Deciphering Global Patterns of Marine Microbial Community Assembly and Network Stability

Deciphering Global Patterns of Marine Microbial Community Assembly and Network Stability | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Deciphering Global Patterns of Marine Microbial Community Assembly and Network Stability View ORCID Profile Pranathi Ravikumar , View ORCID Profile Aarti Ravindran , View ORCID Profile Karthik Raman doi: https://doi.org/10.1101/2025.09.19.677047 Pranathi Ravikumar 1 Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras , Chennai, 600036, Tamil Nadu, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pranathi Ravikumar Aarti Ravindran 1 Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras , Chennai, 600036, Tamil Nadu, India 2 Centre for Integrative Biology and Systems mEdicine (IBSE), Wadhwani School of Data Science and AI, IIT Madras , Chennai, 600036, Tamil Nadu, India 3 Department of Data Science and AI, Wadhwani School of Data Science and AI, Indian Institute of Technology Madras , Chennai, 600036, Tamil Nadu, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aarti Ravindran Karthik Raman 2 Centre for Integrative Biology and Systems mEdicine (IBSE), Wadhwani School of Data Science and AI, IIT Madras , Chennai, 600036, Tamil Nadu, India 3 Department of Data Science and AI, Wadhwani School of Data Science and AI, Indian Institute of Technology Madras , Chennai, 600036, Tamil Nadu, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karthik Raman For correspondence: kraman{at}dsai.iitm.ac.in Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Microbial community assembly processes can help us understand ecology, evolution, and climatic influences on community composition while presenting opportunities for biotechnological applications and improved marine conservation. In this study, we have investigated species richness patterns, community assembly mechanisms, and interaction patterns of marine microbial communities by analysing 16S rRNA amplicon sequencing data from 4,611 samples collected from major ocean microbiome projects. Using neutral community models, the iCAMP framework, and co-occurrence network analyses, we showed that stochastic processes drive microbial community assembly across all latitude zones. Drift was the primary driver of community assembly in polar communities. The polar microbial communities exhibited the highest modularity and network robustness, but were more vulnerable to hub removal. While previous studies have attributed higher stability of the polar communities to environmental filtering, our analyses reveal that the resilience of the community is dependent on a few central taxa. By classifying the genera as generalists and specialists, we further highlight the role played by the specialist taxa in maintaining the stability of the marine microbial community, especially under the pressures of climate change and global warming. Overall, our findings offer a latitudinal perspective on ocean microbial community stability and the effect of anthropogenic disturbances on these communities. 1 Introduction Microbial community assembly processes explain the fundamental principles of ecology, evolution, and climatic variations and can be used for biotechnological, environmental applications, and management. Large-scale ocean microbiome projects, including the Tara Oceans Project [ 1 ], Malaspina Expedition [ 2 , 3 ], Global Ocean Sampling [ 4 , 5 ], and Bio-GO-SHIP [ 6 ], have revolutionised our understanding of microbial life in the oceans. These initiatives have enabled public access to microbial community data across spatial and environmental gradients, supporting global-scale analysis of the ocean microbiome. To investigate the ecological mechanisms underlying community assembly, researchers employ conceptual frameworks and statistical methods that differentiate between deterministic (niche-driven) and stochastic (neutral) processes [ 7 – 9 ].Deterministic processes such as environmental filtering and species interaction structure communities by selecting traits suited to particular environmental conditions. On the other hand, stochastic processes include ecological drift, dispersal limitation, and historical contingencies resulting in community patterns that arise from random events and colonisation. Models such as the Neutral Community Model [ 10 ] and the iCAMP (integrated Community Assembly Mechanisms Profiling) framework [ 11 ] enable quantitative estimation of the contribution of these processes in driving community assembly. While community assembly models uncover abiotic drivers of community composition, they cannot capture the interactions of taxa within the communities. Hence, co-occurrence network analysis can be a powerful tool in microbial ecology. Co-occurrence networks are constructed by identifying statistically significant correlations between taxa across samples, enabling the inference of potential ecological interactions [ 12 ]. The structure of these networks can be quantified using network properties such as modularity, degree distribution, clustering coefficient, and natural connectivity. These properties can reveal emergent properties such as stability, robustness, and keystone taxa in the communities [ 13 ]. These network topologies often explain the ecological roles played by the taxa. For example, some genera may act as generalists and have high prevalence across environments. On the other hand, they may be specialists, having niche-specific prevalence. These patterns can be used to understand how microbial ecosystems respond to perturbations such as warming, acidification, and nutrient fluctuations [ 14 , 15 ]. One of the most widely used methods to characterise marine microbial communities in such studies is 16S rRNA gene amplicon sequencing [ 16 – 18 ]. This technique targets conserved regions of the ribosomal rRNA gene to profile taxonomic composition at relatively low costs and high throughput [ 19 ]. It can be used to estimate the microbial diversity and biogeographic patterns to understand ecological processes in these communities [ 20 ]. We have utilised 16S rRNA amplicon sequencing data from ocean microbiome projects in this study. We have investigated the ecological mechanisms governing community assembly processes in the ocean microbial community using the Sloan Neutral Community Model (NCM) and the iCAMP framework. Furthermore, co-occurrence networks were constructed and have been used to uncover structural features and keystone taxa. The microbes in these networks were classified as generalists and specialists based on their prevalence to evaluate their topological roles and contributions to network robustness. Through this integrative framework, the study aims to provide a comprehensive understanding of the ecological processes structuring global marine microbial communities, focusing on how latitude-mediated environmental gradients influence the presence of taxa within these communities, and how they interact and function as a collective ecological network. 