chemical components of the soil and is highly susceptible to climate change (Ochoa-Hueso et al. 2020 ; de Vries and Caruso 2016). Additionally, the phylotypes in soil food webs that have comparable environmental and resource preferences may group together to form dense ecological clusters in ecological networks, which would have significant effects on ecosystem functions (Delgado-Baquerizo et al., 2016; 2020). Thus, identifying the key biotic and abiotic drivers of soil food web structure (including bacteria, fungi, protists and invertebrates) along extensive climatic gradients is a global priority if we are to predict how soil communities respond to changing environmental conditions. Approaches such as network analysis can help fill this research gap, where associations among the soil microbiome can be summarized using ecological network attributes (strong correlations of degree, betweenness and clustering coefficient) that illustrate how interactions among soil taxa change across environmental or climate gradients or in response to correlation disturbances (Coyte et al., 2015 ; Felipe-Lucia et al., 2020). Recent studies have also shown evidence of strong co-occurrence of microbial taxa that form a well-defined module in soils across environmental or climatic gradients (Barberán et al. 2012 ; Delgado-Baquerizo, Doulcier, et al. 2020). A module is a group of nodes connected more densely to each other than to other nodes outside the group. Increasing aridity may alter the relative abundance of modules via reductions in water availability and via changes in soil properties (Delgado-Baquerizo et al. 2020; Maestre et al. 2015). Additionally, network analysis can be used to identify generalist or keystone taxa that may play a central role in the overall stability of belowground multitrophic interactions (Banerjee et al., 2018 ; Delgado-Baquerizo et al., 2020). Thus, taking a whole-network approach including bacteria, fungi, protists and nematodes has the potential to advance our knowledge of the soil microbiome and ecosystem responses to global change drivers (e.g., climate change) at both local and global scales (Barberán et al. 2012 ; Neilson et al. 2017 ; Rillig et al. 2016 ). This study investigated how variations in rainfall and aridity influence soil multitrophic interactions among bacteria, fungi, protists, and nematodes in semi-arid and arid ecosystems. Given that key trophic groups and keystone taxa can shape the composition and diversity of soil food webs, we aimed to identify the trophic groups central to maintaining food web stability under fluctuating rainfall and climatic conditions. The study was driven by two main hypotheses. Hypothesis 1 posits that rainfall variations significantly affect multitrophic interactions, leading to increased compartmentalization of microbial communities in more arid conditions. We expect this compartmentalization to be dominated by trophic groups adapted to stress conditions, which may reveal important insights into ecosystem resilience and functionality in response to reduced water availability. Current observation of the increasing dominance of stress-tolerant fungal pathotrophs (Delgado-Baquerizo et al. 2020; Glassman et al. 2018 ) and oligotrophic bacteria (Maisnam et al. 2023 ) under increasing drying and warming conditions support Hypothesis 2 , which posits that stress-adapted trophic groups, particularly oligotrophic bacteria and fungal pathotrophs, will play a dominant role in mediating belowground multitrophic interactions in semi-arid and arid ecosystems. Owing to their relatively high abundance, these groups are likely to play key central roles. Their responses to varying rainfall and climatic conditions could be crucial for understanding how belowground food webs adapt to climate change, influencing the health and stability of semi-arid and arid ecosystems. These findings provide novel insights into the shifting dynamics of multitrophic interactions across varying rainfall regimes, contributing to a deeper understanding of semi-arid and arid ecosystems responses to climate change. Material and methods Experimental design and sampling This study leveraged rainfall manipulation facilities at six sites in western New South Wales (NSW) and south-western Queensland (QLD), established to assess the effects of altered rainfall regimes on ecosystem structure and function in Australian semi-arid and arid ecosystems. The sites have different vegetation and soil properties, different mean annual rainfall values (ranging from 209 to 494 mm year − 1 ), temperatures (ranging from 18 to 21º C) and aridity indices (AI; ranging from 0.11 to 0.3) (Table S1 ). The six sites span distinct rainfall regimes, with rainfall increasing from west to east and greater interannual variability (CV; ranging from 0.28 to 0.57) observed at the northern sites. For this study, semi-arid sites with higher CV were grouped as 'Semi-arid High CV' group, whereas those with lower CV were classified into the 'Semi-arid Low CV’ group, and the two remaining arid sites were collectively categorized into the 'Arid’ group, making three different climatic conditions. By creating these groupings, studies can systematically compare how changes in rainfall variability (high vs. low CV) and different aridity conditions (semi-arid vs. arid) influence ecological processes. This helps identify patterns and drivers of ecological change across a gradient of climatic conditions. The sites were established in 2016 with three different rainfall treatments, representing ambient (control), reduced (-65%) and increased (~ 65%, but lower given inefficiencies in the transfer of water) rainfall, with 3 replicates at each site (Maisnam et al. 2023 ). The rainout shelters, each covering a 3 × 3 m plot, were erected above all the plots with corrugated plastic roof slats used to remove approximately 65% of the incoming rain from the reduced rainfall plots, which were collected in gutters and transferred via polyethylene piping to adjacent increased rainfall plots. The shelters above the ambient and increased rainfall plots had a bird mesh to simulate the shelter effect. Prior to collecting soil samples, we conducted vegetation surveys of each plot, focused on the core 1.5 × 1.5 m quadrat to avoid edge effects, counting the number of individuals of each species in each plot to assess composition and species richness. Standing biomass was then estimated using allometric relationships based on the percentage cover of individual plant species via relationships for the same species assessed at individual sites (Chieppa et al. 2020 ). When we were unable to determine the specific species, we used allometric relationships for the most similar functional type. Eight soil cores (3.5 cm diameter, 10 cm deep) were then collected randomly within the central 1.5 × 1.5 m quadrat and combined to form a composite sample for each plot, with 3 replicate plots of each treatment per site resulting in a total of 9 samples per site per year. Samples were transported to Hawkesbury Institute of Environment (HIE), Western Sydney University (WSU), subsampled for chemical analyses and DNA extraction with soils for the latter stored at -20°C until further analyses. In this study, we focused on samples collected in spring 2020 only. Soil properties Soil moisture content was estimated by oven drying ~ 10 g fresh soil at 105°C. Soil pH was measured in a 1:5 soil:water slurry using a calibrated pH meter (S20 SevenEasy Mettler Toledo). The total soil C (TC) and total nitrogen (TN) contents were determined via oxidative combustion method using a LECO CN Analyzer (TruMac, LECO Corporation, St Joseph, MI, USA). Total P was determined using an Epsilon 4 Benchtop X-ray fluorescence (XRF) spectrometer (Malvern Panalytical, Malvern, UK). Amplicon sequencing and processing Bacterial, fungal and protist DNA was extracted from 0.5 g of thawed soil using the DNeasy PowerSoil DNA Isolation Kit (QIAGEN), following the manufacturer’s protocol. For nematode samples, 100 g of homogenized soil was processed using a modified Baermann funnel technique over three days, as described by Wang et al. ( 2019 ). The collected nematodes were concentrated via centrifugation, and DNA was subsequently extracted using the same PowerSoil kit. The concentration and purity of all DNA extracts were assessed using a Qubit 4 Fluorometer (Thermo Fisher Scientific) and NanoDrop spectrophotometer (Thermo Fisher Scientific), respectively, adhering to standard protocols (Lucena-Aguilar et al. 2016; Simbolo et al. 2013 ). DNA samples were then submitted to the Western Sydney University Next-Generation Sequencing Facility for amplicon library preparation and sequencing. Amplicon libraries were prepared using the NEBNext Ultra II Q5 Master Mix (New England Biolabs) in accordance with the manufacturer’s instructions. Specific primer pairs were employed to target distinct taxonomic groups: the 341F/805R pair (Herlemann et al. 2011 ) for bacterial 16S rRNA genes, FITS7/ITS4 (Ihrmark et al. 2012 ) for fungal internal transcribed spacer (ITS) regions, Euk1391F/EukBr (Zhu et al. 2021 ) for protist 18S rRNA genes, and 1274F/706R (Schenk et al. 2020) for nematode 28S rRNA genes. Polymerase chain reaction (PCR) conditions were optimized for each amplicon type. For bacterial 16S and nematode 28S rRNA gene amplifications, the thermocycling protocol consisted of an initial denaturation at 95°C for 3 min, followed by 25 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s, with a final extension at 72°C for 5 min. For fungal ITS and protist 18S rRNA gene amplifications, the protocol included an initial denaturation at 94°C for 5 min, followed by 25 cycles of 94°C for 30 s, 57°C for 30 s, and 72°C for 30 s, concluding with a final extension at 72°C for 5 min. Indexing of amplicons was performed using the Nextera XT Index Kit v2 (Illumina) under the following thermocycling conditions: 95°C for 3 min; 8 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s; and a final extension at 72°C for 5 min. The indexed libraries were purified using AMPure XP beads (Beckman Coulter) and assessed for fragment size distribution using the Agilent TapeStation 4200 system. Equimolar concentrations of purified libraries were pooled and quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). Sequencing was conducted on the Illumina MiSeq platform employing the MiSeq Reagent Kit v3 (600-cycle), generating paired-end reads of 2 × 300 bp (Shokralla et al. 2012 ). Downstream sequence data processing was performed using Quantitative Insights into Microbial Ecology software (QIIME 2; https://qiime2.org/ ) (Bolyen et al. 2019). Initially, generated raw paired end sequences were demultiplexed and low-quality regions and chimeric sequences were removed using the DADA2 plugin in QIIME2 (Callahan et al. 2016 ). Feature tables (amplicon sequence variants, ASVs) and feature data (representative sequences) were generated as resulting data from DADA2. Taxonomy was assigned to the ASVs with QIIME2 using a pre-trained Naive Bayes classifier (Rosen et al., 2011 ) and compared against the SILVA database for bacteria and nematodes, and UNITE database for fungal, and PR2 database for protist classification respectively (Nilsson et al. 2019; Quast et al. 2013 ). Following ASV assignments, FUNGuild (Nguyen et al. 2016 ) was used to identify fungal trophic mode by grouping each guild into pathotrophs (plant and animal pathogens), saprotrophs (plant, soil and wood saprotroph), and symbiotrophs (arbuscular mycorrhizal fungi, ectomycorrhizal fungi, and endophytes) (Zhao et al. 2020). The functional groups of protists such as consumers, phototrophs and pathothrophs, are classified on the basis of their feeding habits at the genus level (Nguyen et al. 2023 ). The protist taxa at the genus level are considered to have similar feeding modes. Similarly, all the trophic groups of nematodes, such as bacterivores, fungivores and omnivores, were collectively placed under consumers. While consumers feed mainly on bacteria and other eukaryotes, phototrophs synthesize energy via photosynthesis. Pathotrophs are protists living in soil animals or plants, and other unassigned groups are defined as unknown. Statistical analyses Unless otherwise noted, statistical analyses were performed in R software (R Core Team, 2020) and visualized using the “ggplot2” package ( https://ggplot2.tidyverse.org ). Before we conducted the soil organism community analysis, the ASV tables were rarefied to a minimum number of sequences from each sample, at 4,485 for bacteria, 10,526 for fungi, 1,079 for protists, and 412 for nematodes, to ensure even sampling. Community composition across sites and rainfall treatments was visualised using principal coordinate analysis (PCoA) based on Bray-Curtis distance for each of the four taxa in the Phyloseq R package (McMurdie and Holmes 2013). We analysed whether there was a statistical significance of site and treatment using permutational ANOVA (PERMANOVA) with the Adonis function in vegan package in R (McMurdie and Holmes 2013). Additionally, distance-based redundancy analysis (dbRDA) using the Bray-Curtis distance matrix was performed to test the significance and importance of the environmental variables in explaining the variation in community composition across sites and treatments. Soil and environmental parameters were Total.C = total soil carbon, Total.N = total soil nitrogen, Total.P = total soil phosphorous, CN:ratio = soil C:N ratio, VR = plant richness, MAP = mean annual precipitation, OYR = one year rain, TMR = three month rain, and Temp = mean annual temperature (MAT). These analyses were performed using the capscale function of the vegan package (Oksanen et al. 2016 ). Additionally, differential relative abundance analysis was performed using the run_lefse function from the microbiomeMarker package in R to identify significant ASVs associated with the three climatic conditions and rainfall treatments, with adjusted p-values < 0.05 considered significant. Co-occurrence network analysis Multitrophic co-occurrence networks were constructed using a random matrix theory (RMT)-based approach implemented in the Molecular Ecological Network Analysis Pipeline (MENAP; http://ieg4.rccc.ou.edu/mena/ ) with default parameters (Deng et al., 2012 ; Zhou et al., 2010 ). This method is widely used and has proven effective for constructing multitrophic co-occurrence networks (Delgado-Baquerizo et al. 2020; Jiao, Lu, and Wei 2022), offering results that are comparable across ecological studies and consistent with our own previous analyses conducted at the same experimental site (Maisnam et al. 2023 ). Prior to conducting the final network analysis, we compared network topology metrics between MENAP’s RMT-based Pearson correlation method and the compositionally aware SparCC approach with default parameters (via the iNAP platform; Feng et al. 2022 ) using the combined ASV dataset (n = 54 samples) that included bacteria and eukaryotes (fungi, protists, and nematodes). The two methods showed strong concordance across key network metrics (Table S2), consistent with previous evaluations (Hirano and Takemoto, 2019), thereby supporting the reliability and robustness of the RMT-based approach for constructing multitrophic ecological networks. The RMT-based approach can automatically define a threshold for cellular network construction and are robust to noise, offering effective solutions to common challenges in high-throughput amplicon sequencing data (Deng et al. 2012 ). The RMT-based co-occurrence network method used in the MENAP addresses some of the key concerns in ecological network analysis, such as threshold selection, noise, high-dimensional data, and false positives, resulting in more robust, replicable and meaningful ecological co-occurrence networks (Goberna and Verdú 2022). The ASVs of bacteria and eukaryotes (fungi, protists, and nematodes) were merged into a table and co-occurrence networks were constructed. Three multitrophic co-occurrence networks were constructed comprising Semi-arid High CV (n = 18), Semi-arid Low CV (n = 18), and Arid (n = 18) climatic conditions. To obtain robust associations between soil organisms, we set the threshold of Pearson correlation coefficient to 0.6 and false discovery rate (FDR)-corrected p-values at p < 0.001, this cutoff is extensively used and comparable across studies (Delgado-Baquerizo et al. 2020; Jiao, Lu, and Wei 2022; Maisnam et al. 2023 , Peng et al. 2024 ). Only the ASVs detected in more than half of all samples were retained for network construction. The RMT-based approach can delineate separate modules, where each network was separated into modules by the fast-greedy modularity optimization. Each node in a module signifies an ASV and each edge signifies a significant (p < 0.05) pairwise association calculated based on the Pearson correlation coefficient. The network graph was represented using identified ASVs (nodes) with positive or negative interactions (edges). Positive interactions indicate that the abundances of the two associated ASVs changed following the same trend across different soil samples (i.e., they were positively correlated). Negative interactions indicate that the abundances of those ASVs changed following the opposite trend in different soil samples (Lu et al. 2013 ). A modularity value measures the integrity of networks and is a fundamental characteristic of biological networks (Zhou et al. 2010 ). Each module in a network represents species with similar ecological niches that interact more frequently with each other than with species in other modules (Deng et al. 2012 ; Luo et al. 2021 ). Modularity is a key metric that gauges the extent to which a network is structured into well-defined modules, and it is a highly significant concept in ecology. Various factors can contribute to modularity, such as the specificity of interactions (such as predation or mutualism), diversity in habitat, resource partitioning, overlapping ecological niches, natural selection, convergent evolution, and phylogenetic relatedness. To identify keystone taxa or functional groups, we analysed modular topological roles, which are based on the nodes' roles within their respective modules. The topological role of each node (ASV) was defined by two parameters: within-module connectivity (Zi) and among-module connectivity (Pi) and the ZiPi scatter was plotted in Microsoft Excel. The Zi value determines how well a node is connected to other nodes within its module, whereas the Pi value determined how well a node is connected to nodes in different modules (Guimerà and Amaral 2005). Within-module connectivity (Zi) and among-module connectivity (Pi) were calculated to identify the keystone ASVs. Previous research proposed threshold values of 2.5 for Zi and 0.62 for Pi, which were used to categorize the nodes into four groups (Guimerà and Amaral 2005; Zhou et al. 2010 ). (i) Peripheral nodes (specialists) had low Zi (< 2.5) and low Pi (< 0.62) values. These nodes had only a few links and were almost always connected to the nodes within their own modules. (ii) Connectors (named generalists) had low Zi values ( 0.62). These modules were highly connected with other modules. (iii) Module hubs (also named generalists) had high Zi values (> 2.5) but low Pi values ( 2.5) and Pi (> 0.62) values. Generalists (connectors, module hubs) and network hubs are the key organisms that play important roles in maintaining network stability. Finally, the associations between module-based eigengenes and environmental variables were examined to elucidate the modules' responses to environmental changes (Deng et al. 2012 ). This analysis involved calculating Pearson correlation coefficients (r values) and their corresponding significance levels (p values). Moreover, Spearman correlation analysis was performed in R to explore the relationships between the identified keystone groups and environmental variables, providing insights into how these taxa are influenced by key environmental factors. Results Soil biotic communities differed among climatic conditions Bacterial and eukaryotic community analysis across sites and rainfall treatments indicated that the variation between datasets was predominantly explained by climatic conditions (p < 0.001), with no effect of rainfall treatment for any of the four groups (Fig. S1 ). Both bacterial and fungal communities displayed distinct clustering patterns, with Arid (Broken Hill and Milparinka), Semi-arid High CV (Cobar and Nyngan), and Semi-arid High CV (Charleville and Quilpie) forming separate clusters (Fig. S1 ). However, such clear clustering patterns was not observed for the protist and nematode communities. Distance-based redundancy analysis (dbRDA) demonstrated that, with the exception of fungal communities, all groups presented strong associations with MAP, soil pH, and soil carbon and nitrogen contents (Fig. 1 ). Furthermore, differential abundance analysis among the three climatic conditions revealed that most of the significant groups belonged to oligotrophic bacteria and saprotrophic fungi (Table S3). Arid multitrophic co-occurrence network exhibited stronger clustering compared to semi-arid networks The Molecular Ecological Network Analysis (MENA) pipeline was used to infer association networks for bacterial, protist, fungal, and nematode communities. Three separate networks were constructed, i.e., Semi-arid Low CV, Semi-arid High CV and Arid. Our findings revealed that the arid network was the most complex, exhibiting the greatest number of nodes and edges (148 nodes, 546 edges), followed by the Semi-arid Low CV (130 nodes, 302 edges) and Semi-arid High CV (115 nodes, 315 edges) networks (Table 1 ). Interestingly, only the Arid network showed more positive associations than negative associations. Thus, the ratio of positive to negative links in the Arid network (1.4) was greater than that in the Semi-arid Low CV (0.86) and Semi-arid High CV (0.56) networks. Compared with those of the Semi-arid High CV (8.081, 0.139) and Arid (7.622, 0.161) netwroks, the average degree (avgk) and average clustering coefficient (avgCC) of Semi-arid Low CV had the lowest values (4.646, 0.08). However, the Arid network had the lowest value of average path distance (GD) compared with both semi-arid networks. This suggests that the nodes of the Arid network were more closely clustered than those of the two other networks. In addition, the modularity value of empirical networks was much higher than that of random networks, as observed in the case of Arid network. Similarly, larger differences can also be observed with the average path distance (GD) which is higher in the empirical network than in the random network (Table 1 ). Table 1 Empirical and random network properties of Semi-arid Low, High, and Arid co-occurrence networks. Empirical network properties Semi-arid Low Semi-arid High Arid Number of nodes 130 124 148 Number of edges Positive Negative Ratio ± 302 140 162 0.86 501 181 320 0.56 546 319 227 1.4 Average degree (avgK) 4.646 8.081 7.622 Average clustering coefficient (avgCC) 0.080 0.139 0.161 Average path distance (GD) 3.553 3.429 3.177 Modularity (no. of modules) 0.465 (12) 0.296 (8) 0.434 (8) Random networks properties Average clustering coefficient (avgCC) (± SD) 0.066 (± 0.011) 0.211 (± 0.019) 0.123 (± 0.013) Average path distance (GD) (± SD) 3.181 (± 0.056) 2.624 (± 0.035) 2.788 (± 0.042) Modularity (± SD) 0.400 (± 0.008) 0.246 (± 0.006) 0.297 (± 0.007) In the Semi-arid Low CV and High CV networks, seven and six modules (with ≥ 5 nodes) were respectively obtained, whereas the Arid network had only four such modules (Fig. 2 ). The modules within the Arid network comprised three large modules that were closely connected, whereas the Semi-arid High CV network had four larger modules. In contrast, the Semi-arid Low CV network had modules that were more evenly distributed. Most of the modules included all the trophic groups, with only a few represented by a single trophic group (such as the bacteria-dominated Semi-arid Low CV M3 and Arid M3 modules). Microbiome associations differ across semi-arid and arid climatic conditions The comparison between inter-group networks revealed that bacteria had the highest number of nodes, followed by fungi, protists, and nematodes, with varying relative abundances among the networks. The Semi-arid Low CV network had fewer fungal nodes but more protist nodes, while Semi-arid High CV and Arid networks had relatively higher abundances of bacteria, fungi, and nematodes. Notably, when the trophic groups nodes were compared, the Arid network had the highest number of saprotrophs followed by symbiotrophs, pathotrophs and phototrophs with lowest number (Fig. 2 ; D). Additionally, the study compared the number of positive and negative associations within and between microbiome groups (Fig. 2 ; E). The Semi-arid Low CV and Arid networks were dominated by bacteria-bacteria associations (BB; 40% and 47.25%, respectively) whereas the Semi-arid High CV network was mainly dominated by bacteria-fungi associations (BF; 42%). Fewer associations were observed with protists and nematodes. Protists showed more associations with bacteria and fungi in Semi-arid Low CV (BP; 12.5% and FP; 4.3%) than Arid (BP; 3.8% and FP; 2.3%) and Semi-arid High CV (BP; 2.5% and FP; 1.7%). Nematodes showed only a few associations with bacteria (BN) and fungi (FN), together representing less than 1.5% of the total number of associations across all three networks. Additionally, the bacteria-bacteria associations presented the highest positive-to-negative link ratio, while bacteria-fungi had the lowest ratio in all three networks. The keystone taxa are predominantly fungal pathothrophs, saprotroph and oligotrophic bacteria The ZiPi-plot was used to illustrate the topological roles of nodes, effectively identifying key populations or functional groups within each network. The nodes were classified into four categories on the basis of their values of Zi and Pi, which were peripherals, connectors, module hubs, and network hubs (Fig. 3 ). In Semi-arid Low CV, there were a total of 17 connectors and one module hub (identified as genus Alternaria, Ascomycota), while semi-arid High had 19 connectors but no module hub. The connectors associated with Semi-arid Low CV were mostly dominated by Actinobacteria (29.41%) followed by Ascomycota (17.64%), Chloroflexi (11.76%) and Alpha-proteobacteria (11.76%) (Table 2 ). In contrast, Ascomycota (47.36%) dominated in Semi-arid High CV, followed by Actinobacteria (21.05%) and Chloroflexi (10.52%). On the other hand, Arid had only one connector and one module hub, both of which were Alpha-proteobacteria of the genera Balneimonas and Pseudonocardia. Interestingly, the identified keystone taxa are mostly oligotrophic bacteria, along with fungal pathotrophs followed by fungal saprotrophs, which collectively function as major key connectors of belowground multitrophic groups (Table 2 ). Table 2 Keystone groups identified using the ZiPi plot, showing their classification at the highest taxonomic level and their associated trophic modes. Group Phylum taxa Trophic group Semi-arid Low CV Module Hub (Generalist) Fungi Ascomycota Alternaria (genus) Pathothroph Connectors (Generalist) Bacteria Actinobacteria Actinomycetales (order) Oligotroph Bacteria Actinobacteria Frankiaceae (family) Oligotroph Bacteria Actinobacteria Geodermatophilus (genus) Oligotroph Bacteria Actinobacteria Microbacteriaceae (family) Oligotroph Bacteria Actinobacteria Amycolatopsis (genus) Oligotroph Bacteria Actinobacteria Solirubrobacteraceae (family) Oligotroph Bacteria Chloroflexi Thermomicrobia (class) Oligotroph Bacteria Gemmatimonadetes Gemmatimonadetes (class) Oligotroph Bacteria Delta-proteobacteria Cystobacteraceae (family) Copiotroph Bacteria Alpha-proteobacteria Rhizobiales (order) Oligotroph Bacteria Alpha-proteobacteria Acetobacteraceae (family) Oligotroph Fungi Ascomycota Idriella (genus) Saprotroph Fungi Ascomycota Didymellaceae (family) Pathothroph Fungi Ascomycota Wojnowicia_viburni (species) Pathothroph Fungi Basidiomycota Naganishia (genus) Saprotroph Protist Rhizaria Filosa-Thecofilosea consumer Protist Rhizaria Filosa-Sarcomonadea consumer Semi-arid High CV Connectors (Generalist) Bacteria Actinobacteria Georgenia (genus) Oligotroph Bacteria Actinobacteria Nocardiacea (family) Oligotroph Bacteria Actinobacteria Actinomycetospora (genus) Oligotroph Bacteria Actinobacteria Gaiellaceae (family) Oligotroph Bacteria Chloroflexic Ktedonobacteria (class) Oligotroph Bacteria Chloroflexic Thermomicrobia (class) Oligotroph Bacteria Cyanobacteria Xenococcaceae (family) Copiotroph Bacteria Delta-proteobacteria Syntrophobacteraceae (family) Copiotroph Fungi Ascomycota Botryosphaeriaceae (family) Pathotroph Fungi Ascomycota Didymosphaeriaceae (family) Pathotroph Fungi Ascomycota Ophiobolus_malleolus (species) Pathotroph Fungi Ascomycota Alternaria (genus) Pathotroph Fungi Ascomycota Didymocrea_sadasivanii (species) Pathotroph Fungi Ascomycota Eurotiomycetes Saprotroph Fungi Ascomycota Exophiala_jeanselmei (species) Saprotroph Fungi Ascomycota Verrucaria_macrostoma (species) Saprotroph Fungi Ascomycota Gibberella_tricincta (species) Saprotroph Protist Archaeplastida Embryophyceae Phototroph Protist Stramenopiles Bacillariophyta Phototroph Arid Module Hub (Generalist) Bacteria Alpha-Proteobacteria Balneimonas (genus) Oligotroph Connectors (Generalist) Bacteria Alpha-Proteobacteria Pseudonocardia (genus) Copiotroph Correlation between network modules, keystone groups, and environmental variables Correlations between network modules and environmental variables revealed significant relationships with vegetation and soil attributes. In Semi-arid Low CV, Module M3 was positively associated with phosphorus content (r = 0.6, P = 0.008), while Module M7 and M2 showed negative (r = -0.05, P = 0.02) and positive (r = 0.49, P = 0.04) correlations with standing biomass (SB), respectively. In Semi-arid High CV, Module M2 was positively correlated with plant richness (VR) (r = 0.6, P = 0.009), whereas Module M6 was negatively associated with SB (r = -0.6, P = 0.009). For the Arid network, Module M3 was negatively correlated with pH (r = -0.6, P = 0.008) and SB (r = -0.6, P = 0.003), and Module M2 with VR (r = -0.5, P = 0.02). In general, bacteria-dominated modules were negatively correlated with SB, whereas symbiotroph and saprotroph-dominated modules were positively associated with SB and VR. Additionally, protist-dominated modules correlated positively with phosphorus. These findings underscore the distinct environmental responses of different submodules across dryland networks, with key abiotic factors impacting specific groups Additionally, Spearman correlation analysis between the identified keystone groups and environmental variables revealed that most keystone taxa, including fungal pathotrophs and bacterial oligotrophs, were strongly positively associated with MAP, OYR and MAT. Fungal saprotrophs, on the other hand, were primarily correlated with OYR. In contrast, TMR exhibited strong negative associations with the identified keystone groups (Fig. 4 ). Discussion Altered rainfall patterns resulting from climate change can have detrimental consequences for biodiversity in semi-arid and arid ecosystems, impacting organisms across all trophic levels (Oliverio et al., 2020 ; Jiao et al., 2022). We studied the community composition of four key taxa, i.e., bacteria, fungi, protists, and nematodes, and found that all were significantly different among three distinct climatic conditions (Fig. S1 ), specifically Arid, Semi-arid High CV and Semi-arid Low CV. Our results also indicate that protists and nematodes can be affected by varying rainfall and aridity to the same extent as bacterial and fungal communities. However, consistent with our previous findings (Maisnam et al. 2023 ), rainfall treatment effects were not observed for any of the communities. The distinct clustering patterns observed for bacterial and fungal communities in Arid, Semi-arid Low CV, and Semi-arid High CV sites suggest that environmental factors associated with these regions shape the structure of microbial communities. Specifically, we found that MAP and soil properties such as pH, soil total carbon and nitrogen content, influence belowground community composition (Fig. 1 ). These results are in line with previous studies, illustrating how multiple abiotic factors drive belowground communities, with differences in aridity associated with rainfall being a major factor (Maestre et al., 2015; Delgado-Baquerizo et al., 2017; 2020). Aridity has been found to impact the structure of soil microbiome networks (Delgado-Baquerizo, Doulcier, et al. 2020). Similarly, the Arid multitrophic network was the most complex, exhibiting the greatest number of nodes and edges, followed by the Semi-arid Low CV and Semi-arid High CV networks. Specifically, the analysis showed that as aridity increased, the proportion of significant positive associations among the nodes of bacterial domains, especially of oligotrophs, increased. Previous research has similarly shown an increase in bacterial abundance and positive associations with increasing aridity (Delgado-Baquerizo et al. 2020; Liu et al. 2022 ). Furthermore, the higher modularity observed in Arid and Semi-arid Low CV networks (Table 1 ) suggests meaningful compartmentalized multitrophic networks. This is further supported by varying responses of different modules to environmental factors (Table S4), suggesting a significant impact on the constituents of certain members of some submodules, particularly those strongly associated with soil phosphorous content and vegetation. These compartmentalized structures contribute to the diversity, stability, and resilience of ecological communities by structuring interactions into distinct units, each with its own attributes and dynamics (Stouffer and Bascompte 2011). In addition, the differences observed in Semi-arid and Arid networks were driven by bacterial and fungal abundance and their interactions. The Semi-arid High CV had more fungal nodes and negative bacterial-fungal interactions, suggesting a less disturbed network (Coyte et al. 2015 ; Herren and McMahon 2017 ). Bacterial and fungal abundance influenced protist and nematode community structure, with more protist-bacteria associations in the Semi-arid Low CV, indicating active protist predation (Geisen et al. 2021 ). Analysis of topological roles further revealed that Semi-arid networks have a higher abundance of putative keystone taxa (module hubs and connectors) compared to the Arid network (Fig. 4 ). This finding suggests a higher level of multitrophic interconnectedness in semi-arid compared to arid ecosystems. Additionally, the presence of only two keystone taxa in the Arid network may be associated with the formation of fewer, but larger modules (Gao et al. 2022 ), as observed in our study. Among these keystone taxa, oligotrophic bacteria and fungal pathotrophs and saprotrophs belonging to phyla Actinobacteria, Alpha-proteobacteria, Chloroflexi and Ascomycota were found to dominate the networks. Similarly, previous research has suggested that arid conditions promote slow-growing, drought-adapted microbial taxa, leading to the co-occurrence of oligotrophic organisms (Delgado-Baquerizo et al. 2020). Moreover, the dominance of oligotrophs, as highlighted in the differential abundance analysis across the three climatic conditions (Table S3), underscores their ecological dominance and significance in drylands. Conversely, the higher dominance of fungal pathotrophs highlights a significant ecological concern. Interestingly, many of these fungal pathotrophs, including Alternaria sp., Ophiobolus malleolus , and Brotryosphaeriaceae family, are opportunistic pathogens that thrive under stress conditions, particularly under conditions where plants are stressed (Lahlali et al. 2024 ). This observation aligns with the understanding that environmental stresses induced by climate change can increase the susceptibility of plants to fungal invasions, thereby compromising plant health and mortality rates (Devendra, 2012). This trend supports with the broader observations of rising soil-borne pathogens under climate change, particularly in response to warming temperatures (Delgado-Baquerizo et al. 2020; Glassman et al. 2018 ). While this study also observed correlations with temperature, the primary driver remains the variation in mean annual rainfall (Fig. 4 ). The increased prevalence of opportunistic fungal pathogens under dry conditions may exacerbate plant stress, leading to reduced plant resilience and productivity in arid regions (Delgado-Baquerizo et al. 2020). This study also found a higher abundance of saprotrophs underscoring their role as key taxa in nutrient redistribution. These fungi function as nutrient ‘miners,’ breaking down leaf litter and complex organic substances to acquire energy and nutrients, which may explain their adaptability and survival under stress (Cao et al. 2022 ). Some of the identified pathotrophs may function primarily as saprotrophs, while certain saprotrophic fungi, such as Idrella and Eurotiomycetes , can also switch to act as opportunistic pathogens under stress. Given climate change, it is likely that opportunistic fungal pathogen groups will become more prevalent in the future as environmental stressors intensify. In addition, keystone protist functional groups, such as consumers and phototrophs ( Rhizaria, Archaeplastida, and Stramenopiles ), were influenced by bacteria and fungi, with protist consumers dominating in bacterial-dominated networks and phototrophs in fungal-dominated ones. Nematodes were less prominent in multitrophic networks, highlighting that smaller organisms drive processes while larger organisms regulate functional processes (Delgado-Baquerizo et al., 2017; Jiao et al., 2022). Overall, our findings emphasize the importance of dominant bacterial and fungal groups, particularly oligotrophs and pathotrophs, which play crucial roles in shaping soil food web complexity and responses to climate change. While this study offers insights into soil food web complexity and trophic modes, it is important to acknowledge its potential limitations. Correlation-based co-occurrence networks provide a simplified view and may not fully represent real-world soil food webs (Goberna and Verdú 2022b). These networks can produce spurious results and may not capture the complete architecture and connectedness of soil ecosystems. However, they remain useful for estimating species relationships and understanding the impact of network complexity on biodiversity and ecosystem functioning (Deng et al., 2012 ; Banerjee et al., 2018 ; Delgado-Baquerizo et al., 2020; Felipe-Lucia et al., 2020). Additionally, sequencing methods may underrepresent larger soil invertebrates such as nematodes based on this approach. Nonetheless, a few studies have successfully used this approach to estimate soil invertebrate biodiversity, and it remains a useful tool for investigating the diversity of smaller soil invertebrates (Schenk et al. 2019 ; Xiong et al. 2020 ). Conclusion This study provides critical insights into the co-occurrence patterns of bacterial, fungal, protist, and nematode communities in semi-arid and arid ecosystems. We demonstrate that increased aridity enhances positive associations among oligotrophic bacteria, resulting in networks dominated by drought-adapted taxa. Fungal pathotrophs and bacterial oligotrophs emerged as central drivers in shaping the belowground food web, highlighting their pivotal ecological roles under future climate scenarios. Of particular concern is the rising dominance of fungal pathogens, which poses a significant threat to ecosystem health and stability in the face of climate change. These findings underscore the importance of investigating multitrophic interactions to better predict and mitigate the broader impacts of climate change on semi-arid and arid ecosystems. Declarations Availability of data and materials The raw sequencing data generated in this study are available in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1241245 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1241245). Associated metadata, including environmental variables, soil physicochemical properties, and plant richness and standing biomass, and ASV tables used in network construction, along with network properties and cytoscape file generated using both RMT and SparCC methods, are publicly accessible via Figshare at https://doi.org/10.6084/m9.figshare.28672310. Acknowledgements We thank Chelsea Maier, Kamrul Hassan, Giles Ross, Samantha Travers, Arjunan Krishnananthaselvan, Casper Quist and Jara Domínguez‐Begines for their involvement in field site establishment, sample collection and processing. We thank the Next generation sequencing facility at WSU for processing our DNA samples. Funding This work was supported by Australian Research Council (DP150104199; DP190101968) and Hawkesbury Institute of Environment, Western Sydney University (WSU). Contribution PM conceived t he project and design, led the data collection and analysis, and wrote the manuscript with guidance by TJ and UN. JS and DB helped during soil sampling and processing. UN secured funding for the project and established the experimental framework. All authors contributed to the manuscript. Corresponding author Correspondence to Premchand Maisnam. Email: [email protected] Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Austin, Amy T., Laura Yahdjian, John M. Stark, Jayne Belnap, Amilcare Porporato, Urszula Norton, Damián A. Ravetta, and Sean M. Schaeffer. 