2 Methods 2.1 Data Retrieval And Preprocessing A total of 4611 16S amplicon sequencing samples were obtained from multiple ocean microbiome projects, including Tara Oceans Project [ 1 ], Malaspina Expedition [ 21 ], Global Ocean Sampling [ 4 ] and Bio-GO-SHIP [ 6 ]. Additionally, marine samples from the Australian Microbiome Project and the Earth Microbiome Project were incorporated [ 22 , 23 ].The number of samples taken from each of these projects has been mentioned in Supplementary Table 1. The sample locations have been inferred from the latitude and longitude data and incorporated into the metadata file (Supplementary File 1 and depicted in Supplementary Figure 1. The quality filtering and preprocessing of sequence data were performed using the Quantitative Insights into Microbial Ecology (QIIME2) pipeline (version 2024.5) [ 24 ]. The sequences were clustered using the DADA2 algorithm [ 25 ]. The representative sequences from each ASV were aligned to the GreenGenes2 reference database (v22.10) [ 26 ], and taxonomic classification was performed using the RDP Classifier. The unclassified ASVs have been filtered out from further downstream analysis. The parameters used in the denoising process and the primers used for training the RDP Classifier are mentioned in Supplementary File 2. The results of the denoising process are described in detail in Supplementary File 3. 2.2 Statistical Analysis The Chao1 and Shannon Diversity indices were calculated using the ‘vegan’ package v2.6.10 [ 27 ] in R Statistical Computing Environment [ 28 ]. The Kruskal-Wallis test was performed using the ‘Phyloseq’ v.1.52.0 package [ 29 ] to test the significance of the differences in Shannon Diversity metrics amongst samples. Using the ‘adonis2’ function available in the ‘vegan’ package, Permutational Multivariate Analysis of Variance (PERMANOVA) was performed to identify the significant factors contributing to the variation between samples. The Bray-Curtis dissimilarity index was used as the basis for the Principal Coordinate Analysis (PCoA), and was then used to plot the beta diversity of the samples. Beta dispersion, which shows the spread or variability in the community composition, was estimated using the ‘betadisper’ function. The Canonical Correspondence Analysis (CCA) and Variation Partition Analysis (VPA) were performed using the ‘vegan’ package to corroborate the PERMANOVA results. 2.3 Community Assembly The Neutral Community Model (NCM) was used to estimate the contribution of stochastic and deterministic processes in community assembly of marine samples [ 10 ]. The ‘MicEco’ and ‘vegan’ packages were used for the analysis at the genus level without any filtering criteria applied. Another approach used to reveal the patterns of stochasticity and determinism and their influence on microbial communities was to calculate the Levin’s Niche breadth (B) index (Bcom) [ 30 ]. This analysis was conducted using the ‘niche.width’ function in the “spaa” package available in R [ 31 ]. NST was calculated using the NST package in R (v.3.1.10) [ 32 ]. This was carried out with a cutoff of at least 50 reads in a sample and a taxa prevalence of 0.1 per cent. A phylogenetic-bin-based framework (iCAMP) was used to infer community assembly mechanisms. A cutoff of at least 50 reads and a minimum prevalence of 0.1% was used to make it computationally less intensive without losing too much information. The icamp.big, icamp.bins and icamp.boot functions in the R package iCAMP function were used [ 11 ]. 2.4 Co-occurrence Networks Construction And Analyses The microbial co-occurrence network was constructed based on the association values computed using SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference) [ 12 ]. A cutoff of at least 50 reads and a minimum of 1% prevalence was used to filter out noise and avoid spurious interactions. Network analysis was performed using the ‘igraph’ [ 33 ] and ‘NetCoMi’ package [ 34 ]. The plots generated in the study have been generated using the ‘ggplot’ package [ 35 ]. 3 Results 3.1 Multiple Factors Contribute To Variation In The Marine Bacterial Community The metadata uniformly available across all datasets included latitude and longitude coordinates, the ocean (determined from these coordinates), and the sample origin, categorised as marine, coral-associated, sponge-associated, or marine sediment. To capture the differences in the community composition, Bray-Curtis dissimilarity was calculated. PERMANOVA was performed to quantify how much these factors contribute to the variation in diversity. PERMANOVA results showed that the latitude zones explain 12.5% and the oceans (which are correlated with the latitude) explain 11.9% of the variance (Supplementary Table 2). The Bray-Curtis distance was ordinated with PCoA. The first two principal axes accounted for more than 30 per cent of the variation (Supplementary Figure 2). The bacterial communities were clustered into three groups based on the latitude zones (Polar, Temperate and Tropical, Figure 1A ). The beta dispersion of samples was evaluated to compare microbiome composition variability across different latitude zones. Samples from temperate regions exhibited the highest beta dispersion, followed by those from tropical and polar regions ( Figure 1B ). The differences in beta dispersion between latitude zones were statistically significant (Kruskal-Wallis test p-value< 0.001). Download figure Open in new tab Fig. 1: Compositional Analysis of Marine Microbial Communities. A: PCoA. B: Beta-dispersion (Kruskal– Wallis test, p-value< 0.001). C: CCA depicting the effect of different environmental factors on the Bray–Curtis distance. D: VPA. E: Shannon diversity of samples grouped by latitude zones. F: Chao1 diversity of samples grouped by latitude zones. CCA revealed that sample origin, latitude zones, and ocean significantly influenced marine microbial community composition. ( Figure 1C ). The analysis showed that latitude zones, sample origin and the ocean of origin had significant correlations ( p-value< 0.05) with the bacterial communities (Supplementary Table 3). VPA was performed to determine how much variation in species composition can be uniquely and jointly explained by different environmental variables. The ocean of origin was the most significant factor influencing marine microbial community composition among the tested variables, followed by latitude ( Figure 1D ). Alpha diversity indices (Shannon and Chao1) were also calculated to complement the beta diversity patterns. ANOVA results and effect size analysis indicated that latitude zones are the primary contributors to differences in alpha diversity indices (Supplementary Table 4). The sample origin and the ocean of origin also showed significant differences in all alpha diversity indices. Polar regions showed the lowest Shannon and Chao1 diversity indices ( Figure 1E, F ). The bacterial communities present in the temperate regions had the highest alpha diversity indices (Shannon diversity index: 3.37 ± 0.72 and Chao1 diversity index: 160.01 ± 76.71). Pairwise Wilcoxon tests were performed, which showed significant differences in the alpha diversity indices between polar, temperate and tropical zones ( Figure 1E, F ). Given the high correlation between ocean and latitude, and to avoid collinearity in downstream analyses, we retained latitude as the primary environmental variable The Kruskal-Wallis test was conducted at the phylum level to assess taxonomic shifts across latitude zones. Campylobacterota, Bacteroidota and Firmicutes showed the most pronounced differences in abundance (Supplementary Table 5). Campylobacterota was more abundant in temperate regions, whereas Bacteroidota and Firmicutes were enriched in polar and tropical regions, respectively. Genus-level agglomeration retained an optimal number of ASVs while providing adequate resolution for our analysis. Pelagibacter was the most abundant genus in the ocean microbial community. 