2004. ‘Water Pulses and Biogeochemical Cycles in Arid and Semiarid Ecosystems’. Oecologia . doi: 10.1007/s00442-004-1519-1. Banerjee, Samiran, Klaus Schlaeppi, and Marcel G. A. van der Heijden. 2018. ‘Keystone Taxa as Drivers of Microbiome Structure and Functioning’. Nature Reviews Microbiology 16(9):567–76. Barberán, Albert, Scott T. Bates, Emilio O. Casamayor, and Noah Fierer. 2012. ‘Using Network Analysis to Explore Co-Occurrence Patterns in Soil Microbial Communities’. ISME Journal . doi: 10.1038/ismej.2011.119. Bardgett, Richard D., and Wim H. Van Der Putten. 2014. ‘Belowground Biodiversity and Ecosystem Functioning’. Nature 515. doi: 10.1038/nature13855. Bolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, Eric J. Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E. Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J. Brislawn, C. Titus Brown, Benjamin J. Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily K. Cope, Ricardo Da Silva, Christian Diener, Pieter C. Dorrestein, Gavin M. Douglas, Daniel M. Durall, Claire Duvallet, Christian F. Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M. Gauglitz, Sean M. Gibbons, Deanna L. Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin A. Huttley, Stefan Janssen, Alan K. Jarmusch, Lingjing Jiang, Benjamin D. Kaehler, Kyo Bin Kang, Christopher R. Keefe, Paul Keim, Scott T. Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan G. I. Langille, Joslynn Lee, Ruth Ley, Yong Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D. Martin, Daniel McDonald, Lauren J. McIver, Alexey V. Melnik, Jessica L. Metcalf, Sydney C. Morgan, Jamie T. Morton, Ahmad Turan Naimey, Jose A. Navas-Molina, Louis Felix Nothias, Stephanie B. Orchanian, Talima Pearson, Samuel L. Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S. Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R. Spear, Austin D. Swafford, Luke R. Thompson, Pedro J. Torres, Pauline Trinh, Anupriya Tripathi, Peter J. Turnbaugh, Sabah Ul-Hasan, Justin J. J. van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C. Weber, Charles H. D. Williamson, Amy D. Willis, Zhenjiang Zech Xu, Jesse R. Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight, and J. Gregory Caporaso. 2019. ‘Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2’. Nature Biotechnology . Callahan, Benjamin J., Paul J. McMurdie, Michael J. Rosen, Andrew W. Han, Amy Jo A. Johnson, and Susan P. Holmes. 2016. ‘DADA2: High-Resolution Sample Inference from Illumina Amplicon Data’. Nature Methods . doi: 10.1038/nmeth.3869. Cao, Tingting, You Fang, Yunru Chen, Xiangshi Kong, Junbo Yang, Hattan Alharbi, Yakov Kuzyakov, and Xingjun Tian. 2022. ‘Synergy of Saprotrophs with Mycorrhiza for Litter Decomposition and Hotspot Formation Depends on Nutrient Availability in the Rhizosphere’. Geoderma 410:115662. doi: 10.1016/J.GEODERMA.2021.115662. Chen, Junda, Yuan Yao, Gui Wang, Xiaoyue Zhong, Tianxue Yang, and Wei Sun. 2021. ‘Decreased Precipitation Frequency Altered Abundance, but Not Community Structure, of Soil Nematodes in Degraded Grasslands’. Ecological Indicators 131:108184. doi: 10.1016/J.ECOLIND.2021.108184. Chieppa, Jeff, Sally A. Power, David T. Tissue, and Uffe N. Nielsen. 2020. ‘Allometric Estimates of Aboveground Biomass Using Cover and Height Are Improved by Increasing Specificity of Plant Functional Groups in Eastern Australian Rangelands’. Rangeland Ecology & Management 73(3):375–83. doi: 10.1016/J.RAMA.2020.01.009. Coyte, Katharine Z., Jonas Schluter, and Kevin R. Foster. 2015. ‘The Ecology of the Microbiome: Networks, Competition, and Stability’. Science 350(6261):663–66. doi: 10.1126/SCIENCE.AAD2602/SUPPL_FILE/COYTE.SM.PDF. Delgado-Baquerizo, Manuel, Guilhem Doulcier, David J. Eldridge, Daniel B. Stouffer, Fernando T. Maestre, Juntao Wang, Jeff R. Powell, Thomas C. Jeffries, and Brajesh K. Singh. 2020. ‘Increases in Aridity Lead to Drastic Shifts in the Assembly of Dryland Complex Microbial Networks’. Land Degradation and Development 31(3):346–55. doi: 10.1002/ldr.3453. Delgado-Baquerizo, Manuel, David J. Eldridge, Victoria Ochoa, Beatriz Gozalo, Brajesh K. Singh, and Fernando T. Maestre. 2017. ‘Soil Microbial Communities Drive the Resistance of Ecosystem Multifunctionality to Global Change in Drylands across the Globe’. Ecology Letters 20(10):1295–1305. doi: 10.1111/ELE.12826. Delgado-Baquerizo, Manuel, Carlos A. Guerra, Concha Cano-Díaz, Eleonora Egidi, Jun Tao Wang, Nico Eisenhauer, Brajesh K. Singh, and Fernando T. Maestre. 2020. ‘The Proportion of Soil-Borne Pathogens Increases with Warming at the Global Scale’. Nature Climate Change 2020 10:6 10(6):550–54. doi: 10.1038/s41558-020-0759-3. Delgado-Baquerizo, Manuel, Fernando T. Maestre, Peter B. Reich, Thomas C. Jeffries, Juan J. Gaitan, Daniel Encinar, Miguel Berdugo, Colin D. Campbell, and Brajesh K. Singh. 2016. ‘Microbial Diversity Drives Multifunctionality in Terrestrial Ecosystems’. Nature Communications 2015 7:1 7(1):1–8. doi: 10.1038/ncomms10541. Delgado-Baquerizo, Manuel, Peter B. Reich, Chanda Trivedi, David J. Eldridge, Sebastián Abades, Fernando D. Alfaro, Felipe Bastida, Asmeret A. Berhe, Nick A. Cutler, Antonio Gallardo, Laura García-Velázquez, Stephen C. Hart, Patrick E. Hayes, Ji Zheng He, Zeng Yei Hseu, Hang Wei Hu, Martin Kirchmair, Sigrid Neuhauser, Cecilia A. Pérez, Sasha C. Reed, Fernanda Santos, Benjamin W. Sullivan, Pankaj Trivedi, Jun Tao Wang, Luis Weber-Grullon, Mark A. Williams, and Brajesh K. Singh. 2020. ‘Multiple Elements of Soil Biodiversity Drive Ecosystem Functions across Biomes’. Nature Ecology & Evolution 2020 4:2 4(2):210–20. doi: 10.1038/s41559-019-1084-y. Deng, Ye, Yi Huei Jiang, Yunfeng Yang, Zhili He, Feng Luo, and Jizhong Zhou. 2012. ‘Molecular Ecological Network Analyses’. BMC Bioinformatics 13(1):1–20. doi: 10.1186/1471-2105-13-113/FIGURES/6. Devendra, C. n.d. ‘ASM Series on Climate Change Climate Change Threats and Effects: Challenges for Agriculture and Food Security’. Felipe-Lucia, María R., Santiago Soliveres, Caterina Penone, Markus Fischer, Christian Ammer, Steffen Boch, Runa S. Boeddinghaus, Michael Bonkowski, François Buscot, Anna Maria Fiore-Donno, Kevin Frank, Kezia Goldmann, Martin M. Gossner, Norbert Hölzel, Malte Jochum, Ellen Kandeler, Valentin H. Klaus, Till Kleinebecker, Sophia Leimer, Peter Manning, Yvonne Oelmann, Hugo Saiz, Peter Schall, Michael Schloter, Ingo Schöning, Marion Schrumpf, Emily F. Solly, Barbara Stempfhuber, Wolfgang W. Weisser, Wolfgang Wilcke, Tesfaye Wubet, and Eric Allan. 2020. ‘Land-Use Intensity Alters Networks between Biodiversity, Ecosystem Functions, and Services’. Proceedings of the National Academy of Sciences of the United States of America 117(45). doi: 10.1073/pnas.2016210117. Feng, Kai, Xi Peng, Zheng Zhang, Songsong Gu, Qing He, Wenli Shen, Zhujun Wang, Danrui Wang, Qiulong Hu, Yan Li, Shang Wang, and Ye Deng. 2022. ‘INAP: An Integrated Network Analysis Pipeline for Microbiome Studies’. IMeta 1(2). doi: 10.1002/IMT2.13. Gao, Cheng, Ling Xu, Liliam Montoya, Mary Madera, Joy Hollingsworth, Liang Chen, Elizabeth Purdom, Vasanth Singan, John Vogel, Robert B. Hutmacher, Jeffery A. Dahlberg, Devin Coleman-Derr, Peggy G. Lemaux, and John W. Taylor. 2022. ‘Co-Occurrence Networks Reveal More Complexity than Community Composition in Resistance and Resilience of Microbial Communities’. Nature Communications 2022 13:1 13(1):1–12. doi: 10.1038/s41467-022-31343-y. Geisen, Stefan, Shunran Hu, • Thomas, Edison E. Dela Cruz, • G F ( Ciska, and ) Veen. 2021. ‘Protists as Catalyzers of Microbial Litter Breakdown and Carbon Cycling at Different Temperature Regimes’. The ISME Journal 15:618–21. doi: 10.1038/s41396-020-00792-y. Geisen, Stefan, Edward A. D. Mitchell, Sina Adl, Michael Bonkowski, Micah Dunthorn, Flemming Ekelund, Leonardo D. Fernández, Alexandre Jousset, Valentyna Krashevska, David Singer, Frederick W. Spiegel, Julia Walochnik, and Enrique Lara. 2018. ‘Soil Protists: A Fertile Frontier in Soil Biology Research’. FEMS Microbiology Reviews 42(3):293–323. doi: 10.1093/FEMSRE/FUY006. Glassman, Sydney I., Claudia Weihe, Junhui Li, Michaeline B. N. Albright, Caitlin I. Looby, Adam C. Martiny, Kathleen K. Treseder, Steven D. Allison, and Jennifer B. H. Martiny. 2018. ‘Decomposition Responses to Climate Depend on Microbial Community Composition’. Proceedings of the National Academy of Sciences of the United States of America 115(47):11994–99. doi: 10.1073/PNAS.1811269115/SUPPL_FILE/PNAS.1811269115.SAPP.PDF. Goberna, Marta, and Miguel Verdú. 2022a. ‘Cautionary Notes on the Use of Co-Occurrence Networks in Soil Ecology’. Soil Biology and Biochemistry 166:108534. doi: 10.1016/J.SOILBIO.2021.108534. Goberna, Marta, and Miguel Verdú. 2022b. ‘Cautionary Notes on the Use of Co-Occurrence Networks in Soil Ecology’. Soil Biology and Biochemistry 166. Guimerà, Roger, and Luis A. Nunes Amaral. 2005. ‘Functional Cartography of Complex Metabolic Networks’. Nature 433(7028). doi: 10.1038/nature03288. Herlemann, Daniel P. R., Matthias Labrenz, Klaus Jürgens, Stefan Bertilsson, Joanna J. Waniek, and Anders F. Andersson. 2011. ‘Transitions in Bacterial Communities along the 2000 Km Salinity Gradient of the Baltic Sea’. The ISME Journal 5(10):1571–79. doi: 10.1038/ISMEJ.2011.41. Herren, Cristina M., and Katherine D. McMahon. 2017. ‘Cohesion: A Method for Quantifying the Connectivity of Microbial Communities’. The ISME Journal 2017 11:11 11(11):2426–38. doi: 10.1038/ismej.2017.91. van den Hoogen, Johan, Stefan Geisen, Devin Routh, Howard Ferris, Walter Traunspurger, David A. Wardle, Ron G. M. de Goede, Byron J. Adams, Wasim Ahmad, Walter S. Andriuzzi, Richard D. Bardgett, Michael Bonkowski, Raquel Campos-Herrera, Juvenil E. Cares, Tancredi Caruso, Larissa de Brito Caixeta, Xiaoyun Chen, Sofia R. Costa, Rachel Creamer, José Mauro da Cunha Castro, Marie Dam, Djibril Djigal, Miguel Escuer, Bryan S. Griffiths, Carmen Gutiérrez, Karin Hohberg, Daria Kalinkina, Paul Kardol, Alan Kergunteuil, Gerard Korthals, Valentyna Krashevska, Alexey A. Kudrin, Qi Li, Wenju Liang, Matthew Magilton, Mariette Marais, José Antonio Rodríguez Martín, Elizaveta Matveeva, El Hassan Mayad, Christian Mulder, Peter Mullin, Roy Neilson, T. A. Duong Nguyen, Uffe N. Nielsen, Hiroaki Okada, Juan Emilio Palomares Rius, Kaiwen Pan, Vlada Peneva, Loïc Pellissier, Julio Carlos Pereira da Silva, Camille Pitteloud, Thomas O. Powers, Kirsten Powers, Casper W. Quist, Sergio Rasmann, Sara Sánchez Moreno, Stefan Scheu, Heikki Setälä, Anna Sushchuk, Alexei V. Tiunov, Jean Trap, Wim van der Putten, Mette Vestergård, Cecile Villenave, Lieven Waeyenberge, Diana H. Wall, Rutger Wilschut, Daniel G. Wright, Jiue in Yang, and Thomas Ward Crowther. 2019. ‘Soil Nematode Abundance and Functional Group Composition at a Global Scale’. Nature 572(7768). doi: 10.1038/s41586-019-1418-6. Huang, J., Y. Li, C. Fu, F. Chen, Q. Fu, A. Dai, M. Shinoda, Z. Ma, W. Guo, Z. Li, L. Zhang, Y. Liu, H. Yu, Y. He, Y. Xie, X. Guan, M. Ji, L. Lin, S. Wang, H. Yan, and G. Wang. 2017. ‘Dryland Climate Change: Recent Progress and Challenges’. Reviews of Geophysics 55(3):719–78. doi: 10.1002/2016RG000550. Ihrmark, Katarina, Inga T. M. Bödeker, Karelyn Cruz-Martinez, Hanna Friberg, Ariana Kubartova, Jessica Schenck, Ylva Strid, Jan Stenlid, Mikael Brandström-Durling, Karina E. Clemmensen, and Björn D. Lindahl. 2012. ‘New Primers to Amplify the Fungal ITS2 Region – Evaluation by 454-Sequencing of Artificial and Natural Communities’. FEMS Microbiology Ecology 82(3):666–77. doi: 10.1111/J.1574-6941.2012.01437.X. Jiang, Ying, Zhonghua Wang, Ye Liu, Yanlai Han, Yi Wang, Qiang Wang, and Ting Liu. 2023. ‘Nematodes and Their Bacterial Prey Improve Phosphorus Acquisition by Wheat’. New Phytologist 237(3):974–86. doi: 10.1111/NPH.18569. Lahlali, Rachid, Taoussi Mohammed, Salah-Eddine Laasli, Grace Gachara, Rachid Ezzouggari, Zine Belabess, Kamal Aberkani, Amine Assougeum, Abdelilah Meddich, Moussa El Jarroudi, and Essaid Ait Barka. 2024. ‘Effects of Climate Change on Plant Pathogens and Host-Pathogen Interactions’. Crop and Environment 3(3):159–70. doi: 10.1016/J.CROPE.2024.05.003. Liu, Nana, Huifeng Hu, Wenhong Ma, Ye Deng, Dimitar Dimitrov, Qinggang Wang, Nawal Shrestha, Xiangyan Su, Kai Feng, Yuqing Liu, Baihui Hao, Xinying Zhang, Xiaojuan Feng, and Zhiheng Wang. 2022. ‘Relationships Between Soil Microbial Diversities Across an Aridity Gradient in Temperate Grasslands: Soil Microbial Diversity Relationships’. Microbial Ecology 85(3):1013–27. doi: 10.1007/S00248-022-01997-8/TABLES/2. Lu, Lihua, Shixue Yin, Xing Liu, Wenming Zhang, Tianyu Gu, Qirong Shen, and Huizhen Qiu. 2013. ‘Fungal Networks in Yield-Invigorating and -Debilitating Soils Induced by Prolonged Potato Monoculture’. Soil Biology and Biochemistry 65:186–94. doi: 10.1016/J.SOILBIO.2013.05.025. Lucena-Aguilar, Gema, Ana María Sánchez-López, Cristina Barberán-Aceituno, José Antonio Carrillo-Ávila, José Antonio López-Guerrero, and Rocío Aguilar-Quesada. 2016. ‘DNA Source Selection for Downstream Applications Based on DNA Quality Indicators Analysis’. in Biopreservation and Biobanking . Luo, Jipeng, Xinyu Guo, Qi Tao, Jinxing Li, Yuankun Liu, Yilin Du, Yuying Liu, Yongchao Liang, and Tingqiang Li. 2021. ‘Succession of the Composition and Co-Occurrence Networks of Rhizosphere Microbiota Is Linked to Cd/Zn Hyperaccumulation’. Soil Biology and Biochemistry 153:108120. doi: 10.1016/J.SOILBIO.2020.108120. Maestre, Fernando T., Manuel Delgado-Baquerizo, Thomas C. Jeffries, David J. Eldridge, Victoria Ochoa, Beatriz Gozalo, José Luis Quero, Miguel García-Gómez, Antonio Gallardo, Werner Ulrich, Matthew A. Bowker, Tulio Arredondo, Claudia Barraza-Zepeda, Donaldo Bran, Adriana Florentino, Juan Gaitán, Julio R. Gutiérrez, Elisabeth Huber-Sannwald, Mohammad Jankju, Rebecca L. Mau, Maria Miriti, Kamal Naseri, Abelardo Ospina, Ilan Stavi, Deli Wang, Natasha N. Woods, Xia Yuan, Eli Zaady, and Brajesh K. Singh. 2015. ‘Increasing Aridity Reduces Soil Microbial Diversity and Abundance in Global Drylands’. Proceedings of the National Academy of Sciences of the United States of America 112(51):15684–89. doi: 10.1073/PNAS.1516684112/SUPPL_FILE/PNAS.201516684SI.PDF. Maisnam, P., T. C. Jeffries, J. Szejgis, D. Bristol, B. K. Singh, D. J. Eldridge, S. Horn, J. Chieppa, and U. N. Nielsen. 2023. ‘Severe Prolonged Drought Favours Stress-Tolerant Microbes in Australian Drylands’. Microbial Ecology 86(4). doi: 10.1007/s00248-023-02303-w. McMurdie, Paul J., and Susan Holmes. 2013. ‘Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data’. PLOS ONE 8(4):e61217. doi: 10.1371/JOURNAL.PONE.0061217. Neilson, Julia W., Katy Califf, Cesar Cardona, Audrey Copeland, Will van Treuren, Karen L. Josephson, Rob Knight, Jack A. Gilbert, Jay Quade, J. Gregory Caporaso, and Raina M. Maier. 2017. ‘Significant Impacts of Increasing Aridity on the Arid Soil Microbiome’. MSystems 2(3). doi: 10.1128/msystems.00195-16. Nguyen, Nhu H., Zewei Song, Scott T. Bates, Sara Branco, Leho Tedersoo, Jon Menke, Jonathan S. Schilling, and Peter G. Kennedy. 2016. ‘FUNGuild: An Open Annotation Tool for Parsing Fungal Community Datasets by Ecological Guild’. Fungal Ecology 20:241–48. doi: 10.1016/J.FUNECO.2015.06.006. Nguyen, Thi Bao Anh, Qing Lin Chen, Zhen Zhen Yan, Chaoyu Li, Ji Zheng He, and Hang Wei Hu. 2023. ‘Trophic Interrelationships of Bacteria Are Important for Shaping Soil Protist Communities’. Environmental Microbiology Reports . doi: 10.1111/1758-2229.13143. Nielsen, Uffe N., and Becky A. Ball. 2015. ‘Impacts of Altered Precipitation Regimes on Soil Communities and Biogeochemistry in Arid and Semi-Arid Ecosystems’. Global Change Biology 21(4):1407–21. Nilsson, Rolf Henrik, Karl Henrik Larsson, Andy F. S. Taylor, Johan Bengtsson-Palme, Thomas S. Jeppesen, Dmitry Schigel, Peter Kennedy, Kathryn Picard, Frank Oliver Glöckner, Leho Tedersoo, Irja Saar, Urmas Kõljalg, and Kessy Abarenkov. 2019. ‘The UNITE Database for Molecular Identification of Fungi: Handling Dark Taxa and Parallel Taxonomic Classifications’. Nucleic Acids Research . doi: 10.1093/nar/gky1022. Ochoa-Hueso, Raúl, Valentina Arca, Manuel Delgado-Baquerizo, Kelly Hamonts, Juan Piñeiro, Lilia Serrano-Grijalva, Julien Shawyer, and Sally A. Power. 2020. ‘Links between Soil Microbial Communities, Functioning, and Plant Nutrition under Altered Rainfall in Australian Grassland’. Ecological Monographs 90(4):e01424. doi: 10.1002/ECM.1424. Oksanen, Author Jari, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, and R. O’Hara. 2016. ‘Vegan: Community Ecology Package. R Package 2.3-3.’ Journal of Vegetation Science . Oliverio, Angela M., Stefan Geisen, Manuel Delgado-Baquerizo, Fernando T. Maestre, Benjamin L. Turner, and Noah Fierer. 2020. The Global-Scale Distributions of Soil Protists and Their Contributions to Belowground Systems . Peng, Xi, Shang Wang, Miaoxiao Wang, Kai Feng, Qing He, Xingsheng Yang, Weiguo Hou, Fangru Li, Yuxiang Zhao, Baolan Hu, Xiao Zou, and Ye Deng. 2024. ‘Metabolic Interdependencies in Thermophilic Communities Are Revealed Using Co-Occurrence and Complementarity Networks’. Nature Communications 2024 15:1 15(1):1–14. doi: 10.1038/s41467-024-52532-x. Quast, Christian, Elmar Pruesse, Pelin Yilmaz, Jan Gerken, Timmy Schweer, Pablo Yarza, Jörg Peplies, and Frank Oliver Glöckner. 