3.2 Marine Communities Display High Stochastic Community Assembly Processes The NCM analysis was employed to assess the role of neutral processes, such as random dispersal and ecological drift, in shaping the assembly of microbial communities ( Figure 2A-C ). The microbial communities in the polar regions had the lowest R2, and the temperate regions had the highest R2, indicating the best fit with the neutral model. The estimated dispersal parameter (Nm) reflects the combined influence of migration and community size on taxon distribution and was highest in the polar bacterial communities. Download figure Open in new tab Fig. 2: Assessment of neutral model fit across marine microbial communities in polar, temperate, and tropical zones. (A–C) Comparison of the fit of polar (A), temperate (B), and tropical (C) marine microbial communities to the neutral community model. The blue line represents the predicted distribution under the neutral model. Taxa colored blue fit the model predictions, while green and red indicate taxa with significantly higher or lower abundances than expected, respectively. (D) Fraction of neutral taxa identified in polar, temperate, and tropical marine regions. (E) BCom values illustrating the niche width of taxa across polar, temperate, and tropical communities. (F) NST values of the polar, tropical, and temperate regions. Following the NCM analysis, the fraction of taxa that conformed to neutrality was quantified to assess if stochastic processes could explain the community composition. There was a significant difference in the fraction of taxa conforming to NCM across latitude zones ( Figure 2D ) (Wilcoxon test: p-value < 0.001). The polar and temperate regions exhibited the highest and lowest fraction of neutral taxa, respectively. A null model analysis was conducted on PERMDISP to examine the variation in community composition across latitude zones. The null model test results showed that dissimilarities in the polar regions were significantly lower than null expectations (Supplementary Table 7). The temperate regions had a higher dissimilarity than the null expectations, suggesting that environmental filtering plays a strong role in polar microbial community formation and a weaker role in the temperate regions. We also compared the niche width across latitude zones to assess the factors driving community turnover. Polar communities displayed lower BCom values ( Figure 2E ), indicating that the community composition is relatively similar across the samples. In contrast, temperate and tropical regions displayed significantly broader BCom, suggesting greater beta-diversity and spatial turnover. NST was calculated to quantify the relative contribution of the stochastic and deterministic processes ( Figure 2F ). These results imply that environmental heterogeneity and niche-based processes influence the community composition in tropical and temperate regions, whereas the polar communities are more homogeneous. We further applied the iCAMP framework to partition stochastic and deterministic processes into specific ecological mechanisms such as selection, dispersal, and drift to understand the ecological processes driving community assembly ( Figure 3A-C ). Drift was the dominant driver of community assembly across all latitude zones, with its influence being particularly pronounced in the polar regions. Deterministic processes, such as homogeneous and heterogeneous selection, played a relatively more important role in the tropical and temperate communities than in the polar communities. Download figure Open in new tab Fig. 3: Patterns of neutrality, niche breadth, and community assembly processes across latitudinal marine microbial communities. (A–C) Relative contributions of community assembly processes—including dispersal limitation (DL), drift (DR), homogeneous selection (HoS), and heterogeneous selection (HeS)—in (A) polar, (B) temperate, and (C) tropical regions. These results reveal that microbial community assembly in the ocean is shaped by stochastic and deterministic processes, with strong environmental filtering playing dominant but latitude-dependent roles. 3.3 Network Modularity Enhances Perturbation Resistance In Polar Microbiomes We then proceeded to study the co-occurrence patterns using network analysis to understand the ecological interactions shaping these communities. The network constructed using the above methods had 554 nodes and 4227 edges. Overall, positive interactions predominate (92.81%) in the co-occurrence networks. After removing the negative edges, the modularity of the network was calculated using the Louvain algorithm [ 36 ]. The network has a modularity of 0.66. Given the modularity value ( > 0.5), the network has an overall modular structure [ 37 ] with 10 sub-modules with sizes varying from 26 to 101 nodes (Supplementary Figure 3). The network has an average degree of 15.26 and an average clustering coefficient of 0.218 (Supplementary Figure 3). Nodes in the co-occurrence network largely belonged to the phyla Proteobacteria (44.7%), Bacteroidota (17.15%), Actinobacteriota (3.79%), Verrucomicrobiota (3.61%) and Chloroflexota (3.07%) ( Figure 4A ). Inter-phyla interactions comprised 2569 of the total interactions, with the remaining 1354 occurring within phyla. Inter-phyla interactions vastly outnumber intra-phyla interactions across all phyla. Proteobacteria and Thermoplasmota exhibited the highest inter-phyla to intra-phyla interaction ratios ( Figure 4B ). Proteobacteria and Bacteroidota accounted for 72.2% and 15.01% of all the intra-phyla interactions ( Figure 4C ) and had the largest number of inter-phyla interactions ( Figure 4D ). Download figure Open in new tab Fig. 4: Phylum-level structure and interaction patterns in the ocean microbial community network. (A) Distribution of phyla represented by nodes within the ocean microbial community network. (B) Ratio of inter-phyla to intra-phyla interactions among nodes, illustrating connectivity between and within different phyla. (C) Proportion of intra-phyla interactions attributed to each phylum, highlighting internal network structure. (D) Composition and types of inter-phyla interactions across the entire ocean microbial network. We then constructed latitude-zone-specific co-occurrence networks ( Figure 5A-C ). The polar, temperate and tropical co-occurrence networks had 201, 630 and 365 nodes ( Table 1 ). The degree distribution of these networks is shown (Supplementary Figure 3A-C). The temperate co-occurrence network had the highest average degree of 15.81. The co-occurrence network constructed using the samples from polar regions had the least variability in the degree ( Figure 5D ). To assess network robustness, we removed high-degree nodes (hubs) and evaluated the resulting changes in natural connectivity, a measure of network resilience. Natural connectivity decreases with increasing percentage of nodes removed (targeted by degree) across all three