2013. ‘The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools’. Nucleic Acids Research . doi: 10.1093/nar/gks1219. Rillig, Matthias C., Anika Lehmann, Carlos A. Aguilar-Trigueros, Janis Antonovics, Tancredi Caruso, Stefan Hempel, Johannes Lehmann, Kriszta Valyi, Erik Verbruggen, Stavros D. Veresoglou, and Jeff R. Powell. 2016. ‘Soil Microbes and Community Coalescence’. Pedobiologia . doi: 10.1016/j.pedobi.2016.01.001. Rosen, Gail L., Erin R. Reichenberger, and Aaron M. Rosenfeld. 2011. ‘NBC: The Naïve Bayes Classification Tool Webserver for Taxonomic Classification of Metagenomic Reads’. Bioinformatics 27(1):127–29. doi: 10.1093/BIOINFORMATICS/BTQ619. Schenk, Janina, Stefan Geisen, Nils Kleinboelting, and Walter Traunspurger. 2019. ‘Metabarcoding Data Allow for Reliable Biomass Estimates in the Most Abundant Animals on Earth’. Metabarcoding and Metagenomics 3:e46704. doi: 10.3897/MBMG.3.46704. Schenk, Janina, Sebastian Höss, Marvin Brinke, Nils Kleinbölting, Henrike Brüchner-Hüttemann, and Walter Traunspurger. 2020. ‘Nematodes as Bioindicators of Polluted Sediments Using Metabarcoding and Microscopic Taxonomy’. Environment International 143. doi: 10.1016/J.ENVINT.2020.105922. Schimel, David S. 2010. ‘Drylands in the Earth System’. Science . Shokralla, Shadi, Jennifer L. Spall, Joel F. Gibson, and Mehrdad Hajibabaei. 2012. ‘Next-Generation Sequencing Technologies for Environmental DNA Research’. Molecular Ecology 21(8):1794–1805. doi: 10.1111/J.1365-294X.2012.05538.X;REQUESTEDJOURNAL:JOURNAL:1365294X;CSUBTYPE:STRING:SPECIAL;PAGE:STRING:ARTICLE/CHAPTER. Simbolo, Michele, Marisa Gottardi, Vincenzo Corbo, Matteo Fassan, Andrea Mafficini, Giorgio Malpeli, Rita T. Lawlor, and Aldo Scarpa. 2013. ‘DNA Qualification Workflow for Next Generation Sequencing of Histopathological Samples’. PLoS ONE . doi: 10.1371/journal.pone.0062692. Stouffer, Daniel B., and Jordi Bascompte. 2011. ‘Compartmentalization Increases Food-Web Persistence’. Proceedings of the National Academy of Sciences of the United States of America 108(9):3648–52. doi: 10.1073/PNAS.1014353108/-/DCSUPPLEMENTAL. de Vries, Franciska T., and Tancredi Caruso. 2016. ‘Eating from the Same Plate? Revisiting the Role of Labile Carbon Inputs in the Soil Food Web’. Soil Biology and Biochemistry 102:4–9. doi: 10.1016/J.SOILBIO.2016.06.023. Wang, Jianqing, Mao Li, Xuhui Zhang, Xiaoyu Liu, Lianqing Li, Xiuzhen Shi, Hang wei Hu, and Genxing Pan. 2019. ‘Changes in Soil Nematode Abundance and Composition under Elevated [CO2] and Canopy Warming in a Rice Paddy Field’. Plant and Soil 445(1–2). doi: 10.1007/s11104-019-04330-4. Xiong, Dan, Cun Zheng Wei, E. R. Jasper Wubs, G. F. Veen, Wenju Liang, Xiaobo Wang, Qi Li, Wim H. Van der Putten, and Xingguo Han. 2020. ‘Nonlinear Responses of Soil Nematode Community Composition to Increasing Aridity’. Global Ecology and Biogeography 29(1):117–26. doi: 10.1111/GEB.13013. Zhao, Pei shan, Mi shan Guo, Guang lei Gao, Ying Zhang, Guo dong Ding, Yue Ren, and Mobeen Akhtar. 2020. ‘Community Structure and Functional Group of Root-Associated Fungi of Pinus Sylvestris Var. Mongolica across Stand Ages in the Mu Us Desert’. Ecology and Evolution 10(6):3032–42. doi: 10.1002/ECE3.6119. Zhou, Jizhong, Ye Deng, Feng Luo, Zhili He, Qichao Tu, and Xiaoyang Zhi. 2010. ‘Functional Molecular Ecological Networks’. MBio 1(4):169–79. doi: 10.1128/MBIO.00169-10/SUPPL_FILE/MBIO00169-10-ST01.DOC. Zhu, Hongfei, Bailian Li, Ning Ding, Zheng Hua, Xiaoxu Jiang, Hongfei Zhu, Bailian Li, Ning Ding, Zheng Hua, and Xiaoxu Jiang. 2021. ‘A Case Study on Microbial Diversity Impacts of a Wastewater Treatment Plant to the Receiving River’. Journal of Geoscience and Environment Protection 9(4):206–20. doi: 10.4236/GEP.2021.94013. Additional Declarations No competing interests reported. Supplementary Files Supplementarymutitrophicinteractions.docx Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Environmental Microbiome → Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviews received at journal 23 Jun, 2025 Reviews received at journal 21 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 21 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 01 May, 2025 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-6575220","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465105739,"identity":"4da45151-d2f0-4a86-83c2-a9b86b9c9dd8","order_by":0,"name":"Premchand Maisnam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFACxgYQycMP4TETr0VGsoF4LRBgY3CAWC267YebPxfUbOMxvnb8mQRDhXViAyEtZmcS26RnHLvNY3Y7x0yC4Uw6EVoOJLYx87CBtbBJMLYdJkLL+YfNn3n+3eYxnp3+TILxHzFabiQ2SPO23eYxkE4wk2BsIErLwzZp3r7bPBK3c4wtEo6lGxPhsPTHn3m+3bbnn53+8MaHGmtZglpQQQJpykfBKBgFo2AU4AIAFnc+eVJsrx8AAAAASUVORK5CYII=","orcid":"","institution":"W.K. Kellogg Biological Station, Michigan State University","correspondingAuthor":true,"prefix":"","firstName":"Premchand","middleName":"","lastName":"Maisnam","suffix":""},{"id":465105740,"identity":"514d1c51-6c68-4f74-848f-0c6b0e0d12a3","order_by":1,"name":"Thomas C Jeffries","email":"","orcid":"","institution":"Western Sydney University","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"C","lastName":"Jeffries","suffix":""},{"id":465105743,"identity":"4fd7f334-b2c0-4e3f-ba4f-ec0c5d32183d","order_by":2,"name":"Jerzy Szejgis","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jerzy","middleName":"","lastName":"Szejgis","suffix":""},{"id":465105744,"identity":"e873ece9-cf39-4fed-9fce-0ab6b99df511","order_by":3,"name":"Dylan Bristol","email":"","orcid":"","institution":"Western Sydney University","correspondingAuthor":false,"prefix":"","firstName":"Dylan","middleName":"","lastName":"Bristol","suffix":""},{"id":465105745,"identity":"b25668eb-cad7-4c39-b7b8-0efeef381fa7","order_by":4,"name":"Uffe N Nielsen","email":"","orcid":"","institution":"Western Sydney University","correspondingAuthor":false,"prefix":"","firstName":"Uffe","middleName":"N","lastName":"Nielsen","suffix":""}],"badges":[],"createdAt":"2025-05-02 04:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6575220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6575220/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40793-025-00788-1","type":"published","date":"2025-11-19T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83826499,"identity":"31316d2c-9cd5-40cd-bbb2-eacf050cbd8e","added_by":"auto","created_at":"2025-06-03 10:19:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1100827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistance-based redundancy analysis (db-RDA) biplots for bacteria (A), fungi (B), protists (C) and nematodes (D) including only environmental parameters that explained a significant amount of variability in community structure (arrows). The direction of the arrow indicates the direction of maximum change of that variable, whereas the length of the arrow is proportional to the magnitude of change to variables such as MAP, and Total. C, Total. N, CN:ratio, VR, OYR, TMR, and Temp.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6575220/v1/91e2f59ac2062cec3fff114d.png"},{"id":83826410,"identity":"3918f541-7d3b-41d4-b961-d6cdf8fd4cf4","added_by":"auto","created_at":"2025-06-03 10:11:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":464876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMultitrophic co-occurrence networks across the three climatic conditions. A) Semi-arid Low CV, B) Semi-arid High CV, and C) Arid. The networks were separated into modules by the fast-greedy modularity optimization, only modules with five or more nodes were included. Each node represents different prokaryotic or eukaryotic families, and the size of the node is proportional to the number of connections (degree). A blue edge indicates a positive interaction between two individual nodes, while a red dotted edge indicates a negative interaction. The shape of the nodes represents their trophic functional mode. Bar plots representing (D) the number of nodes per treatment for each microbial group and their trophic modes, and (E) the number of positive (blue) and negative (red) links in inter-domain networks for each treatment and within microbial domains.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6575220/v1/f3367ce73a2e6861c2afe6cf.jpeg"},{"id":83826414,"identity":"ed81c32a-5f29-46cf-99af-eb7dd6440f83","added_by":"auto","created_at":"2025-06-03 10:11:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCombined ZiPi scatter plot of network node topology of Semi-arid Low CV, Semi-arid High CV and Arid. Detailed taxonomic information for nodes in module hubs and connectors can be found in Table 2. The taxa (nodes) in the table match the order shown in the figure from the top (module hubs) to bottom (connectors) of each network separately.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6575220/v1/9edfa548beaf8532d039582c.png"},{"id":83826413,"identity":"fdfa4ce8-7e89-49cf-b2ee-8320b1146008","added_by":"auto","created_at":"2025-06-03 10:11:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":173637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpearman correlations between environmental factors and the relative abundances of identified keystone fungal pathotrophs, saprotrophs, oligotrophic bacteria, and individual keystone pathotroph groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6575220/v1/5bac52e48fbb97abb521ae63.png"},{"id":96650110,"identity":"c7046502-0c20-48fb-a52f-d5e70e8f88c0","added_by":"auto","created_at":"2025-11-24 16:07:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2918872,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6575220/v1/1f9cfb92-b3e6-4c7c-b7a1-dbb4b985f8bd.pdf"},{"id":83826416,"identity":"7414a90c-283f-403e-93ad-7a8aa00bfcca","added_by":"auto","created_at":"2025-06-03 10:11:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":779774,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymutitrophicinteractions.docx","url":"https://assets-eu.researchsquare.com/files/rs-6575220/v1/bd0671d61b0cb95d93043c3d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Oligotrophic bacteria and pathotrophic fungi moderate multitrophic interactions in semi-arid and arid environments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal warming is expected to amplify the hydrological cycle, leading to more extreme variation in intra-annual precipitation, resulting in fewer but larger rainfall events and longer periods of dryness (IPCC, 2021). Such changes pose a significant threat to life on Earth, especially in regions that heavily rely on rainfall including arid and semi-arid areas (Austin et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Soil biota play a crucial role in dryland ecological processes, such as nutrient cycling, and net ecosystem productivity (Schimel, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Substantial evidence indicates that these organisms are highly sensitive to changes in rainfall patterns and increasing aridity, which can significantly impact the structure and composition of soil biological communities (Huang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nielsen and Ball \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While bacterial and fungal co-occurrence have been extensively examined under climate change in semi-arid and arid ecosystems, the co-occurrence patterns across multitrophic groups including bacterial, fungal, protist and nematode domains remain relatively under-investigated.\u003c/p\u003e \u003cp\u003eBelowground communities are complex and diverse, with organisms ranging from microscopic bacteria, fungi, and protists to larger invertebrates such as nematodes (Bardgett and Putten, 2014). These organisms form intricate food webs or ecological networks that regulate ecosystem processes (Bardgett and Putten, 2014; Delgado-Baquerizo et al., 2020). The structure and composition of the soil food web play critical roles in shaping energy flux, food web stability, and carbon (C) and nitrogen (N) cycle processes. Bacteria and fungi constitute the base of the soil food web and play critical roles in soil functions, while protists and nematodes play important roles as biological regulators of prey bacteria and fungi (Jiang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oliverio et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Protists and nematodes represent multiple trophic groups, including herbivores, omnivores, predators, and microbial feeders, and thus influence microbial communities and the health of plants and soil processes (Jiang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oliverio et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xiong et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMicrobes, protists and nematodes all require soil water for their activities; hence, shifts in rainfall, particularly in drylands, will have significant consequences. For instance, reduced precipitation has been shown to affect total nematode abundance, although individual feeding groups show differential responses, with bacterivores being particularly sensitive, which might influence bacterial and fungal dominance in arid conditions (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Dylan et al., 2023). Protists are also abundant in dryland soils and are highly sensitive to changes in soil moisture. As active feeders on bacteria and fungi, shifts in precipitation regimes can influence their role in moderating elemental cycling (Geisen et al. 2018; Oliverio et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, microbes, protists and nematodes, which represent a range of life histories and feeding habits, can respond to environmental disturbances and climate change in remarkably different ways (van den Hoogen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Future changes in precipitation patterns may have various effects on the soil biota and potentially alter the relationships and interactions between them. Hence, research that focuses on multitrophic interactions across the soil food web with changing environmental conditions is needed.\u003c/p\u003e \u003cp\u003eThe functioning of an ecosystem depends on the complex interactions among biological, physical and chemical components of the soil and is highly susceptible to climate change (Ochoa-Hueso et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; de Vries and Caruso 2016). Additionally, the phylotypes in soil food webs that have comparable environmental and resource preferences may group together to form dense ecological clusters in ecological networks, which would have significant effects on ecosystem functions (Delgado-Baquerizo et al., 2016; 2020). Thus, identifying the key biotic and abiotic drivers of soil food web structure (including bacteria, fungi, protists and invertebrates) along extensive climatic gradients is a global priority if we are to predict how soil communities respond to changing environmental conditions. Approaches such as network analysis can help fill this research gap, where associations among the soil microbiome can be summarized using ecological network attributes (strong correlations of degree, betweenness and clustering coefficient) that illustrate how interactions among soil taxa change across environmental or climate gradients or in response to correlation disturbances (Coyte et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Felipe-Lucia et al., 2020). Recent studies have also shown evidence of strong co-occurrence of microbial taxa that form a well-defined module in soils across environmental or climatic gradients (Barber\u0026aacute;n et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Delgado-Baquerizo, Doulcier, et al. 2020). A module is a group of nodes connected more densely to each other than to other nodes outside the group. Increasing aridity may alter the relative abundance of modules via reductions in water availability and via changes in soil properties (Delgado-Baquerizo et al. 2020; Maestre et al. 2015). Additionally, network analysis can be used to identify generalist or keystone taxa that may play a central role in the overall stability of belowground multitrophic interactions (Banerjee et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Delgado-Baquerizo et al., 2020). Thus, taking a whole-network approach including bacteria, fungi, protists and nematodes has the potential to advance our knowledge of the soil microbiome and ecosystem responses to global change drivers (e.g., climate change) at both local and global scales (Barber\u0026aacute;n et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Neilson et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rillig et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigated how variations in rainfall and aridity influence soil multitrophic interactions among bacteria, fungi, protists, and nematodes in semi-arid and arid ecosystems. Given that key trophic groups and keystone taxa can shape the composition and diversity of soil food webs, we aimed to identify the trophic groups central to maintaining food web stability under fluctuating rainfall and climatic conditions. The study was driven by two main hypotheses. \u003cb\u003eHypothesis 1\u003c/b\u003e posits that rainfall variations significantly affect multitrophic interactions, leading to increased compartmentalization of microbial communities in more arid conditions. We expect this compartmentalization to be dominated by trophic groups adapted to stress conditions, which may reveal important insights into ecosystem resilience and functionality in response to reduced water availability. Current observation of the increasing dominance of stress-tolerant fungal pathotrophs (Delgado-Baquerizo et al. 2020; Glassman et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and oligotrophic bacteria (Maisnam et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) under increasing drying and warming conditions support \u003cb\u003eHypothesis 2\u003c/b\u003e, which posits that stress-adapted trophic groups, particularly oligotrophic bacteria and fungal pathotrophs, will play a dominant role in mediating belowground multitrophic interactions in semi-arid and arid ecosystems. Owing to their relatively high abundance, these groups are likely to play key central roles. Their responses to varying rainfall and climatic conditions could be crucial for understanding how belowground food webs adapt to climate change, influencing the health and stability of semi-arid and arid ecosystems. These findings provide novel insights into the shifting dynamics of multitrophic interactions across varying rainfall regimes, contributing to a deeper understanding of semi-arid and arid ecosystems responses to climate change.