networks. The slopes of this decline were 0.00060 for polar networks, 0.00042 for temperate networks, and 0.00055 for tropical networks. A less negative slope in the temperate network indicates greater robustness to targeted node removal than the polar and tropical networks. The polar network had the lowest natural connectivity and the steepest drop upon removing nodes of the highest degree (Supplementary Figure 4). The polar co-occurrence network had the highest density ( Table 1 ). The clustering coefficient was highest in the tropical co-occurrence network. The polar, temperate and tropical co-occurrence networks across latitude zones have a modular structure, with 14, 11 and 12 sub-modules, respectively [ 37 ]. The modularity is reported to be the highest for the polar co-occurrence network. View this table: View inline View popup Download powerpoint Table 1: Properties of polar, temperate and tropical microbial co-occurrence networks, using taxa which are prevalent in at least one per cent of the samples Download figure Open in new tab Fig. 5: Modular structure and connectivity of microbial co-occurrence networks across polar, temperate, and tropical marine regions. (A–C) Co-occurrence networks of polar (A), temperate (B), and tropical (C) marine microbial communities, with nodes colored according to their respective network modules, highlighting modular organisation within each region. (D) Comparison of the average degree (network connectivity) across polar, temperate, and tropical microbial co-occurrence networks, illustrating variability in community connectivity among regions. In addition to the global network properties, the topological properties of the individual genera help understand their ecological roles. Genera are classified into four buckets ( Figure 6 ) based on their within-module (Z) and among-module connectivity (P) [ 38 ]. The peripherals (low Z and low P) are genera that have fewer interactions with other genera within their modules. Most genera in the network were peripherals. The module hubs (high Z and low P) contain genera that have a higher number of interactions with other genera within their own modules. The polar, temperate, and tropical co-occurrence networks had 3, 4, and 1 module hubs, respectively. The polar co-occurrence network had the largest percentage of connectors (high P, low Z) compared to the other co-occurrence networks. Genera acting as connectors in the polar microbial community include Moritella, Oleispira , and Marinomonas . Download figure Open in new tab Fig. 6: Role of taxa in polar, temperate, and tropical marine microbial communities. Taxa are classified according to their network roles—peripherals, connectors, module hubs, and network hubs— based on patterns of inter- and intra-module connectivity within each co-occurrence network. This highlights the structural and functional positions of taxa across distinct climatic regions. 3.4 Specialist Taxa Play A Major Role In Maintaining The Overall Structure Of The Ocean Microbiome Genera are classified as marine generalists (prevalence > 50%) and specialists (prevalence < 5%) ( Figure 7A ). Under this criterion, 8% of the genera were classified as generalists and 12% of the genera were classified as specialists. Generalists and specialists tend to interact amongst themselves, rather than with other genera ( Figure 7B ). Specialists show a larger variability in the degree compared to the generalists ( Figure 7C ). The average degrees of the generalists and specialists were 14 and 13 with standard deviations of 2.85 and 4.11, respectively. Download figure Open in new tab Fig. 7: Analysis of the roles of the generalists and specialists in ocean microbial communities. (A) Occupancy plot depicting the mean abundance and prevalence of different taxa in the ocean microbial network. (B) Overall ocean microbial co-occurrence network depicting the interactions between generalists and specialists. (C) Degree distribution of the generalist and specialist taxa in the ocean microbial network. (D) Topological roles of the generalist and specialist taxa in the ocean microbial network based on their inter-module and intra-module connectivity. (E) Changes in the natural connectivity of the overall ocean microbial network after removing specialists and generalists to highlight their importance. (F) Phyla with a significantly different number of generalists and specialists in the ocean microbial co-occurrence network. To further understand the ecological roles of the generalists and specialists, the inter-module and intra-module connectivity were calculated and plotted ( Figure 7D ). Specialists act as module hubs and connectors. On the other hand, generalists have a limited role in the co-occurrence network as peripherals. To determine the role of the generalists and specialists on network stability, the natural connectivity of the network was calculated after progressively removing the generalist and specialist nodes of the highest degree. There is a steep drop in the natural connectivity of the network upon removal of the specialists compared to removal of the generalists ( Figure 7E ).The generalists belonged to phyla Proteobacteria, Bacteroidota, Actinobacteriota, Cyanobacteria, Mariniso-matota and Verrucomicrobiota . The specialists belonged to a broader range of phyla, including Proteobacteria,Chloroflexota, Bacteroidota, Actinobacteriota, Desulfobacterota, Nitrospirota , and Verrucomicrobiota . There was a significant difference in the number of generalists and specialists belonging to the phyla Chloroflexota and Bacteroidota ( Figure 7F ) (Fisher test p-value < 0.05). Approximately 67% of the generalists belonged to Bacteroidota ; in comparison, only 26% of the specialists belonged to the same phylum. On the other hand, 30% of the specialists belonged to Chloroflexota and no generalists were identified in this phylum. Specialists had significantly higher inter-phyla interactions than intra-phyla interactions (Fisher test p-value < 0.005). Generalists have nearly equal inter- and intra-phyla interactions (Supplementary Figure 5). 4 Discussion We structured our study on the latitude variation as it provided the most homogeneous distribution of samples while capturing climatic and environmental gradients. Previous studies have also identified that latitudinal gradients strongly affect the diversity in marine bacterial communities [ 39 – 41 ], which is consistent with the intermediate disturbance hypothesis, which proposes that diversity peaks when environmental disturbances occur at moderate levels [ 42 ].VPA suggests that the sample origin is a major contributor to bacterial composition. Given that only a small fraction of samples (171 out of 4,583) in our dataset are host-associated, this factor was not detected as significant in the PERMANOVA analysis. Overall, the three factors, namely latitude, sample origin, and ocean, are highly interrelated variables, and the discrepancies in the batch sizes render meaningful insights challenging. Diversity analysis at the genus and phylum levels was performed to understand the community composition. Pelagibacter is the most dominant genus in the marine samples, consistent with its known role as a key player in marine microbial ecosystems [ 43 ]. The differential abundance of Bacteroidota, Firmicutes