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and sampling\u003c/h2\u003e \u003cp\u003eThis study leveraged rainfall manipulation facilities at six sites in western New South Wales (NSW) and south-western Queensland (QLD), established to assess the effects of altered rainfall regimes on ecosystem structure and function in Australian semi-arid and arid ecosystems. The sites have different vegetation and soil properties, different mean annual rainfall values (ranging from 209 to 494 mm year \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), temperatures (ranging from 18 to 21\u0026ordm; C) and aridity indices (AI; ranging from 0.11 to 0.3) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The six sites span distinct rainfall regimes, with rainfall increasing from west to east and greater interannual variability (CV; ranging from 0.28 to 0.57) observed at the northern sites. For this study, semi-arid sites with higher CV were grouped as 'Semi-arid High CV' group, whereas those with lower CV were classified into the 'Semi-arid Low CV\u0026rsquo; group, and the two remaining arid sites were collectively categorized into the 'Arid\u0026rsquo; group, making three different climatic conditions. By creating these groupings, studies can systematically compare how changes in rainfall variability (high vs. low CV) and different aridity conditions (semi-arid vs. arid) influence ecological processes. This helps identify patterns and drivers of ecological change across a gradient of climatic conditions. The sites were established in 2016 with three different rainfall treatments, representing ambient (control), reduced (-65%) and increased (~\u0026thinsp;65%, but lower given inefficiencies in the transfer of water) rainfall, with 3 replicates at each site (Maisnam et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The rainout shelters, each covering a 3 \u0026times; 3 m plot, were erected above all the plots with corrugated plastic roof slats used to remove approximately 65% of the incoming rain from the reduced rainfall plots, which were collected in gutters and transferred via polyethylene piping to adjacent increased rainfall plots. The shelters above the ambient and increased rainfall plots had a bird mesh to simulate the shelter effect.\u003c/p\u003e \u003cp\u003ePrior to collecting soil samples, we conducted vegetation surveys of each plot, focused on the core 1.5 \u0026times; 1.5 m quadrat to avoid edge effects, counting the number of individuals of each species in each plot to assess composition and species richness. Standing biomass was then estimated using allometric relationships based on the percentage cover of individual plant species via relationships for the same species assessed at individual sites (Chieppa et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). When we were unable to determine the specific species, we used allometric relationships for the most similar functional type.\u003c/p\u003e \u003cp\u003eEight soil cores (3.5 cm diameter, 10 cm deep) were then collected randomly within the central 1.5 \u0026times; 1.5 m quadrat and combined to form a composite sample for each plot, with 3 replicate plots of each treatment per site resulting in a total of 9 samples per site per year. Samples were transported to Hawkesbury Institute of Environment (HIE), Western Sydney University (WSU), subsampled for chemical analyses and DNA extraction with soils for the latter stored at -20\u0026deg;C until further analyses. In this study, we focused on samples collected in spring 2020 only.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil properties\u003c/h3\u003e\n\u003cp\u003eSoil moisture content was estimated by oven drying\u0026thinsp;~\u0026thinsp;10 g fresh soil at 105\u0026deg;C. Soil pH was measured in a 1:5 soil:water slurry using a calibrated pH meter (S20 SevenEasy Mettler Toledo). The total soil C (TC) and total nitrogen (TN) contents were determined via oxidative combustion method using a LECO CN Analyzer (TruMac, LECO Corporation, St Joseph, MI, USA). Total P was determined using an Epsilon 4 Benchtop X-ray fluorescence (XRF) spectrometer (Malvern Panalytical, Malvern, UK).\u003c/p\u003e\n\u003ch3\u003eAmplicon sequencing and processing\u003c/h3\u003e\n\u003cp\u003eBacterial, fungal and protist DNA was extracted from 0.5 g of thawed soil using the DNeasy PowerSoil DNA Isolation Kit (QIAGEN), following the manufacturer\u0026rsquo;s protocol. For nematode samples, 100 g of homogenized soil was processed using a modified Baermann funnel technique over three days, as described by Wang et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The collected nematodes were concentrated via centrifugation, and DNA was subsequently extracted using the same PowerSoil kit. The concentration and purity of all DNA extracts were assessed using a Qubit 4 Fluorometer (Thermo Fisher Scientific) and NanoDrop spectrophotometer (Thermo Fisher Scientific), respectively, adhering to standard protocols (Lucena-Aguilar et al. 2016; Simbolo et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). DNA samples were then submitted to the Western Sydney University Next-Generation Sequencing Facility for amplicon library preparation and sequencing.\u003c/p\u003e \u003cp\u003eAmplicon libraries were prepared using the NEBNext Ultra II Q5 Master Mix (New England Biolabs) in accordance with the manufacturer\u0026rsquo;s instructions. Specific primer pairs were employed to target distinct taxonomic groups: the 341F/805R pair (Herlemann et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) for bacterial 16S rRNA genes, FITS7/ITS4 (Ihrmark et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for fungal internal transcribed spacer (ITS) regions, Euk1391F/EukBr (Zhu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for protist 18S rRNA genes, and 1274F/706R (Schenk et al. 2020) for nematode 28S rRNA genes. Polymerase chain reaction (PCR) conditions were optimized for each amplicon type. For bacterial 16S and nematode 28S rRNA gene amplifications, the thermocycling protocol consisted of an initial denaturation at 95\u0026deg;C for 3 min, followed by 25 cycles of 95\u0026deg;C for 30 s, 55\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s, with a final extension at 72\u0026deg;C for 5 min. For fungal ITS and protist 18S rRNA gene amplifications, the protocol included an initial denaturation at 94\u0026deg;C for 5 min, followed by 25 cycles of 94\u0026deg;C for 30 s, 57\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s, concluding with a final extension at 72\u0026deg;C for 5 min.\u003c/p\u003e \u003cp\u003eIndexing of amplicons was performed using the Nextera XT Index Kit v2 (Illumina) under the following thermocycling conditions: 95\u0026deg;C for 3 min; 8 cycles of 95\u0026deg;C for 30 s, 55\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s; and a final extension at 72\u0026deg;C for 5 min. The indexed libraries were purified using AMPure XP beads (Beckman Coulter) and assessed for fragment size distribution using the Agilent TapeStation 4200 system. Equimolar concentrations of purified libraries were pooled and quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). Sequencing was conducted on the Illumina MiSeq platform employing the MiSeq Reagent Kit v3 (600-cycle), generating paired-end reads of 2 \u0026times; 300 bp (Shokralla et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDownstream sequence data processing was performed using Quantitative Insights into Microbial Ecology software (QIIME 2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://qiime2.org/\u003c/span\u003e\u003cspan address=\"https://qiime2.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Bolyen et al. 2019). Initially, generated raw paired end sequences were demultiplexed and low-quality regions and chimeric sequences were removed using the DADA2 plugin in QIIME2 (Callahan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Feature tables (amplicon sequence variants, ASVs) and feature data (representative sequences) were generated as resulting data from DADA2. Taxonomy was assigned to the ASVs with QIIME2 using a pre-trained Naive Bayes classifier (Rosen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and compared against the SILVA database for bacteria and nematodes, and UNITE database for fungal, and PR2 database for protist classification respectively (Nilsson et al. 2019; Quast et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Following ASV assignments, FUNGuild (Nguyen et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was used to identify fungal trophic mode by grouping each guild into pathotrophs (plant and animal pathogens), saprotrophs (plant, soil and wood saprotroph), and symbiotrophs (arbuscular mycorrhizal fungi, ectomycorrhizal fungi, and endophytes) (Zhao et al. 2020). The functional groups of protists such as consumers, phototrophs and pathothrophs, are classified on the basis of their feeding habits at the genus level (Nguyen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The protist taxa at the genus level are considered to have similar feeding modes. Similarly, all the trophic groups of nematodes, such as bacterivores, fungivores and omnivores, were collectively placed under consumers. While consumers feed mainly on bacteria and other eukaryotes, phototrophs synthesize energy via photosynthesis. Pathotrophs are protists living in soil animals or plants, and other unassigned groups are defined as unknown.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eUnless otherwise noted, statistical analyses were performed in R software (R Core Team, 2020) and visualized using the \u0026ldquo;ggplot2\u0026rdquo; package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ggplot2.tidyverse.org\u003c/span\u003e\u003cspan address=\"https://ggplot2.tidyverse.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Before we conducted the soil organism community analysis, the ASV tables were rarefied to a minimum number of sequences from each sample, at 4,485 for bacteria, 10,526 for fungi, 1,079 for protists, and 412 for nematodes, to ensure even sampling. Community composition across sites and rainfall treatments was visualised using principal coordinate analysis (PCoA) based on Bray-Curtis distance for each of the four taxa in the Phyloseq R package (McMurdie and Holmes 2013). We analysed whether there was a statistical significance of site and treatment using permutational ANOVA (PERMANOVA) with the Adonis function in vegan package in R (McMurdie and Holmes 2013). Additionally, distance-based redundancy analysis (dbRDA) using the Bray-Curtis distance matrix was performed to test the significance and importance of the environmental variables in explaining the variation in community composition across sites and treatments. Soil and environmental parameters were Total.C\u0026thinsp;=\u0026thinsp;total soil carbon, Total.N\u0026thinsp;=\u0026thinsp;total soil nitrogen, Total.P\u0026thinsp;=\u0026thinsp;total soil phosphorous, CN:ratio\u0026thinsp;=\u0026thinsp;soil C:N ratio, VR\u0026thinsp;=\u0026thinsp;plant richness, MAP\u0026thinsp;=\u0026thinsp;mean annual precipitation, OYR\u0026thinsp;=\u0026thinsp;one year rain, TMR\u0026thinsp;=\u0026thinsp;three month rain, and Temp\u0026thinsp;=\u0026thinsp;mean annual temperature (MAT). These analyses were performed using the capscale function of the vegan package (Oksanen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, differential relative abundance analysis was performed using the run_lefse function from the microbiomeMarker package in R to identify significant ASVs associated with the three climatic conditions and rainfall treatments, with adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e\n\u003ch3\u003eCo-occurrence network analysis\u003c/h3\u003e\n\u003cp\u003eMultitrophic co-occurrence networks were constructed using a random matrix theory (RMT)-based approach implemented in the Molecular Ecological Network Analysis Pipeline (MENAP; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ieg4.rccc.ou.edu/mena/\u003c/span\u003e\u003cspan address=\"http://ieg4.rccc.ou.edu/mena/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default parameters (Deng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This method is widely used and has proven effective for constructing multitrophic co-occurrence networks (Delgado-Baquerizo et al. 2020; Jiao, Lu, and Wei 2022), offering results that are comparable across ecological studies and consistent with our own previous analyses conducted at the same experimental site (Maisnam et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Prior to conducting the final network analysis, we compared network topology metrics between MENAP\u0026rsquo;s RMT-based Pearson correlation method and the compositionally aware SparCC approach with default parameters (via the iNAP platform; Feng et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using the combined ASV dataset (n\u0026thinsp;=\u0026thinsp;54 samples) that included bacteria and eukaryotes (fungi, protists, and nematodes). The two methods showed strong concordance across key network metrics (Table S2), consistent with previous evaluations (Hirano and Takemoto, 2019), thereby supporting the reliability and robustness of the RMT-based approach for constructing multitrophic ecological networks.\u003c/p\u003e \u003cp\u003eThe RMT-based approach can automatically define a threshold for cellular network construction and are robust to noise, offering effective solutions to common challenges in high-throughput amplicon sequencing data (Deng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The RMT-based co-occurrence network method used in the MENAP addresses some of the key concerns in ecological network analysis, such as threshold selection, noise, high-dimensional data, and false positives, resulting in more robust, replicable and meaningful ecological co-occurrence networks (Goberna and Verd\u0026uacute; 2022). The ASVs of bacteria and eukaryotes (fungi, protists, and nematodes) were merged into a table and co-occurrence networks were constructed. Three multitrophic co-occurrence networks were constructed comprising Semi-arid High CV (n\u0026thinsp;=\u0026thinsp;18), Semi-arid Low CV (n\u0026thinsp;=\u0026thinsp;18), and Arid (n\u0026thinsp;=\u0026thinsp;18) climatic conditions. To obtain robust associations between soil organisms, we set the threshold of Pearson correlation coefficient to 0.6 and false discovery rate (FDR)-corrected p-values at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, this cutoff is extensively used and comparable across studies (Delgado-Baquerizo et al. 2020; Jiao, Lu, and Wei 2022; Maisnam et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Peng et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Only the ASVs detected in more than half of all samples were retained for network construction.\u003c/p\u003e \u003cp\u003eThe RMT-based approach can delineate separate modules, where each network was separated into modules by the fast-greedy modularity optimization. Each node in a module signifies an ASV and each edge signifies a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) pairwise association calculated based on the Pearson correlation coefficient. The network graph was represented using identified ASVs (nodes) with positive or negative interactions (edges). Positive interactions indicate that the abundances of the two associated ASVs changed following the same trend across different soil samples (i.e., they were positively correlated). Negative interactions indicate that the abundances of those ASVs changed following the opposite trend in different soil samples (Lu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A modularity value measures the integrity of networks and is a fundamental characteristic of biological networks (Zhou et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Each module in a network represents species with similar ecological niches that interact more frequently with each other than with species in other modules (Deng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Modularity is a key metric that gauges the extent to which a network is structured into well-defined modules, and it is a highly significant concept in ecology. Various factors can contribute to modularity, such as the specificity of interactions (such as predation or mutualism), diversity in habitat, resource partitioning, overlapping ecological niches, natural selection, convergent evolution, and phylogenetic relatedness.\u003c/p\u003e \u003cp\u003eTo identify keystone taxa or functional groups, we analysed modular topological roles, which are based on the nodes' roles within their respective modules. The topological role of each node (ASV) was defined by two parameters: within-module connectivity (Zi) and among-module connectivity (Pi) and the ZiPi scatter was plotted in Microsoft Excel. The Zi value determines how well a node is connected to other nodes within its module, whereas the Pi value determined how well a node is connected to nodes in different modules (Guimer\u0026agrave; and Amaral 2005). Within-module connectivity (Zi) and among-module connectivity (Pi) were calculated to identify the keystone ASVs. Previous research proposed threshold values of 2.5 for Zi and 0.62 for Pi, which were used to categorize the nodes into four groups (Guimer\u0026agrave; and Amaral 2005; Zhou et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). (i) Peripheral nodes (specialists) had low Zi (\u0026lt;\u0026thinsp;2.5) and low Pi (\u0026lt;\u0026thinsp;0.62) values. These nodes had only a few links and were almost always connected to the nodes within their own modules. (ii) Connectors (named generalists) had low Zi values (\u0026lt;\u0026thinsp;2.5) but high Pi values (\u0026gt;\u0026thinsp;0.62). These modules were highly connected with other modules. (iii) Module hubs (also named generalists) had high Zi values (\u0026gt;\u0026thinsp;2.5) but low Pi values (\u0026lt;\u0026thinsp;0.62). They were highly connected with many nodes in their own modules. (iv) Network hubs (named supergeneralists) had both high Zi (\u0026gt;\u0026thinsp;2.5) and Pi (\u0026gt;\u0026thinsp;0.62) values. Generalists (connectors, module hubs) and network hubs are the key organisms that play important roles in maintaining network stability.\u003c/p\u003e \u003cp\u003eFinally, the associations between module-based eigengenes and environmental variables were examined to elucidate the modules' responses to environmental changes (Deng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This analysis involved calculating Pearson correlation coefficients (r values) and their corresponding significance levels (p values). Moreover, Spearman correlation analysis was performed in R to explore the relationships between the identified keystone groups and environmental variables, providing insights into how these taxa are influenced by key environmental factors.