and Campylobacterota across latitude zones implies that specific phyla may adapt to specific environmental conditions (Supplementary Table 5). Bacteroidota is enriched in polar regions because they can efficiently utilise complex carbohydrates using fermentation pathways in energy-limited environments, providing a competitive advantage in lower temperatures [ 44 ]. Firmicutes have the highest abundance in tropical regions through endospore formation, producing highly resistant spores that can withstand heat and UV radiation [ 45 , 46 ]. Campylobacterota thrive in temperate regions because their key autotrophic enzymes, such as ATP citrate lyase, exhibit mesophilic temperature optima, aligning with the moderate thermal regimes of temperate marine waters [ 47 ]. While this approach sheds light on key ecological mechanisms, the utilisation of 16S rRNA data, results in a lack of species-level resolution. The relative contributions of the deterministic and stochastic processes were estimated across different latitude zones. The host-associated samples and the marine sediment samples have been removed from this analysis to reduce the confounding factors. The neutral theory model, null theory model and iCAMP frameworks were used to understand the roles of selection, dispersal and environmental filtering in shaping the structure of the marine microbial communities [ 11 , 30 ]. These three approaches integrate taxonomic community composition, dispersion metrics and phylogenetic analyses, respectively, to provide a holistic understanding of marine microbial community assembly across latitude zones. NCM analysis revealed that the dispersal parameter (Nm) was the highest for the polar communities ( Figure 2A ). The iCAMP framework indicated that drift was the dominant factor shaping community assembly in the polar microbial communities ( Figure 3A ) [ 48 ]. The polar regions also have the highest proportion of neutral taxa. The seasonal freeze-thaw and extreme cold conditions are probably responsible for the lower diversity ( Figure 1E, F ), which in turn causes smaller community sizes [ 49 ]. As the overall community size decreases, the relative importance of drift increases ( Figure 2A ) [ 50 ].. The PERMDISP and the BCom analyses reveal a more homogeneous community structure caused by strong environmental filtering. Altogether, these analyses suggest that stochastic processes in polar regions operate within narrow environmental constraints. Environmental filtering limits which taxa can persist, while stochastic processes influence their relative abundances. Stochastic processes played a more important role in temperate communities than polar communities, as evidenced by the R2 values, NCM analysis, and the NST values. The high BCom values and the dissimilarity of the temperate communities being higher than the null communities suggest high spatial turnover and environmental heterogeneity in temperate regions. The iCAMP results indicate notable roles of deterministic processes (homogeneous and heterogeneous selection) in shaping microbial community assembly in temperate regions, at the bin-level. Previous studies have also shown that homogeneous selection is a key contributor to community assembly [ 51 ]. This suggests that environmental selection and niche specialisation drive microbial community assembly. The absence of time series data limits the temporal resolution of our study, thereby reducing its ability to capture dynamic changes and temporal patterns within the microbial community. Co-occurrence networks were constructed to investigate the ecological interactions and structural organisation underlying the observed patterns. The polar regions displayed the highest modularity of 0.778 across latitude zones ( Table 1 ). Higher modularity is a characteristic of a more stable community, as perturbations affecting taxa in one module are less likely to affect the rest of the network [ 52 ]. The taxa acting as connectors and module hubs are specifically important for maintaining the network structure and stability. Polar microbial communities displayed the highest fraction of connectors and module hubs ( Figure 6 ), suggesting that certain taxa in these communities play disproportionately important roles in maintaining the overall network stability. The natural connectivity analysis (Supplementary Figure 5) revealed that the polar communities are highly vulnerable to hub removal, as they exhibit the most negative slopes. While high modularity in the polar communities provides local stability, the high dependence on connector genera creates systemic vulnerabilities [ 53 ]. The taxa acting as connectors in the polar microbial co-occurrence network include Moritella [ 54 ] and Oleispira [ 55 ], which require low temperatures for survival and can degrade complex organic material. The increased vulnerability of polar communities due to increased modularity, along with the nature of the connector taxa, suggests that they may face risks from warming temperatures [ 56 ]. While the latitude-specific networks provided valuable insights into how community structure and stability vary across latitude gradients, it is also vital to understand the global ocean microbial co-occurrence network to investigate the interaction patterns and phylum-level organisation. This allowed for the improvement of the understanding of the ecological trends, such as dominant phyla, the nature of the inter- and intra-phyla interactions. Over 60% of the genera in the global ocean network belong to Proteobacteria and Bacteroidota ( Figure 4A ), consistent with findings from earlier ocean microbiome studies [ 17 , 57 – 62 ]. Proteobacteria make up a large proportion of the nodes in the global ocean microbial networks due to their well-documented metabolic versatility and ecological adaptability [ 63 , 64 ]. The high representation of Bacteroidota in the ocean microbial networks can be attributed to their ability to degrade complex carbohydrates, a trait enabled by their specialised polysaccharide utilisation loci (PULs) [ 65 , 66 ]. The ocean microbiome had higher inter-phyla interactions compared to intra-phyla interactions ( Figure 4B ) across nodes. This aligns with studies which suggest that functional complementarity, rather than phylogenetic relatedness, governs ecological interactions in microbial communities [ 67 , 68 ]. Specialists act as module hubs and connectors ( Figure 7D ), underscoring their role as keystone taxa in maintaining network structure and stability. Despite their widespread prevalence, the generalists act as peripherals, indicating that they interact broadly but weakly, or exist transiently in the community, as evidenced by the stochastic community assembly. To further understand the role of the specialists in network stability, the natural connectivity of the network was computed after removing a fraction of the specialists and generalists ( Figure 7E ). The specialists contribute more to the network stability than the generalists. The asymmetric vulnerability suggests that conservation of the specialist taxa is critical for sustaining marine microbial communities under perturbations such as warming and nutrient shifts and show that even rare taxa warrant conservation attention. Future research could utilise temporal data with species-level resolution to enhance understanding of marine microbiome resilience and functional dynamics under ongoing climate change. 