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSoil biotic communities differed among climatic conditions\u003c/h2\u003e \u003cp\u003eBacterial and eukaryotic community analysis across sites and rainfall treatments indicated that the variation between datasets was predominantly explained by climatic conditions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with no effect of rainfall treatment for any of the four groups (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Both bacterial and fungal communities displayed distinct clustering patterns, with Arid (Broken Hill and Milparinka), Semi-arid High CV (Cobar and Nyngan), and Semi-arid High CV (Charleville and Quilpie) forming separate clusters (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, such clear clustering patterns was not observed for the protist and nematode communities. Distance-based redundancy analysis (dbRDA) demonstrated that, with the exception of fungal communities, all groups presented strong associations with MAP, soil pH, and soil carbon and nitrogen contents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, differential abundance analysis among the three climatic conditions revealed that most of the significant groups belonged to oligotrophic bacteria and saprotrophic fungi (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eArid multitrophic co-occurrence network exhibited stronger clustering compared to semi-arid networks\u003c/h3\u003e\n\u003cp\u003eThe Molecular Ecological Network Analysis (MENA) pipeline was used to infer association networks for bacterial, protist, fungal, and nematode communities. Three separate networks were constructed, i.e., Semi-arid Low CV, Semi-arid High CV and Arid. Our findings revealed that the arid network was the most complex, exhibiting the greatest number of nodes and edges (148 nodes, 546 edges), followed by the Semi-arid Low CV (130 nodes, 302 edges) and Semi-arid High CV (115 nodes, 315 edges) networks (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interestingly, only the Arid network showed more positive associations than negative associations. Thus, the ratio of positive to negative links in the Arid network (1.4) was greater than that in the Semi-arid Low CV (0.86) and Semi-arid High CV (0.56) networks. Compared with those of the Semi-arid High CV (8.081, 0.139) and Arid (7.622, 0.161) netwroks, the average degree (avgk) and average clustering coefficient (avgCC) of Semi-arid Low CV had the lowest values (4.646, 0.08). However, the Arid network had the lowest value of average path distance (GD) compared with both semi-arid networks. This suggests that the nodes of the Arid network were more closely clustered than those of the two other networks. In addition, the modularity value of empirical networks was much higher than that of random networks, as observed in the case of Arid network. Similarly, larger differences can also be observed with the average path distance (GD) which is higher in the empirical network than in the random network (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEmpirical and random network properties of Semi-arid Low, High, and Arid co-occurrence networks.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpirical network properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSemi-arid Low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-arid High\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArid\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of edges\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003eRatio \u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302\u003c/p\u003e \u003cp\u003e140\u003c/p\u003e \u003cp\u003e162\u003c/p\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e501\u003c/p\u003e \u003cp\u003e181\u003c/p\u003e \u003cp\u003e320\u003c/p\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e546\u003c/p\u003e \u003cp\u003e319\u003c/p\u003e \u003cp\u003e227\u003c/p\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage degree (avgK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage clustering coefficient (avgCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage path distance (GD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModularity (no. of modules)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.465 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.296 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.434 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRandom networks properties\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage clustering coefficient (avgCC) (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.066 (\u0026plusmn;\u0026thinsp;0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.211 (\u0026plusmn;\u0026thinsp;0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123 (\u0026plusmn;\u0026thinsp;0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage path distance (GD) (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.181 (\u0026plusmn;\u0026thinsp;0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.624 (\u0026plusmn;\u0026thinsp;0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.788 (\u0026plusmn;\u0026thinsp;0.042)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModularity (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.400 (\u0026plusmn;\u0026thinsp;0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246 (\u0026plusmn;\u0026thinsp;0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297 (\u0026plusmn;\u0026thinsp;0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the Semi-arid Low CV and High CV networks, seven and six modules (with \u0026ge;\u0026thinsp;5 nodes) were respectively obtained, whereas the Arid network had only four such modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The modules within the Arid network comprised three large modules that were closely connected, whereas the Semi-arid High CV network had four larger modules. In contrast, the Semi-arid Low CV network had modules that were more evenly distributed. Most of the modules included all the trophic groups, with only a few represented by a single trophic group (such as the bacteria-dominated Semi-arid Low CV M3 and Arid M3 modules).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome associations differ across semi-arid and arid climatic conditions\u003c/h2\u003e \u003cp\u003eThe comparison between inter-group networks revealed that bacteria had the highest number of nodes, followed by fungi, protists, and nematodes, with varying relative abundances among the networks. The Semi-arid Low CV network had fewer fungal nodes but more protist nodes, while Semi-arid High CV and Arid networks had relatively higher abundances of bacteria, fungi, and nematodes. Notably, when the trophic groups nodes were compared, the Arid network had the highest number of saprotrophs followed by symbiotrophs, pathotrophs and phototrophs with lowest number (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; D).\u003c/p\u003e \u003cp\u003eAdditionally, the study compared the number of positive and negative associations within and between microbiome groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; E). The Semi-arid Low CV and Arid networks were dominated by bacteria-bacteria associations (BB; 40% and 47.25%, respectively) whereas the Semi-arid High CV network was mainly dominated by bacteria-fungi associations (BF; 42%). Fewer associations were observed with protists and nematodes. Protists showed more associations with bacteria and fungi in Semi-arid Low CV (BP; 12.5% and FP; 4.3%) than Arid (BP; 3.8% and FP; 2.3%) and Semi-arid High CV (BP; 2.5% and FP; 1.7%). Nematodes showed only a few associations with bacteria (BN) and fungi (FN), together representing less than 1.5% of the total number of associations across all three networks. Additionally, the bacteria-bacteria associations presented the highest positive-to-negative link ratio, while bacteria-fungi had the lowest ratio in all three networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe keystone taxa are predominantly fungal pathothrophs, saprotroph and oligotrophic bacteria\u003c/h2\u003e \u003cp\u003eThe ZiPi-plot was used to illustrate the topological roles of nodes, effectively identifying key populations or functional groups within each network. The nodes were classified into four categories on the basis of their values of Zi and Pi, which were peripherals, connectors, module hubs, and network hubs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Semi-arid Low CV, there were a total of 17 connectors and one module hub (identified as genus Alternaria, Ascomycota), while semi-arid High had 19 connectors but no module hub. The connectors associated with Semi-arid Low CV were mostly dominated by Actinobacteria (29.41%) followed by Ascomycota (17.64%), Chloroflexi (11.76%) and Alpha-proteobacteria (11.76%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, Ascomycota (47.36%) dominated in Semi-arid High CV, followed by Actinobacteria (21.05%) and Chloroflexi (10.52%). On the other hand, Arid had only one connector and one module hub, both of which were Alpha-proteobacteria of the genera Balneimonas and Pseudonocardia. Interestingly, the identified keystone taxa are mostly oligotrophic bacteria, along with fungal pathotrophs followed by fungal saprotrophs, which collectively function as major key connectors of belowground multitrophic groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKeystone groups identified using the ZiPi plot, showing their classification at the highest taxonomic level and their associated trophic modes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhylum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etaxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrophic group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSemi-arid Low CV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModule Hub (Generalist)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlternaria (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathothroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConnectors (Generalist)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActinomycetales (order)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrankiaceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeodermatophilus (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMicrobacteriaceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmycolatopsis (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSolirubrobacteraceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChloroflexi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermomicrobia (class)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGemmatimonadetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGemmatimonadetes (class)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelta-proteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCystobacteraceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopiotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha-proteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhizobiales (order)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha-proteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcetobacteraceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIdriella (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaprotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDidymellaceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathothroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWojnowicia_viburni (species)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathothroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasidiomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNaganishia (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaprotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhizaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFilosa-Thecofilosea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econsumer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhizaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFilosa-Sarcomonadea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econsumer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSemi-arid High CV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConnectors (Generalist)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeorgenia (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNocardiacea (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eActinomycetospora (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaiellaceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChloroflexic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKtedonobacteria (class)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChloroflexic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermomicrobia (class)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyanobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXenococcaceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopiotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelta-proteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSyntrophobacteraceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopiotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBotryosphaeriaceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathotroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDidymosphaeriaceae (family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathotroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOphiobolus_malleolus (species)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathotroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlternaria (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathotroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDidymocrea_sadasivanii (species)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePathotroph\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEurotiomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaprotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExophiala_jeanselmei (species)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaprotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerrucaria_macrostoma (species)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaprotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGibberella_tricincta (species)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaprotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArchaeplastida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmbryophyceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhototroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStramenopiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBacillariophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhototroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eArid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModule Hub (Generalist)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha-Proteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBalneimonas (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOligotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConnectors (Generalist)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha-Proteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePseudonocardia (genus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCopiotroph\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between network modules, keystone groups, and environmental variables\u003c/h2\u003e \u003cp\u003eCorrelations between network modules and environmental variables revealed significant relationships with vegetation and soil attributes. In Semi-arid Low CV, Module M3 was positively associated with phosphorus content (r\u0026thinsp;=\u0026thinsp;0.6, P\u0026thinsp;=\u0026thinsp;0.008), while Module M7 and M2 showed negative (r = -0.05, P\u0026thinsp;=\u0026thinsp;0.02) and positive (r\u0026thinsp;=\u0026thinsp;0.49, P\u0026thinsp;=\u0026thinsp;0.04) correlations with standing biomass (SB), respectively. In Semi-arid High CV, Module M2 was positively correlated with plant richness (VR) (r\u0026thinsp;=\u0026thinsp;0.6, P\u0026thinsp;=\u0026thinsp;0.009), whereas Module M6 was negatively associated with SB (r = -0.6, P\u0026thinsp;=\u0026thinsp;0.009). For the Arid network, Module M3 was negatively correlated with pH (r = -0.6, P\u0026thinsp;=\u0026thinsp;0.008) and SB (r = -0.6, P\u0026thinsp;=\u0026thinsp;0.003), and Module M2 with VR (r = -0.5, P\u0026thinsp;=\u0026thinsp;0.02). In general, bacteria-dominated modules were negatively correlated with SB, whereas symbiotroph and saprotroph-dominated modules were positively associated with SB and VR. Additionally, protist-dominated modules correlated positively with phosphorus. These findings underscore the distinct environmental responses of different submodules across dryland networks, with key abiotic factors impacting specific groups\u003c/p\u003e \u003cp\u003eAdditionally, Spearman correlation analysis between the identified keystone groups and environmental variables revealed that most keystone taxa, including fungal pathotrophs and bacterial oligotrophs, were strongly positively associated with MAP, OYR and MAT. Fungal saprotrophs, on the other hand, were primarily correlated with OYR. In contrast, TMR exhibited strong negative associations with the identified keystone groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAltered rainfall patterns resulting from climate change can have detrimental consequences for biodiversity in semi-arid and arid ecosystems, impacting organisms across all trophic levels (Oliverio et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiao et al., 2022). We studied the community composition of four key taxa, i.e., bacteria, fungi, protists, and nematodes, and found that all were significantly different among three distinct climatic conditions (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), specifically Arid, Semi-arid High CV and Semi-arid Low CV. Our results also indicate that protists and nematodes can be affected by varying rainfall and aridity to the same extent as bacterial and fungal communities. However, consistent with our previous findings (Maisnam et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), rainfall treatment effects were not observed for any of the communities. The distinct clustering patterns observed for bacterial and fungal communities in Arid, Semi-arid Low CV, and Semi-arid High CV sites suggest that environmental factors associated with these regions shape the structure of microbial communities. Specifically, we found that MAP and soil properties such as pH, soil total carbon and nitrogen content, influence belowground community composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These results are in line with previous studies, illustrating how multiple abiotic factors drive belowground communities, with differences in aridity associated with rainfall being a major factor (Maestre et al., 2015; Delgado-Baquerizo et al., 2017; 2020).