5 Data Availability The accession codes for the datasets used in the study are PRJEB42019, PRJEB36282, PRJEB36283, PRJEB25224, PRJNA656268, PRJNA1122929, PRJEB40762, PRJEB45011 and PRJEB27154. 7 Funding P.R. acknowledges the Half-Time Teaching Assistantship (HTTA) from the Ministry of Education, Government of India. A.R. acknowledges support from the project RB22231279BTHINT008481. K.R. acknowledges support from the Wadhwani School of Data Science and AI. 6 Acknowledgements The authors would like to thank Shradha Sharma and Pratyay Sengupta for their valuable insights and feedback on the manuscript. We are also grateful to Barathi L for her guidance and support in applying QIIME2 for data pre-processing. The authors used an AI-based language editing tool (ChatGPT 4.0 and Grammarly) to improve grammar and readability of the manuscript. No AI tools were used for data analysis, interpretation, or drawing scientific conclusions. References [1]. ↵ Shinichi Sunagawa et al. “ Tara Oceans: towards global ocean ecosystems biology ”. en. In: Nature Reviews Microbiology 18 . 8 ( Aug . 2020 ), pp. 428 – 445 . ISSN: 1740-1526 , 1740-1534. doi: 10.1038/s41579-020-0364-5 . URL: https://www.nature.com/articles/s41579-020-0364-5 (visited on 02/27/2025). OpenUrl CrossRef [2]. ↵ Carlos M. Duarte . “ Seafaring in the 21St Century: The Malaspina 2010 Circumnavigation Expedition ”. en. In: Limnology and Oceanography Bulletin 24 . 1 ( Feb . 2015 ), pp. 11 – 14 . ISSN: 1539-607X , 1539-6088. doi: 10.1002/lob.10008 . URL: https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lob.10008 (visited on 07/01/2025). OpenUrl CrossRef [3]. ↵ Guillem Salazar et al. “ Global diversity and biogeography of deep-sea pelagic prokaryotes ”. en. In: The ISME Journal 10 . 3 ( Mar . 2016 ), pp. 596 – 608 . ISSN: 1751-7362 , 1751-7370. doi: 10.1038/ismej.2015.137 . URL: https://academic.oup.com/ismej/article/10/3/596-608/7538240 (visited on 07/01/2025). OpenUrl CrossRef PubMed [4]. ↵ Sean Eddy Shibu Yooseph et al. “ The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families ”. en. In: PLoS Biology 5 . 3 ( Mar . 2007 ). Ed. by Sean Eddy , e16. ISSN: 1545-7885 . doi: 10.1371/journal.pbio.0050016 . URL: https://dx.plos.org/10.1371/journal.pbio.0050016 (visited on 07/01/2025). OpenUrl CrossRef PubMed [5]. ↵ Nancy A Moran Douglas B Rusch et al. “ The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific ”. en. In: PLoS Biology 5 . 3 ( Mar . 2007 ). Ed. by Nancy A Moran , e77. ISSN: 1545-7885 . doi: 10.1371/journal.pbio.0050077 . URL: https://dx.plos.org/10.1371/journal.pbio.0050077 (visited on 02/27/2025). OpenUrl CrossRef PubMed [6]. ↵ Sophie Clayton et al. “ Bio-GO-SHIP: The Time Is Right to Establish Global Repeat Sections of Ocean Biology ”. In: Frontiers in Marine Science 8 ( Jan . 2022 ), p. 767443 . ISSN: 2296-7745 . doi: 10.3389/fmars.2021.767443 . URL: https://www.frontiersin.org/articles/10.3389/fmars.2021.767443/full (visited on 02/27/2025). OpenUrl CrossRef [7]. ↵ Weidong Chen and Donghui Wen . “ Archaeal and bacterial communities assembly and co-occurrence networks in subtropical mangrove sediments under Spartina alterniflora invasion ”. en. In: Environmental Microbiome 16 . 1 ( Dec . 2021 ), p. 10 . ISSN: 2524-6372 . doi: 10.1186/s40793-021-00377-y . URL: https://environmentalmicrobiome.biomedcentral.com/articles/10.1186/s40793-021-00377-y (visited on 07/01/2025). OpenUrl CrossRef PubMed [8]. Pengfei Li et al. “ Spatial distribution pattern across multiple microbial groups along an environmental stress gradient in tobacco soil ”. en. In: Annals of Microbiology 73 . 1 ( June 2023 ), p. 20 . ISSN: 1869-2044 . doi: 10.1186/s13213-023-01717-8 . URL: https://annalsmicrobiology.biomedcentral.com/articles/10.1186/s13213-023-01717-8 (visited on 07/01/2025). OpenUrl CrossRef [9]. ↵ Daliang Ning et al. “ Environmental stress mediates groundwater microbial community assembly ”. en. In: Nature Microbiology 9 . 2 ( Jan . 2024 ), pp. 490 – 501 . ISSN: 2058-5276 . doi: 10.1038/s41564-023-01573-x . URL: https://www.nature.com/articles/s41564-023-01573-x (visited on 07/01/2025). OpenUrl CrossRef PubMed [10]. ↵ William T. Sloan et al. “ Quantifying the roles of immigration and chance in shaping prokaryote community structure ”. en. In: Environmental Microbiology 8 . 4 ( Apr . 2006 ), pp. 732 – 740 . ISSN: 1462-2912 , 1462-2920. doi: 10.1111/j.1462-2920.2005.00956.x . URL: https://sfamjournals.onlinelibrary.wiley.com/doi/10.1111/j.1462-2920.2005.00956.x (visited on 06/29/2025). OpenUrl CrossRef PubMed Web of Science [11]. ↵ Daliang Ning et al. “ A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming ”. en. In: Nature Communications 11 . 1 ( Sept . 2020 ), p. 4717 . ISSN: 2041-1723 . doi: 10.1038/s41467-020-18560-z . URL: https://www.nature.com/articles/s41467-020-18560-z (visited on 06/29/2025). OpenUrl CrossRef PubMed [12]. ↵ Christian Von Mering Zachary D. Kurtz et al. “ Sparse and Compositionally Robust Inference of Microbial Ecological Networks ”. en. In: PLOS Computational Biology 11 . 5 ( May 2015 ). Ed. by Christian Von Mering , e1004226. ISSN: 1553-7358 . doi: 10.1371/journal.pcbi.1004226 . URL: https://dx.plos.org/10.1371/journal.pcbi.1004226 (visited on 03/04/2025). OpenUrl CrossRef PubMed [13]. ↵ Manoeli Lupatini et al. “ Network topology reveals high connectance levels and few key microbial genera within soils ”. In: Frontiers in Environmental Science 2 ( May 2014 ). ISSN: 2296-665X . doi: 10.3389/fenvs.2014.00010 . URL: http://journal.frontiersin.org/article/10.3389/fenvs.2014.00010/abstract (visited on 07/01/2025). OpenUrl CrossRef [14]. ↵ Anna J. Székely and Silke Langenheder . “ The importance of species sorting differs between habitat generalists and specialists in bacterial communities ”. en. In: FEMS Microbiology Ecology 87 . 1 ( Jan . 2014 ), pp. 102 – 112 . ISSN: 01686496 . doi: 10.1111/1574-6941.12195 . URL: https://academic.oup.com/femsec/article-lookup/doi/10.1111/1574-6941.12195 (visited on 07/01/2025). OpenUrl CrossRef PubMed [15]. ↵ Shubha N. Pandit , Jurek Kolasa , and Karl Cottenie . “ Contrasts between habitat generalists and spe-cialists: an empirical extension to the basic metacommunity framework ”. en. In: Ecology 90 . 8 ( Aug . 2009 ), pp. 2253 – 2262 . ISSN: 0012-9658 , 1939-9170. doi: 10.1890/08-0851.1 . URL: https://esajournals.onlinelibrary.wiley.com/doi/10.1890/08-0851.1 (visited on 07/01/2025). OpenUrl CrossRef PubMed Web of Science [16]. ↵ Fabiola Valenzuela-González et al. “ The 16S rRNA gene in the study of marine microbial communities ”. In: Ciencias Marinas 41 . 4 ( Dec . 2015 ), pp. 297 – 313 . ISSN: 2395-9053 , 0185-3880. doi: 10.7773/cm.v41i4.2492 . URL: http://cienciasmarinas.com.mx/index.php/cmarinas/article/view/2492 (visited on 07/01/2025). OpenUrl CrossRef [17]. ↵ Matthew T. Cottrell and David L. Kirchman . “ Community Composition of Marine Bacterioplankton Determined by 16S rRNA Gene Clone Libraries and Fluorescence In Situ Hybridization ”. en. In: Applied and Environmental Microbiology 66 . 12 ( Dec . 2000 ), pp. 5116 – 5122 . ISSN: 0099-2240 , 1098-5336. doi: 10.1128/AEM.66.12.5116-5122.2000 . URL: https://journals.asm.org/doi/10.1128/AEM.66.12.5116-5122.2000 (visited on 06/30/2025). OpenUrl Abstract / FREE Full Text [18]. ↵ Joshua Ladau et al. “ Global marine bacterial diversity peaks at high latitudes in winter ”. en. In: The ISME Journal 7 . 