\u003c/p\u003e \u003cp\u003eAridity has been found to impact the structure of soil microbiome networks (Delgado-Baquerizo, Doulcier, et al. 2020). Similarly, the Arid multitrophic network was the most complex, exhibiting the greatest number of nodes and edges, followed by the Semi-arid Low CV and Semi-arid High CV networks. Specifically, the analysis showed that as aridity increased, the proportion of significant positive associations among the nodes of bacterial domains, especially of oligotrophs, increased. Previous research has similarly shown an increase in bacterial abundance and positive associations with increasing aridity (Delgado-Baquerizo et al. 2020; Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the higher modularity observed in Arid and Semi-arid Low CV networks (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) suggests meaningful compartmentalized multitrophic networks. This is further supported by varying responses of different modules to environmental factors (Table S4), suggesting a significant impact on the constituents of certain members of some submodules, particularly those strongly associated with soil phosphorous content and vegetation. These compartmentalized structures contribute to the diversity, stability, and resilience of ecological communities by structuring interactions into distinct units, each with its own attributes and dynamics (Stouffer and Bascompte 2011). In addition, the differences observed in Semi-arid and Arid networks were driven by bacterial and fungal abundance and their interactions. The Semi-arid High CV had more fungal nodes and negative bacterial-fungal interactions, suggesting a less disturbed network (Coyte et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Herren and McMahon \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bacterial and fungal abundance influenced protist and nematode community structure, with more protist-bacteria associations in the Semi-arid Low CV, indicating active protist predation (Geisen et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of topological roles further revealed that Semi-arid networks have a higher abundance of putative keystone taxa (module hubs and connectors) compared to the Arid network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding suggests a higher level of multitrophic interconnectedness in semi-arid compared to arid ecosystems. Additionally, the presence of only two keystone taxa in the Arid network may be associated with the formation of fewer, but larger modules (Gao et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as observed in our study. Among these keystone taxa, oligotrophic bacteria and fungal pathotrophs and saprotrophs belonging to phyla Actinobacteria, Alpha-proteobacteria, Chloroflexi and Ascomycota were found to dominate the networks. Similarly, previous research has suggested that arid conditions promote slow-growing, drought-adapted microbial taxa, leading to the co-occurrence of oligotrophic organisms (Delgado-Baquerizo et al. 2020). Moreover, the dominance of oligotrophs, as highlighted in the differential abundance analysis across the three climatic conditions (Table S3), underscores their ecological dominance and significance in drylands. Conversely, the higher dominance of fungal pathotrophs highlights a significant ecological concern. Interestingly, many of these fungal pathotrophs, including \u003cem\u003eAlternaria sp., Ophiobolus malleolus\u003c/em\u003e, and \u003cem\u003eBrotryosphaeriaceae\u003c/em\u003e family, are opportunistic pathogens that thrive under stress conditions, particularly under conditions where plants are stressed (Lahlali et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This observation aligns with the understanding that environmental stresses induced by climate change can increase the susceptibility of plants to fungal invasions, thereby compromising plant health and mortality rates (Devendra, 2012). This trend supports with the broader observations of rising soil-borne pathogens under climate change, particularly in response to warming temperatures (Delgado-Baquerizo et al. 2020; Glassman et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While this study also observed correlations with temperature, the primary driver remains the variation in mean annual rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The increased prevalence of opportunistic fungal pathogens under dry conditions may exacerbate plant stress, leading to reduced plant resilience and productivity in arid regions (Delgado-Baquerizo et al. 2020). This study also found a higher abundance of saprotrophs underscoring their role as key taxa in nutrient redistribution. These fungi function as nutrient \u0026lsquo;miners,\u0026rsquo; breaking down leaf litter and complex organic substances to acquire energy and nutrients, which may explain their adaptability and survival under stress (Cao et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some of the identified pathotrophs may function primarily as saprotrophs, while certain saprotrophic fungi, such as \u003cem\u003eIdrella\u003c/em\u003e and \u003cem\u003eEurotiomycetes\u003c/em\u003e, can also switch to act as opportunistic pathogens under stress. Given climate change, it is likely that opportunistic fungal pathogen groups will become more prevalent in the future as environmental stressors intensify. In addition, keystone protist functional groups, such as consumers and phototrophs (\u003cem\u003eRhizaria, Archaeplastida, and Stramenopiles\u003c/em\u003e), were influenced by bacteria and fungi, with protist consumers dominating in bacterial-dominated networks and phototrophs in fungal-dominated ones. Nematodes were less prominent in multitrophic networks, highlighting that smaller organisms drive processes while larger organisms regulate functional processes (Delgado-Baquerizo et al., 2017; Jiao et al., 2022). Overall, our findings emphasize the importance of dominant bacterial and fungal groups, particularly oligotrophs and pathotrophs, which play crucial roles in shaping soil food web complexity and responses to climate change.\u003c/p\u003e \u003cp\u003eWhile this study offers insights into soil food web complexity and trophic modes, it is important to acknowledge its potential limitations. Correlation-based co-occurrence networks provide a simplified view and may not fully represent real-world soil food webs (Goberna and Verd\u0026uacute; 2022b). These networks can produce spurious results and may not capture the complete architecture and connectedness of soil ecosystems. However, they remain useful for estimating species relationships and understanding the impact of network complexity on biodiversity and ecosystem functioning (Deng et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Banerjee et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Delgado-Baquerizo et al., 2020; Felipe-Lucia et al., 2020). Additionally, sequencing methods may underrepresent larger soil invertebrates such as nematodes based on this approach. Nonetheless, a few studies have successfully used this approach to estimate soil invertebrate biodiversity, and it remains a useful tool for investigating the diversity of smaller soil invertebrates (Schenk et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xiong et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides critical insights into the co-occurrence patterns of bacterial, fungal, protist, and nematode communities in semi-arid and arid ecosystems. We demonstrate that increased aridity enhances positive associations among oligotrophic bacteria, resulting in networks dominated by drought-adapted taxa. Fungal pathotrophs and bacterial oligotrophs emerged as central drivers in shaping the belowground food web, highlighting their pivotal ecological roles under future climate scenarios. Of particular concern is the rising dominance of fungal pathogens, which poses a significant threat to ecosystem health and stability in the face of climate change. These findings underscore the importance of investigating multitrophic interactions to better predict and mitigate the broader impacts of climate change on semi-arid and arid ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data generated in this study are available in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA1241245 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1241245). Associated metadata, including environmental variables, soil physicochemical properties, and plant richness and standing biomass, and ASV tables used in network construction, along with network properties and cytoscape file generated using both RMT and SparCC methods, are publicly accessible via Figshare at https://doi.org/10.6084/m9.figshare.28672310.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Chelsea Maier, Kamrul Hassan, Giles Ross, Samantha Travers, Arjunan Krishnananthaselvan, Casper Quist and Jara Dom\u0026iacute;nguez‐Begines for their involvement in field site establishment, sample collection and processing. We thank the Next generation sequencing facility at WSU for processing our DNA samples. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Australian Research Council (DP150104199; DP190101968) and Hawkesbury Institute of Environment, Western Sydney University (WSU).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM conceived\u003cstrong\u003e\u0026nbsp;t\u003c/strong\u003ehe project and design, led the data collection and analysis, and wrote the manuscript with guidance by TJ and UN. JS and DB helped during soil sampling and processing. UN secured funding for the project and established the experimental framework. All authors contributed to the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Premchand Maisnam. Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAustin, Amy T., Laura Yahdjian, John M. Stark, Jayne Belnap, Amilcare Porporato, Urszula Norton, Dami\u0026aacute;n A. Ravetta, and Sean M. Schaeffer. 2004. \u0026lsquo;Water Pulses and Biogeochemical Cycles in Arid and Semiarid Ecosystems\u0026rsquo;. \u003cem\u003eOecologia\u003c/em\u003e. doi: 10.1007/s00442-004-1519-1.\u003c/li\u003e\n \u003cli\u003eBanerjee, Samiran, Klaus Schlaeppi, and Marcel G. A. van der Heijden. 2018. \u0026lsquo;Keystone Taxa as Drivers of Microbiome Structure and Functioning\u0026rsquo;. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e 16(9):567\u0026ndash;76.\u003c/li\u003e\n \u003cli\u003eBarber\u0026aacute;n, Albert, Scott T. Bates, Emilio O. Casamayor, and Noah Fierer. 2012. \u0026lsquo;Using Network Analysis to Explore Co-Occurrence Patterns in Soil Microbial Communities\u0026rsquo;. \u003cem\u003eISME Journal\u003c/em\u003e. doi: 10.1038/ismej.2011.119.\u003c/li\u003e\n \u003cli\u003eBardgett, Richard D., and Wim H. Van Der Putten. 2014. \u0026lsquo;Belowground Biodiversity and Ecosystem Functioning\u0026rsquo;. \u003cem\u003eNature\u003c/em\u003e 515. doi: 10.1038/nature13855.\u003c/li\u003e\n \u003cli\u003eBolyen, Evan, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, Eric J. Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E. Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J. Brislawn, C. Titus Brown, Benjamin J. Callahan, Andr\u0026eacute;s Mauricio Caraballo-Rodr\u0026iacute;guez, John Chase, Emily K. Cope, Ricardo Da Silva, Christian Diener, Pieter C. Dorrestein, Gavin M. Douglas, Daniel M. Durall, Claire Duvallet, Christian F. Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M. Gauglitz, Sean M. Gibbons, Deanna L. Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin A. Huttley, Stefan Janssen, Alan K. Jarmusch, Lingjing Jiang, Benjamin D. Kaehler, Kyo Bin Kang, Christopher R. Keefe, Paul Keim, Scott T. Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan G. I. Langille, Joslynn Lee, Ruth Ley, Yong Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D. Martin, Daniel McDonald, Lauren J. McIver, Alexey V. Melnik, Jessica L. Metcalf, Sydney C. Morgan, Jamie T. Morton, Ahmad Turan Naimey, Jose A. Navas-Molina, Louis Felix Nothias, Stephanie B. Orchanian, Talima Pearson, Samuel L. Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S. Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R. Spear, Austin D. Swafford, Luke R. Thompson, Pedro J. Torres, Pauline Trinh, Anupriya Tripathi, Peter J. Turnbaugh, Sabah Ul-Hasan, Justin J. J. van der Hooft, Fernando Vargas, Yoshiki V\u0026aacute;zquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C. Weber, Charles H. D. Williamson, Amy D. Willis, Zhenjiang Zech Xu, Jesse R. Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight, and J. Gregory Caporaso. 2019. \u0026lsquo;Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2\u0026rsquo;. \u003cem\u003eNature Biotechnology\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eCallahan, Benjamin J., Paul J. McMurdie, Michael J. Rosen, Andrew W. Han, Amy Jo A. Johnson, and Susan P. Holmes. 2016. \u0026lsquo;DADA2: High-Resolution Sample Inference from Illumina Amplicon Data\u0026rsquo;. \u003cem\u003eNature Methods\u003c/em\u003e. doi: 10.1038/nmeth.3869.\u003c/li\u003e\n \u003cli\u003eCao, Tingting, You Fang, Yunru Chen, Xiangshi Kong, Junbo Yang, Hattan Alharbi, Yakov Kuzyakov, and Xingjun Tian. 2022. \u0026lsquo;Synergy of Saprotrophs with Mycorrhiza for Litter Decomposition and Hotspot Formation Depends on Nutrient Availability in the Rhizosphere\u0026rsquo;. \u003cem\u003eGeoderma\u003c/em\u003e 410:115662. doi: 10.1016/J.GEODERMA.2021.115662.\u003c/li\u003e\n \u003cli\u003eChen, Junda, Yuan Yao, Gui Wang, Xiaoyue Zhong, Tianxue Yang, and Wei Sun. 2021. \u0026lsquo;Decreased Precipitation Frequency Altered Abundance, but Not Community Structure, of Soil Nematodes in Degraded Grasslands\u0026rsquo;. \u003cem\u003eEcological Indicators\u003c/em\u003e 131:108184. doi: 10.1016/J.ECOLIND.2021.108184.\u003c/li\u003e\n \u003cli\u003eChieppa, Jeff, Sally A. Power, David T. Tissue, and Uffe N. Nielsen. 2020. \u0026lsquo;Allometric Estimates of Aboveground Biomass Using Cover and Height Are Improved by Increasing Specificity of Plant Functional Groups in Eastern Australian Rangelands\u0026rsquo;. \u003cem\u003eRangeland Ecology \u0026amp; Management\u003c/em\u003e 73(3):375\u0026ndash;83. doi: 10.1016/J.RAMA.2020.01.009.\u003c/li\u003e\n \u003cli\u003eCoyte, Katharine Z., Jonas Schluter, and Kevin R. Foster. 2015. \u0026lsquo;The Ecology of the Microbiome: Networks, Competition, and Stability\u0026rsquo;. \u003cem\u003eScience\u003c/em\u003e 350(6261):663\u0026ndash;66. doi: 10.1126/SCIENCE.AAD2602/SUPPL_FILE/COYTE.SM.PDF.\u003c/li\u003e\n \u003cli\u003eDelgado-Baquerizo, Manuel, Guilhem Doulcier, David J. Eldridge, Daniel B. Stouffer, Fernando T. Maestre, Juntao Wang, Jeff R. Powell, Thomas C. Jeffries, and Brajesh K. Singh. 2020. \u0026lsquo;Increases in Aridity Lead to Drastic Shifts in the Assembly of Dryland Complex Microbial Networks\u0026rsquo;. \u003cem\u003eLand Degradation and Development\u003c/em\u003e 31(3):346\u0026ndash;55. doi: 10.1002/ldr.3453.\u003c/li\u003e\n \u003cli\u003eDelgado-Baquerizo, Manuel, David J. Eldridge, Victoria Ochoa, Beatriz Gozalo, Brajesh K. Singh, and Fernando T. Maestre. 2017. \u0026lsquo;Soil Microbial Communities Drive the Resistance of Ecosystem Multifunctionality to Global Change in Drylands across the Globe\u0026rsquo;. \u003cem\u003eEcology Letters\u003c/em\u003e 20(10):1295\u0026ndash;1305. doi: 10.1111/ELE.12826.\u003c/li\u003e\n \u003cli\u003eDelgado-Baquerizo, Manuel, Carlos A. Guerra, Concha Cano-D\u0026iacute;az, Eleonora Egidi, Jun Tao Wang, Nico Eisenhauer, Brajesh K. Singh, and Fernando T. Maestre. 2020. \u0026lsquo;The Proportion of Soil-Borne Pathogens Increases with Warming at the Global Scale\u0026rsquo;. \u003cem\u003eNature Climate Change 2020 10:6\u003c/em\u003e 10(6):550\u0026ndash;54. doi: 10.1038/s41558-020-0759-3.\u003c/li\u003e\n \u003cli\u003eDelgado-Baquerizo, Manuel, Fernando T. Maestre, Peter B. Reich, Thomas C. Jeffries, Juan J. Gaitan, Daniel Encinar, Miguel Berdugo, Colin D. Campbell, and Brajesh K. Singh. 2016. \u0026lsquo;Microbial Diversity Drives Multifunctionality in Terrestrial Ecosystems\u0026rsquo;. \u003cem\u003eNature Communications 2015 7:1\u003c/em\u003e 7(1):1\u0026ndash;8. doi: 10.1038/ncomms10541.\u003c/li\u003e\n \u003cli\u003eDelgado-Baquerizo, Manuel, Peter B. Reich, Chanda Trivedi, David J. Eldridge, Sebasti\u0026aacute;n Abades, Fernando D. Alfaro, Felipe Bastida, Asmeret A. Berhe, Nick A. Cutler, Antonio Gallardo, Laura Garc\u0026iacute;a-Vel\u0026aacute;zquez, Stephen C. Hart, Patrick E. Hayes, Ji Zheng He, Zeng Yei Hseu, Hang Wei Hu, Martin Kirchmair, Sigrid Neuhauser, Cecilia A. P\u0026eacute;rez, Sasha C. Reed, Fernanda Santos, Benjamin W. Sullivan, Pankaj Trivedi, Jun Tao Wang, Luis Weber-Grullon, Mark A. Williams, and Brajesh K. Singh. 2020. \u0026lsquo;Multiple Elements of Soil Biodiversity Drive Ecosystem Functions across Biomes\u0026rsquo;. \u003cem\u003eNature Ecology \u0026amp; Evolution 2020 4:2\u003c/em\u003e 4(2):210\u0026ndash;20. doi: 10.1038/s41559-019-1084-y.\u003c/li\u003e\n \u003cli\u003eDeng, Ye, Yi Huei Jiang, Yunfeng Yang, Zhili He, Feng Luo, and Jizhong Zhou. 2012. \u0026lsquo;Molecular Ecological Network Analyses\u0026rsquo;. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e 13(1):1\u0026ndash;20. doi: 10.1186/1471-2105-13-113/FIGURES/6.\u003c/li\u003e\n \u003cli\u003eDevendra, C. n.d. \u0026lsquo;ASM Series on Climate Change Climate Change Threats and Effects: Challenges for Agriculture and Food Security\u0026rsquo;.\u003c/li\u003e\n \u003cli\u003eFelipe-Lucia, Mar\u0026iacute;a R., Santiago Soliveres, Caterina Penone, Markus Fischer, Christian Ammer, Steffen Boch, Runa S. Boeddinghaus, Michael Bonkowski, Fran\u0026ccedil;ois Buscot, Anna Maria Fiore-Donno, Kevin Frank, Kezia Goldmann, Martin M. Gossner, Norbert H\u0026ouml;lzel, Malte Jochum, Ellen Kandeler, Valentin H. Klaus, Till Kleinebecker, Sophia Leimer, Peter Manning, Yvonne Oelmann, Hugo Saiz, Peter Schall, Michael Schloter, Ingo Sch\u0026ouml;ning, Marion Schrumpf, Emily F. Solly, Barbara Stempfhuber, Wolfgang W. Weisser, Wolfgang Wilcke, Tesfaye Wubet, and Eric Allan. 2020. \u0026lsquo;Land-Use Intensity Alters Networks between Biodiversity, Ecosystem Functions, and Services\u0026rsquo;. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 117(45). doi: 10.1073/pnas.2016210117.\u003c/li\u003e\n \u003cli\u003eFeng, Kai, Xi Peng, Zheng Zhang, Songsong Gu, Qing He, Wenli Shen, Zhujun Wang, Danrui Wang, Qiulong Hu, Yan Li, Shang Wang, and Ye Deng. 2022. \u0026lsquo;INAP: An Integrated Network Analysis Pipeline for Microbiome Studies\u0026rsquo;. \u003cem\u003eIMeta\u003c/em\u003e 1(2). doi: 10.1002/IMT2.13.\u003c/li\u003e\n \u003cli\u003eGao, Cheng, Ling Xu, Liliam Montoya, Mary Madera, Joy Hollingsworth, Liang Chen, Elizabeth Purdom, Vasanth Singan, John Vogel, Robert B. Hutmacher, Jeffery A. Dahlberg, Devin Coleman-Derr, Peggy G. Lemaux, and John W. Taylor. 2022. \u0026lsquo;Co-Occurrence Networks Reveal More Complexity than Community Composition in Resistance and Resilience of Microbial Communities\u0026rsquo;. \u003cem\u003eNature Communications 2022 13:1\u003c/em\u003e 13(1):1\u0026ndash;12. doi: 10.1038/s41467-022-31343-y.\u003c/li\u003e\n \u003cli\u003eGeisen, Stefan, Shunran Hu, \u0026bull; Thomas, Edison E. Dela Cruz, \u0026bull; G F ( Ciska, and ) Veen. 2021. \u0026lsquo;Protists as Catalyzers of Microbial Litter Breakdown and Carbon Cycling at Different Temperature Regimes\u0026rsquo;. \u003cem\u003eThe ISME Journal\u003c/em\u003e 15:618\u0026ndash;21. doi: 10.1038/s41396-020-00792-y.\u003c/li\u003e\n \u003cli\u003eGeisen, Stefan, Edward A. D. Mitchell, Sina Adl, Michael Bonkowski, Micah Dunthorn, Flemming Ekelund, Leonardo D. Fern\u0026aacute;ndez, Alexandre Jousset, Valentyna Krashevska, David Singer, Frederick W. Spiegel, Julia Walochnik, and Enrique Lara. 2018. \u0026lsquo;Soil Protists: A Fertile Frontier in Soil Biology Research\u0026rsquo;. \u003cem\u003eFEMS Microbiology Reviews\u003c/em\u003e 42(3):293\u0026ndash;323. doi: 10.1093/FEMSRE/FUY006.\u003c/li\u003e\n \u003cli\u003eGlassman, Sydney I., Claudia Weihe, Junhui Li, Michaeline B. N. Albright, Caitlin I. Looby, Adam C. Martiny, Kathleen K. Treseder, Steven D. Allison, and Jennifer B. H. Martiny. 2018. \u0026lsquo;Decomposition Responses to Climate Depend on Microbial Community Composition\u0026rsquo;. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 115(47):11994\u0026ndash;99. doi: 10.1073/PNAS.1811269115/SUPPL_FILE/PNAS.1811269115.SAPP.PDF.\u003c/li\u003e\n \u003cli\u003eGoberna, Marta, and Miguel Verd\u0026uacute;. 2022a. \u0026lsquo;Cautionary Notes on the Use of Co-Occurrence Networks in Soil Ecology\u0026rsquo;. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 166:108534. doi: 10.1016/J.SOILBIO.2021.108534.\u003c/li\u003e\n \u003cli\u003eGoberna, Marta, and Miguel Verd\u0026uacute;. 2022b. \u0026lsquo;Cautionary Notes on the Use of Co-Occurrence Networks in Soil Ecology\u0026rsquo;. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 166.\u003c/li\u003e\n \u003cli\u003eGuimer\u0026agrave;, Roger, and Luis A. Nunes Amaral. 2005. \u0026lsquo;Functional Cartography of Complex Metabolic Networks\u0026rsquo;. \u003cem\u003eNature\u003c/em\u003e 433(7028). doi: 10.1038/nature03288.\u003c/li\u003e\n \u003cli\u003eHerlemann, Daniel P. R., Matthias Labrenz, Klaus J\u0026uuml;rgens, Stefan Bertilsson, Joanna J. Waniek, and Anders F. Andersson. 