9 ( Sept . 2013 ), pp. 1669 – 1677 . ISSN: 1751-7362 , 1751-7370. doi: 10.1038/ismej.2013.37 . URL: https://academic.oup.com/ismej/article/7/9/1669/7590285 (visited on 07/01/2025). OpenUrl CrossRef PubMed Web of Science [19]. ↵ Morgan G. I. Langille Jeremiah J. Minich et al. “ High-Throughput Miniaturized 16S rRNA Amplicon Library Preparation Reduces Costs while Preserving Microbiome Integrity ”. en. In: mSystems 3 . 6 ( Oct . 2018 ). Ed. by Morgan G. I. Langille . Publisher: American Society for Microbiology . ISSN: 2379-5077 . doi: 10.1128/msystems.00166-18 . URL: https://journals.asm.org/doi/10.1128/msystems.00166-18 (visited on 07/16/2025). OpenUrl CrossRef [20]. ↵ Quanchao Zeng and Shaoshan An . “ Identifying the Biogeographic Patterns of Rare and Abundant Bacterial Communities Using Different Primer Sets on the Loess Plateau ”. en. In: Microorganisms 9 . 1 ( Jan . 2021 ). Publisher: MDPI AG , p. 139 . ISSN: 2076-2607 . doi: 10.3390/microorganisms9010139 . URL: https://www.mdpi.com/2076-2607/9/1/139 (visited on 07/16/2025). OpenUrl CrossRef [21]. ↵ Clara Ruiz-González et al. “ Higher contribution of globally rare bacterial taxa reflects environmental transitions across the surface ocean ”. en. In: Molecular Ecology 28 . 8 ( Apr . 2019 ), pp. 1930 – 1945 . ISSN: 0962-1083 , 1365-294X. doi: 10.1111/mec.15026 . URL: https://onlinelibrary.wiley.com/doi/10.1111/mec.15026 (visited on 02/27/2025). OpenUrl CrossRef PubMed [22]. ↵ Justin P. Shaffer et al. “ Standardized multi-omics of Earth’s microbiomes reveals microbial and metabo-lite diversity ”. en. In: Nature Microbiology 7 . 12 ( Nov . 2022 ), pp. 2128 – 2150 . ISSN: 2058-5276 . doi: 10.1038/s41564-022-01266-x . URL: https://www.nature.com/articles/s41564-022-01266-x (visited on 02/27/2025). OpenUrl CrossRef PubMed [23]. ↵ Luke R. Thompson et al. “ A communal catalogue reveals Earth’s multiscale microbial diversity ”. en. In: Nature 551 . 7681 ( Nov . 2017 ), pp. 457 – 463 . ISSN: 0028-0836 , 1476-4687. doi: 10.1038/nature24621 . URL: https://www.nature.com/articles/nature24621 (visited on 02/27/2025). OpenUrl CrossRef PubMed [24]. ↵ J Gregory Caporaso et al. “ QIIME allows analysis of high-throughput community sequencing data ”. en. In: Nature Methods 7 . 5 ( May 2010 ), pp. 335 – 336 . ISSN: 1548-7091 , 1548-7105. doi: 10.1038/nmeth.f.303 . URL: https://www.nature.com/articles/nmeth.f.303 (visited on 02/27/2025). OpenUrl CrossRef PubMed Web of Science [25]. ↵ Benjamin J Callahan et al. “ DADA2: High-resolution sample inference from Illumina amplicon data ”. en. In: Nature Methods 13 . 7 ( July 2016 ), pp. 581 – 583 . ISSN: 1548-7091 , 1548-7105. doi: 10.1038/nmeth.3869 . URL: https://www.nature.com/articles/nmeth.3869 (visited on 02/27/2025). OpenUrl CrossRef PubMed [26]. ↵ Daniel McDonald et al. “ Greengenes2 unifies microbial data in a single reference tree ”. en. In: Nature Biotechnology 42 . 5 ( May 2024 ), pp. 715 – 718 . ISSN: 1087-0156 , 1546-1696. doi: 10.1038/s41587-023-01845-1 . URL: https://www.nature.com/articles/s41587-023-01845-1 (visited on 02/27/2025). OpenUrl CrossRef [27]. ↵ Philip Dixon . “ VEGAN, a package of R functions for community ecology ”. en. In: Journal of Vegetation Science 14 . 6 ( Dec . 2003 ), pp. 927 – 930 . ISSN: 1100-9233 , 1654-1103. doi: 10.1111/j.1654-1103.2003.tb02228.x . URL: https://onlinelibrary.wiley.com/doi/10.1111/j.1654-1103.2003.tb02228.x (visited on 03/04/2025). OpenUrl CrossRef Web of Science [28]. ↵ R Core Team . R: A Language and Environment for Statistical Computing . Vienna, Austria . URL: https://www.R-project.org/ . [29]. ↵ Michael Watson Paul J. McMurdie and Susan Holmes . “ phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data ”. en. In: PLoS ONE 8 . 4 ( Apr . 2013 ). Ed. by Michael Watson , e61217. ISSN: 1932-6203 . doi: 10.1371/journal.pone.0061217 . URL: https://dx.plos.org/10.1371/journal.pone.0061217 (visited on 03/04/2025). OpenUrl CrossRef PubMed [30]. ↵ Richard Levins . Evolution in Changing Environments: Some Theoretical Explorations. (MPB-2) . Princeton University Press , Dec . 1968 . ISBN: 978-0-691-20941-8 . doi: 10.1515/9780691209418 . URL: https://www.degruyter.com/document/doi/10.1515/9780691209418/html (visited on 06/29/2025). OpenUrl CrossRef [31]. ↵ J Zhang and MJ Zhang . Species association analysis . R package version 0.2.1 . 2013 . URL: http://CRAN.R-project.org/package=spaa . [32]. ↵ Daliang Ning et al. “ A general framework for quantitatively assessing ecological stochasticity ”. In: Proceedings of the National Academy of Sciences 116 . 34 ( 2019 ), pp. 16892 – 16898 . doi: 10.1073/pnas.1904623116 . OpenUrl Abstract / FREE Full Text [33]. ↵ Maintainer Gabor Csardi . “ Package ‘igraph’ ”. In: Last accessed 3 . 09 ( 2013 ), p. 2013 . OpenUrl [34]. ↵ Stefanie Peschel et al. “ NetCoMi: network construction and comparison for microbiome data in R ”. In: Briefings in bioinformatics 22 . 4 ( 2021 ). Publisher: Oxford University Press , bbaa290 . OpenUrl [35]. ↵ Hadley Wickham . ggplot2: elegant graphics for data analysis. eng . Second edition. Use R! Cham : Springer international publishing , 2016 . ISBN: 978-3-319-24277-4 . [36]. ↵ Vincent D Blondel et al. “ Fast unfolding of communities in large networks ”. In: Journal of Statistical Mechanics: Theory and Experiment 2008 . 10 ( Oct . 2008 ). Publisher: IOP Publishing , P10008 . ISSN: 1742-5468 . doi: 10.1088/1742-5468/2008/10/p10008 . URL: https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008 (visited on 05/23/2025). OpenUrl CrossRef [37]. ↵ M. E. J. Newman . “ Modularity and community structure in networks ”. en. In: Proceedings of the National Academy of Sciences 103 . 23 ( June 2006 ). Publisher: Proceedings of the National Academy of Sciences , pp. 8577 – 8582 . ISSN: 0027-8424 , 1091-6490. doi: 10.1073/pnas.0601602103 . URL: https://pnas.org/doi/full/10.1073/pnas.0601602103 (visited on 05/23/2025). OpenUrl Abstract / FREE Full Text [38]. ↵ Roger Guimerà and LuÍs A. Nunes Amaral . “ Functional cartography of complex metabolic networks ”. en. In: Nature 433 . 7028 ( Feb . 2005 ), pp. 895 – 900 . ISSN: 0028-0836 , 1476-4687. doi: 10.1038/nature03288 . URL: https://www.nature.com/articles/nature03288 (visited on 05/28/2025). OpenUrl CrossRef PubMed Web of Science [39]. ↵ Jed A. Fuhrman et al. “ A latitudinal diversity gradient in planktonic marine bacteria ”. en. In: Proceedings of the National Academy of Sciences 105 . 22 ( June 2008 ), pp. 7774 – 7778 . ISSN: 0027-8424 , 1091-6490. doi: 10.1073/pnas.0803070105 . URL: https://pnas.org/doi/full/10.1073/pnas.0803070105 (visited on 03/12/2025). OpenUrl Abstract / FREE Full Text [40]. Shinichi Sunagawa et al. “ Structure and function of the global ocean microbiome ”. en. In: Science 348 . 6237 ( May 2015 ), p. 1261359 . ISSN: 0036-8075 , 1095-9203. doi: 10.1126/science.1261359 . URL: https://www.science.org/doi/10.1126/science.1261359 (visited on 03/13/2025). OpenUrl Abstract / FREE Full Text [41]. ↵ Jeroen Raes et al. “ Toward molecular trait-based ecology through integration of biogeochemical, geo-graphical and metagenomic data ”. en. In: Molecular Systems Biology 7 . 1 ( Jan . 