2011. \u0026lsquo;Transitions in Bacterial Communities along the 2000\u0026thinsp;Km Salinity Gradient of the Baltic Sea\u0026rsquo;. \u003cem\u003eThe ISME Journal\u003c/em\u003e 5(10):1571\u0026ndash;79. doi: 10.1038/ISMEJ.2011.41.\u003c/li\u003e\n \u003cli\u003eHerren, Cristina M., and Katherine D. McMahon. 2017. \u0026lsquo;Cohesion: A Method for Quantifying the Connectivity of Microbial Communities\u0026rsquo;. \u003cem\u003eThe ISME Journal 2017 11:11\u003c/em\u003e 11(11):2426\u0026ndash;38. doi: 10.1038/ismej.2017.91.\u003c/li\u003e\n \u003cli\u003evan den Hoogen, Johan, Stefan Geisen, Devin Routh, Howard Ferris, Walter Traunspurger, David A. Wardle, Ron G. M. de Goede, Byron J. Adams, Wasim Ahmad, Walter S. Andriuzzi, Richard D. Bardgett, Michael Bonkowski, Raquel Campos-Herrera, Juvenil E. Cares, Tancredi Caruso, Larissa de Brito Caixeta, Xiaoyun Chen, Sofia R. Costa, Rachel Creamer, Jos\u0026eacute; Mauro da Cunha Castro, Marie Dam, Djibril Djigal, Miguel Escuer, Bryan S. Griffiths, Carmen Guti\u0026eacute;rrez, Karin Hohberg, Daria Kalinkina, Paul Kardol, Alan Kergunteuil, Gerard Korthals, Valentyna Krashevska, Alexey A. Kudrin, Qi Li, Wenju Liang, Matthew Magilton, Mariette Marais, Jos\u0026eacute; Antonio Rodr\u0026iacute;guez Mart\u0026iacute;n, Elizaveta Matveeva, El Hassan Mayad, Christian Mulder, Peter Mullin, Roy Neilson, T. A. Duong Nguyen, Uffe N. Nielsen, Hiroaki Okada, Juan Emilio Palomares Rius, Kaiwen Pan, Vlada Peneva, Lo\u0026iuml;c Pellissier, Julio Carlos Pereira da Silva, Camille Pitteloud, Thomas O. Powers, Kirsten Powers, Casper W. Quist, Sergio Rasmann, Sara S\u0026aacute;nchez Moreno, Stefan Scheu, Heikki Set\u0026auml;l\u0026auml;, Anna Sushchuk, Alexei V. Tiunov, Jean Trap, Wim van der Putten, Mette Vesterg\u0026aring;rd, Cecile Villenave, Lieven Waeyenberge, Diana H. Wall, Rutger Wilschut, Daniel G. Wright, Jiue in Yang, and Thomas Ward Crowther. 2019. \u0026lsquo;Soil Nematode Abundance and Functional Group Composition at a Global Scale\u0026rsquo;. \u003cem\u003eNature\u003c/em\u003e 572(7768). doi: 10.1038/s41586-019-1418-6.\u003c/li\u003e\n \u003cli\u003eHuang, J., Y. Li, C. Fu, F. Chen, Q. Fu, A. Dai, M. Shinoda, Z. Ma, W. Guo, Z. Li, L. Zhang, Y. Liu, H. Yu, Y. He, Y. Xie, X. Guan, M. Ji, L. Lin, S. Wang, H. Yan, and G. Wang. 2017. \u0026lsquo;Dryland Climate Change: Recent Progress and Challenges\u0026rsquo;. \u003cem\u003eReviews of Geophysics\u003c/em\u003e 55(3):719\u0026ndash;78. doi: 10.1002/2016RG000550.\u003c/li\u003e\n \u003cli\u003eIhrmark, Katarina, Inga T. M. B\u0026ouml;deker, Karelyn Cruz-Martinez, Hanna Friberg, Ariana Kubartova, Jessica Schenck, Ylva Strid, Jan Stenlid, Mikael Brandstr\u0026ouml;m-Durling, Karina E. Clemmensen, and Bj\u0026ouml;rn D. Lindahl. 2012. \u0026lsquo;New Primers to Amplify the Fungal ITS2 Region \u0026ndash; Evaluation by 454-Sequencing of Artificial and Natural Communities\u0026rsquo;. \u003cem\u003eFEMS Microbiology Ecology\u003c/em\u003e 82(3):666\u0026ndash;77. doi: 10.1111/J.1574-6941.2012.01437.X.\u003c/li\u003e\n \u003cli\u003eJiang, Ying, Zhonghua Wang, Ye Liu, Yanlai Han, Yi Wang, Qiang Wang, and Ting Liu. 2023. \u0026lsquo;Nematodes and Their Bacterial Prey Improve Phosphorus Acquisition by Wheat\u0026rsquo;. \u003cem\u003eNew Phytologist\u003c/em\u003e 237(3):974\u0026ndash;86. doi: 10.1111/NPH.18569.\u003c/li\u003e\n \u003cli\u003eLahlali, Rachid, Taoussi Mohammed, Salah-Eddine Laasli, Grace Gachara, Rachid Ezzouggari, Zine Belabess, Kamal Aberkani, Amine Assougeum, Abdelilah Meddich, Moussa El Jarroudi, and Essaid Ait Barka. 2024. \u0026lsquo;Effects of Climate Change on Plant Pathogens and Host-Pathogen Interactions\u0026rsquo;. \u003cem\u003eCrop and Environment\u003c/em\u003e 3(3):159\u0026ndash;70. doi: 10.1016/J.CROPE.2024.05.003.\u003c/li\u003e\n \u003cli\u003eLiu, Nana, Huifeng Hu, Wenhong Ma, Ye Deng, Dimitar Dimitrov, Qinggang Wang, Nawal Shrestha, Xiangyan Su, Kai Feng, Yuqing Liu, Baihui Hao, Xinying Zhang, Xiaojuan Feng, and Zhiheng Wang. 2022. \u0026lsquo;Relationships Between Soil Microbial Diversities Across an Aridity Gradient in Temperate Grasslands: Soil Microbial Diversity Relationships\u0026rsquo;. \u003cem\u003eMicrobial Ecology\u003c/em\u003e 85(3):1013\u0026ndash;27. doi: 10.1007/S00248-022-01997-8/TABLES/2.\u003c/li\u003e\n \u003cli\u003eLu, Lihua, Shixue Yin, Xing Liu, Wenming Zhang, Tianyu Gu, Qirong Shen, and Huizhen Qiu. 2013. \u0026lsquo;Fungal Networks in Yield-Invigorating and -Debilitating Soils Induced by Prolonged Potato Monoculture\u0026rsquo;. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 65:186\u0026ndash;94. doi: 10.1016/J.SOILBIO.2013.05.025.\u003c/li\u003e\n \u003cli\u003eLucena-Aguilar, Gema, Ana Mar\u0026iacute;a S\u0026aacute;nchez-L\u0026oacute;pez, Cristina Barber\u0026aacute;n-Aceituno, Jos\u0026eacute; Antonio Carrillo-\u0026Aacute;vila, Jos\u0026eacute; Antonio L\u0026oacute;pez-Guerrero, and Roc\u0026iacute;o Aguilar-Quesada. 2016. \u0026lsquo;DNA Source Selection for Downstream Applications Based on DNA Quality Indicators Analysis\u0026rsquo;. in \u003cem\u003eBiopreservation and Biobanking\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eLuo, Jipeng, Xinyu Guo, Qi Tao, Jinxing Li, Yuankun Liu, Yilin Du, Yuying Liu, Yongchao Liang, and Tingqiang Li. 2021. \u0026lsquo;Succession of the Composition and Co-Occurrence Networks of Rhizosphere Microbiota Is Linked to Cd/Zn Hyperaccumulation\u0026rsquo;. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 153:108120. doi: 10.1016/J.SOILBIO.2020.108120.\u003c/li\u003e\n \u003cli\u003eMaestre, Fernando T., Manuel Delgado-Baquerizo, Thomas C. Jeffries, David J. Eldridge, Victoria Ochoa, Beatriz Gozalo, Jos\u0026eacute; Luis Quero, Miguel Garc\u0026iacute;a-G\u0026oacute;mez, Antonio Gallardo, Werner Ulrich, Matthew A. Bowker, Tulio Arredondo, Claudia Barraza-Zepeda, Donaldo Bran, Adriana Florentino, Juan Gait\u0026aacute;n, Julio R. Guti\u0026eacute;rrez, Elisabeth Huber-Sannwald, Mohammad Jankju, Rebecca L. Mau, Maria Miriti, Kamal Naseri, Abelardo Ospina, Ilan Stavi, Deli Wang, Natasha N. Woods, Xia Yuan, Eli Zaady, and Brajesh K. Singh. 2015. \u0026lsquo;Increasing Aridity Reduces Soil Microbial Diversity and Abundance in Global Drylands\u0026rsquo;. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 112(51):15684\u0026ndash;89. doi: 10.1073/PNAS.1516684112/SUPPL_FILE/PNAS.201516684SI.PDF.\u003c/li\u003e\n \u003cli\u003eMaisnam, P., T. C. Jeffries, J. Szejgis, D. Bristol, B. K. Singh, D. J. Eldridge, S. Horn, J. Chieppa, and U. N. Nielsen. 2023. \u0026lsquo;Severe Prolonged Drought Favours Stress-Tolerant Microbes in Australian Drylands\u0026rsquo;. \u003cem\u003eMicrobial Ecology\u003c/em\u003e 86(4). doi: 10.1007/s00248-023-02303-w.\u003c/li\u003e\n \u003cli\u003eMcMurdie, Paul J., and Susan Holmes. 2013. \u0026lsquo;Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data\u0026rsquo;. \u003cem\u003ePLOS ONE\u003c/em\u003e 8(4):e61217. doi: 10.1371/JOURNAL.PONE.0061217.\u003c/li\u003e\n \u003cli\u003eNeilson, Julia W., Katy Califf, Cesar Cardona, Audrey Copeland, Will van Treuren, Karen L. Josephson, Rob Knight, Jack A. Gilbert, Jay Quade, J. Gregory Caporaso, and Raina M. Maier. 2017. \u0026lsquo;Significant Impacts of Increasing Aridity on the Arid Soil Microbiome\u0026rsquo;. \u003cem\u003eMSystems\u003c/em\u003e 2(3). doi: 10.1128/msystems.00195-16.\u003c/li\u003e\n \u003cli\u003eNguyen, Nhu H., Zewei Song, Scott T. Bates, Sara Branco, Leho Tedersoo, Jon Menke, Jonathan S. Schilling, and Peter G. Kennedy. 2016. \u0026lsquo;FUNGuild: An Open Annotation Tool for Parsing Fungal Community Datasets by Ecological Guild\u0026rsquo;. \u003cem\u003eFungal Ecology\u003c/em\u003e 20:241\u0026ndash;48. doi: 10.1016/J.FUNECO.2015.06.006.\u003c/li\u003e\n \u003cli\u003eNguyen, Thi Bao Anh, Qing Lin Chen, Zhen Zhen Yan, Chaoyu Li, Ji Zheng He, and Hang Wei Hu. 2023. \u0026lsquo;Trophic Interrelationships of Bacteria Are Important for Shaping Soil Protist Communities\u0026rsquo;. \u003cem\u003eEnvironmental Microbiology Reports\u003c/em\u003e. doi: 10.1111/1758-2229.13143.\u003c/li\u003e\n \u003cli\u003eNielsen, Uffe N., and Becky A. Ball. 2015. \u0026lsquo;Impacts of Altered Precipitation Regimes on Soil Communities and Biogeochemistry in Arid and Semi-Arid Ecosystems\u0026rsquo;. \u003cem\u003eGlobal Change Biology\u003c/em\u003e 21(4):1407\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eNilsson, Rolf Henrik, Karl Henrik Larsson, Andy F. S. Taylor, Johan Bengtsson-Palme, Thomas S. Jeppesen, Dmitry Schigel, Peter Kennedy, Kathryn Picard, Frank Oliver Gl\u0026ouml;ckner, Leho Tedersoo, Irja Saar, Urmas K\u0026otilde;ljalg, and Kessy Abarenkov. 2019. \u0026lsquo;The UNITE Database for Molecular Identification of Fungi: Handling Dark Taxa and Parallel Taxonomic Classifications\u0026rsquo;. \u003cem\u003eNucleic Acids Research\u003c/em\u003e. doi: 10.1093/nar/gky1022.\u003c/li\u003e\n \u003cli\u003eOchoa-Hueso, Ra\u0026uacute;l, Valentina Arca, Manuel Delgado-Baquerizo, Kelly Hamonts, Juan Pi\u0026ntilde;eiro, Lilia Serrano-Grijalva, Julien Shawyer, and Sally A. Power. 2020. \u0026lsquo;Links between Soil Microbial Communities, Functioning, and Plant Nutrition under Altered Rainfall in Australian Grassland\u0026rsquo;. \u003cem\u003eEcological Monographs\u003c/em\u003e 90(4):e01424. doi: 10.1002/ECM.1424.\u003c/li\u003e\n \u003cli\u003eOksanen, Author Jari, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, and R. O\u0026rsquo;Hara. 2016. \u0026lsquo;Vegan: Community Ecology Package. R Package 2.3-3.\u0026rsquo; \u003cem\u003eJournal of Vegetation Science\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eOliverio, Angela M., Stefan Geisen, Manuel Delgado-Baquerizo, Fernando T. Maestre, Benjamin L. Turner, and Noah Fierer. 2020. \u003cem\u003eThe Global-Scale Distributions of Soil Protists and Their Contributions to Belowground Systems\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003ePeng, Xi, Shang Wang, Miaoxiao Wang, Kai Feng, Qing He, Xingsheng Yang, Weiguo Hou, Fangru Li, Yuxiang Zhao, Baolan Hu, Xiao Zou, and Ye Deng. 2024. \u0026lsquo;Metabolic Interdependencies in Thermophilic Communities Are Revealed Using Co-Occurrence and Complementarity Networks\u0026rsquo;. \u003cem\u003eNature Communications 2024 15:1\u003c/em\u003e 15(1):1\u0026ndash;14. doi: 10.1038/s41467-024-52532-x.\u003c/li\u003e\n \u003cli\u003eQuast, Christian, Elmar Pruesse, Pelin Yilmaz, Jan Gerken, Timmy Schweer, Pablo Yarza, J\u0026ouml;rg Peplies, and Frank Oliver Gl\u0026ouml;ckner. 2013. \u0026lsquo;The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools\u0026rsquo;. \u003cem\u003eNucleic Acids Research\u003c/em\u003e. doi: 10.1093/nar/gks1219.\u003c/li\u003e\n \u003cli\u003eRillig, Matthias C., Anika Lehmann, Carlos A. Aguilar-Trigueros, Janis Antonovics, Tancredi Caruso, Stefan Hempel, Johannes Lehmann, Kriszta Valyi, Erik Verbruggen, Stavros D. Veresoglou, and Jeff R. Powell. 2016. \u0026lsquo;Soil Microbes and Community Coalescence\u0026rsquo;. \u003cem\u003ePedobiologia\u003c/em\u003e. doi: 10.1016/j.pedobi.2016.01.001.\u003c/li\u003e\n \u003cli\u003eRosen, Gail L., Erin R. Reichenberger, and Aaron M. Rosenfeld. 2011. \u0026lsquo;NBC: The Na\u0026iuml;ve Bayes Classification Tool Webserver for Taxonomic Classification of Metagenomic Reads\u0026rsquo;. \u003cem\u003eBioinformatics\u003c/em\u003e 27(1):127\u0026ndash;29. doi: 10.1093/BIOINFORMATICS/BTQ619.\u003c/li\u003e\n \u003cli\u003eSchenk, Janina, Stefan Geisen, Nils Kleinboelting, and Walter Traunspurger. 2019. \u0026lsquo;Metabarcoding Data Allow for Reliable Biomass Estimates in the Most Abundant Animals on Earth\u0026rsquo;. \u003cem\u003eMetabarcoding and Metagenomics\u003c/em\u003e 3:e46704. doi: 10.3897/MBMG.3.46704.\u003c/li\u003e\n \u003cli\u003eSchenk, Janina, Sebastian H\u0026ouml;ss, Marvin Brinke, Nils Kleinb\u0026ouml;lting, Henrike Br\u0026uuml;chner-H\u0026uuml;ttemann, and Walter Traunspurger. 2020. \u0026lsquo;Nematodes as Bioindicators of Polluted Sediments Using Metabarcoding and Microscopic Taxonomy\u0026rsquo;. \u003cem\u003eEnvironment International\u003c/em\u003e 143. doi: 10.1016/J.ENVINT.2020.105922.\u003c/li\u003e\n \u003cli\u003eSchimel, David S. 2010. \u0026lsquo;Drylands in the Earth System\u0026rsquo;. \u003cem\u003eScience\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eShokralla, Shadi, Jennifer L. Spall, Joel F. Gibson, and Mehrdad Hajibabaei. 2012. \u0026lsquo;Next-Generation Sequencing Technologies for Environmental DNA Research\u0026rsquo;. \u003cem\u003eMolecular Ecology\u003c/em\u003e 21(8):1794\u0026ndash;1805. doi: 10.1111/J.1365-294X.2012.05538.X;REQUESTEDJOURNAL:JOURNAL:1365294X;CSUBTYPE:STRING:SPECIAL;PAGE:STRING:ARTICLE/CHAPTER.\u003c/li\u003e\n \u003cli\u003eSimbolo, Michele, Marisa Gottardi, Vincenzo Corbo, Matteo Fassan, Andrea Mafficini, Giorgio Malpeli, Rita T. Lawlor, and Aldo Scarpa. 2013. \u0026lsquo;DNA Qualification Workflow for Next Generation Sequencing of Histopathological Samples\u0026rsquo;. \u003cem\u003ePLoS ONE\u003c/em\u003e. doi: 10.1371/journal.pone.0062692.\u003c/li\u003e\n \u003cli\u003eStouffer, Daniel B., and Jordi Bascompte. 2011. \u0026lsquo;Compartmentalization Increases Food-Web Persistence\u0026rsquo;. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 108(9):3648\u0026ndash;52. doi: 10.1073/PNAS.1014353108/-/DCSUPPLEMENTAL.\u003c/li\u003e\n \u003cli\u003ede Vries, Franciska T., and Tancredi Caruso. 2016. \u0026lsquo;Eating from the Same Plate? Revisiting the Role of Labile Carbon Inputs in the Soil Food Web\u0026rsquo;. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e 102:4\u0026ndash;9. doi: 10.1016/J.SOILBIO.2016.06.023.\u003c/li\u003e\n \u003cli\u003eWang, Jianqing, Mao Li, Xuhui Zhang, Xiaoyu Liu, Lianqing Li, Xiuzhen Shi, Hang wei Hu, and Genxing Pan. 2019. \u0026lsquo;Changes in Soil Nematode Abundance and Composition under Elevated [CO2] and Canopy Warming in a Rice Paddy Field\u0026rsquo;. \u003cem\u003ePlant and Soil\u003c/em\u003e 445(1\u0026ndash;2). doi: 10.1007/s11104-019-04330-4.\u003c/li\u003e\n \u003cli\u003eXiong, Dan, Cun Zheng Wei, E. R. Jasper Wubs, G. F. Veen, Wenju Liang, Xiaobo Wang, Qi Li, Wim H. Van der Putten, and Xingguo Han. 2020. \u0026lsquo;Nonlinear Responses of Soil Nematode Community Composition to Increasing Aridity\u0026rsquo;. \u003cem\u003eGlobal Ecology and Biogeography\u003c/em\u003e 29(1):117\u0026ndash;26. doi: 10.1111/GEB.13013.\u003c/li\u003e\n \u003cli\u003eZhao, Pei shan, Mi shan Guo, Guang lei Gao, Ying Zhang, Guo dong Ding, Yue Ren, and Mobeen Akhtar. 2020. \u0026lsquo;Community Structure and Functional Group of Root-Associated Fungi of Pinus Sylvestris Var. Mongolica across Stand Ages in the Mu Us Desert\u0026rsquo;. \u003cem\u003eEcology and Evolution\u003c/em\u003e 10(6):3032\u0026ndash;42. doi: 10.1002/ECE3.6119.\u003c/li\u003e\n \u003cli\u003eZhou, Jizhong, Ye Deng, Feng Luo, Zhili He, Qichao Tu, and Xiaoyang Zhi. 2010. \u0026lsquo;Functional Molecular Ecological Networks\u0026rsquo;. \u003cem\u003eMBio\u003c/em\u003e 1(4):169\u0026ndash;79. doi: 10.1128/MBIO.00169-10/SUPPL_FILE/MBIO00169-10-ST01.DOC.\u003c/li\u003e\n \u003cli\u003eZhu, Hongfei, Bailian Li, Ning Ding, Zheng Hua, Xiaoxu Jiang, Hongfei Zhu, Bailian Li, Ning Ding, Zheng Hua, and Xiaoxu Jiang. 2021. \u0026lsquo;A Case Study on Microbial Diversity Impacts of a Wastewater Treatment Plant to the Receiving River\u0026rsquo;. \u003cem\u003eJournal of Geoscience and Environment Protection\u003c/em\u003e 9(4):206\u0026ndash;20. doi: 10.4236/GEP.2021.94013.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6575220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6575220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFuture climate projections indicate shifts in intra-annual precipitation patterns, with intensified rainfall events and prolonged dry periods. These changes may alter soil biotic communities and their interactions within food webs, particularly in semi-arid and arid ecosystems. However, the extent to which varying rainfall regimes and semi-arid and arid conditions influence multitrophic associations remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe leveraged a four-year rainfall manipulation experiment across six dryland sites in eastern Australia, representing arid and semi-arid ecosystems with varying, high and low levels of rainfall variability (CV) making three different climatic conditions. Rainfall treatments simulated increased (+\u0026thinsp;65%) and reduced (-65%) precipitation relative to ambient conditions. We studied multitrophic co-occurrence network among bacteria, fungi, protists, and nematodes, representing key components of the soil food web, and assess their associated changes to varying rainfall and climatic conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eClimatic differences between arid and semi-arid ecosystems were the primary drivers of soil biotic community composition, whereas rainfall treatments had minimal influence. Multitrophic co-occurrence networks varied significantly across climatic conditions, with increasing aridity promoting more positive associations among bacterial nodes. Bacteria, fungi, and their interactions were central to the belowground multitrophic network structure. In particular, stress-tolerant oligotrophic bacteria and pathotrophic fungi played key roles, with mean annual precipitation (MAP) identified as a critical determinant.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings suggest that aridity-driven shifts in biotic interactions may restructure belowground food webs in dryland ecosystems. The increasing dominance of oligotrophic bacteria and fungal pathotrophs under arid conditions highlights potential consequences for soil functioning and plant-soil interactions in response to changing precipitation regimes.\u003c/p\u003e","manuscriptTitle":"Oligotrophic bacteria and pathotrophic fungi moderate multitrophic interactions in semi-arid and arid environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 10:11:47","doi":"10.21203/rs.3.rs-6575220/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-08T13:34:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T23:27:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-21T21:31:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177145721101118341565496285522583091555","date":"2025-06-02T08:25:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196285309267642457323200065162553333024","date":"2025-06-02T07:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-30T12:24:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-21T15:45:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-07T03:57:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Microbiome","date":"2025-05-02T03:53:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7574d02-e7b5-4fe4-9b6c-dc0a83bb48cc","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:01:11+00:00","versionOfRecord":{"articleIdentity":"rs-6575220","link":"https://doi.org/10.1186/s40793-025-00788-1","journal":{"identity":"environmental-microbiome","isVorOnly":false,"title":"Environmental Microbiome"},"publishedOn":"2025-11-19 15:57:04","publishedOnDateReadable":"November 19th, 2025"},"versionCreatedAt":"2025-06-03 10:11:47","video":"","vorDoi":"10.1186/s40793-025-00788-1","vorDoiUrl":"https://doi.org/10.1186/s40793-025-00788-1","workflowStages":[]},"version":"v1","identity":"rs-6575220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6575220","identity":"rs-6575220","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}