2011 ), p. 473 . ISSN: 1744-4292 , 1744-4292. doi: 10.1038/msb.2011.6 . URL: https://www.embopress.org/doi/10.1038/msb.2011.6 (visited on 03/13/2025). OpenUrl Abstract / FREE Full Text [42]. ↵ Roman Dial and Jonathan Roughgarden . “ THEORY OF MARINE COMMUNITIES: THE INTER-MEDIATE DISTURBANCE HYPOTHESIS ”. en. In: Ecology 79 . 4 ( June 1998 ), pp. 1412 – 1424 . ISSN: 0012-9658 . doi: 10.1890/0012-9658(1998)079[1412:TOMCTI]2.0.CO;2 . URL: http://doi.wiley.com/10.1890/0012-9658(1998)079%5B1412:TOMCTI%5D2.0.CO;2 (visited on 03/13/2025). OpenUrl CrossRef Web of Science [43]. ↵ Jing Sun et al. “ The abundant marine bacterium Pelagibacter simultaneously catabolizes dimethylsul-foniopropionate to the gases dimethyl sulfide and methanethiol ”. en. In: Nature Microbiology 1 . 8 ( May 2016 ), p. 16065 . ISSN: 2058-5276 . doi: 10.1038/nmicrobiol.2016.65 . URL: https://www.nature.com/articles/nmicrobiol201665 (visited on 03/13/2025). OpenUrl CrossRef [44]. ↵ Hannah Martin et al. “ Metabolism of hemicelluloses by root-associated Bacteroidota species ”. en. In: The ISME Journal 19 . 1 ( Jan . 2025 ), wraf022 . ISSN: 1751-7362 , 1751-7370. doi: 10.1093/ismejo/wraf022 . URL: https://academic.oup.com/ismej/article/doi/10.1093/ismejo/wraf022/8003669 (visited on 06/30/2025). OpenUrl CrossRef [45]. ↵ Adam Driks and Patrick Eichenberger. Michael Y. Galperin . “ Genome Diversity of Spore-Forming Firmicutes ”. en. In: Microbiology Spectrum 1 . 2 ( Dec . 2013 ). Ed. by Adam Driks and Patrick Eichenberger. Publisher: American Society for Microbiology . ISSN: 2165-0497 , 2165-0497. doi: 10.1128/microbiolspectrum.tbs-0015-2012 . URL: https://journals.asm.org/doi/10.1128/microbiolspectrum.TBS-0015-2012 (visited on 07/09/2025). OpenUrl CrossRef [46]. ↵ Peter Setlow . “ I will survive: DNA protection in bacterial spores ”. en. In: Trends in Microbiology 15 . 4 ( Apr . 2007 ). Publisher: Elsevier BV , pp. 172 – 180 . ISSN: 0966-842X . doi: 10.1016/j.tim.2007.02.004 . URL: https://linkinghub.elsevier.com/retrieve/pii/S0966842X07000261 (visited on 07/09/2025). OpenUrl CrossRef PubMed Web of Science [47]. ↵ Johannes Sikorski et al. “ Complete genome sequence of Sulfurimonas autotrophica type strain (OK10T )”. en. In: Standards in Genomic Sciences 3 . 2 ( Sept . 2010 ). Publisher: Springer Science and Business Media LLC , pp. 194 – 202 . ISSN: 1944-3277 . doi: 10.4056/sigs.1173118 . URL: https://environmentalmicrobiome.biomedcentral.com/articles/10.4056/sigs.1173118 (visited on 07/09/2025). OpenUrl CrossRef PubMed Web of Science [48]. ↵ Pierre E Galand et al. “ Hydrography shapes bacterial biogeography of the deep Arctic Ocean ”. en. In: The ISME Journal 4 . 4 ( Apr . 2010 ). Publisher: Oxford University Press (OUP ), pp. 564 – 576 . ISSN: 1751-7362 , 1751-7370. doi: 10.1038/ismej.2009.134 . URL: https://academic.oup.com/ismej/article/4/4/564/7588076 (visited on 07/23/2025). OpenUrl CrossRef PubMed Web of Science [49]. ↵ Joanna E Sawicka et al. “ Effects of freeze–thaw cycles on anaerobic microbial processes in an Arctic intertidal mud flat ”. en. In: The ISME Journal 4 . 4 ( Apr . 2010 ), pp. 585 – 594 . ISSN: 1751-7362 , 1751-7370. doi: 10.1038/ismej.2009.140 . URL: https://academic.oup.com/ismej/article/4/4/585/7588023 (visited on 08/05/2025). OpenUrl CrossRef [50]. ↵ Stephen P. Hubbell . The unified neutral theory of biodiversity and biogeography . Monographs in population biology 32 . Princeton : Princeton University Press , 2001 . ISBN: 978-0-691-02129-4 978-0-691-02128-7 . [51]. ↵ Felix Milke et al. “ Selection, drift and community interactions shape microbial biogeographic patterns in the Pacific Ocean ”. en. In: The ISME Journal 16 . 12 ( Dec . 2022 ). Publisher: Oxford University Press (OUP) , pp. 2653 – 2665 . ISSN: 1751-7362 , 1751-7370. doi: 10.1038/s41396-022-01318-4 . URL: https://academic.oup.com/ismej/article/16/12/2653-2665/7474068 (visited on 07/23/2025). OpenUrl CrossRef [52]. ↵ Marco Fabbrini et al. “ Connect the dots: sketching out microbiome interactions through networking approaches ”. In: Microbiome Research Reports 2 . 4 ( July 2023 ). ISSN: 2771-5965 . doi: 10.20517/mrr.2023.25 . URL: https://www.oaepublish.com/articles/mrr.2023.25 (visited on 06/30/2025). OpenUrl CrossRef [53]. ↵ Ahmad F. Al Musawi , Satyaki Roy , and Preetam Ghosh . “ Examining indicators of complex network vulnerability across diverse attack scenarios ”. en. In: Scientific Reports 13 . 1 ( Oct . 2023 ), p. 18208 . ISSN: 2045-2322 . doi: 10.1038/s41598-023-45218-9 . URL: https://www.nature.com/articles/s41598-023-45218-9 (visited on 06/30/2025). OpenUrl CrossRef PubMed [54]. ↵ Ying Xu et al. “Moritella Cold-Active Dihydrofolate Reductase: Are There Natural Limits to Optimization of Catalytic Efficiency at Low Temperature?” en . In: Journal of Bacteriology 185 . 18 ( Sept . 2003 ). Publisher: American Society for Microbiology , pp. 5519 – 5526 . ISSN: 0021-9193 , 1098-5530. doi: 10.1128/jb.185.18.5519-5526.2003 . URL: https://journals.asm.org/doi/10.1128/JB.185.18.5519-5526.2003 (visited on 07/15/2025). OpenUrl CrossRef [55]. ↵ Michail M. Yakimov et al. “ Oleispira antarctica gen. nov., sp. nov., a novel hydrocarbonoclastic marine bacterium isolated from Antarctic coastal sea water ”. en. In: International Journal of Systematic and Evolutionary Microbiology 53 . 3 ( May 2003 ). Publisher: Microbiology Society , pp. 779 – 785 . ISSN: 1466-5026 , 1466-5034. doi: 10.1099/ijs.0.02366-0 . URL: https://www.microbiologyresearch.org/content/journal/ijsem/10.1099/ijs.0.02366-0 (visited on 07/15/2025). OpenUrl CrossRef PubMed Web of Science [56]. ↵ Pabulo Rampelotto . “ Polar Microbiology: Recent Advances and Future Perspectives ”. en. In: Biology 3 . 1 ( Feb . 2014 ), pp. 81 – 84 . ISSN: 2079-7737 . doi: 10.3390/biology3010081 . URL: https://www.mdpi.com/2079-7737/3/1/81 (visited on 06/30/2025). OpenUrl CrossRef [57]. ↵ Heike Eilers et al. “ Culturability and In Situ Abundance of Pelagic Bacteria from the North Sea ”. en. In: Applied and Environmental Microbiology 66 . 7 ( July 2000 ), pp. 3044 – 3051 . ISSN: 0099-2240 , 1098-5336. doi: 10.1128/AEM.66.7.3044-3051.2000 . URL: https://journals.asm.org/doi/10.1128/AEM.66.7.3044-3051.2000 (visited on 06/30/2025). OpenUrl Abstract / FREE Full Text [58]. Enric Llobet-Brossa , Ramon RossellÓ-Mora , and Rudolf Amann . “ Microbial Community Composition of Wadden Sea Sediments as Revealed by Fluorescence In Situ Hybridization ”. en. In: Applied and Environmental Microbiology 64 . 7 ( July 1998 ), pp. 2691 – 2696 . ISSN: 0099-2240 , 1098-5336. doi: 10.1128/AEM.64.7.2691-2696.1998 . URL: https://journals.asm.org/doi/10.1128/AEM.64.7.2691-2696.1998 (visited on 06/30/2025). OpenUrl Abstract / FREE Full Text [59]. Frank Oliver GlÖckner , Bernhard M. Fuchs , and Rudolf Amann . “ Bacterioplankton Compositions of Lakes and Oceans: a First Comparison Based on Fluorescence In Situ Hybridization ”. en. In: Applied and Environmental Microbiology 65 . 8 ( Aug . 1999 ), pp. 3721 – 3726 . ISSN: 0099-2240 , 1098-5336. doi: 10.1128/AEM.65.8.3721-3726.1999 . URL: https://journals.asm.org/doi/10.1128/AEM.65.8.3721-3726.1999 (visited on 06/30/2025). OpenUrl Abstract / FREE Full Text [60]. M Simon , F. GlÖckner , and R Amann . “ Different community structure and temperature optima of heterotrophic picoplankton in various regions of the Southern Ocean ”. en. In: Aquatic Microbial Ecology 18 ( 1999 ), pp. 275 – 284 . ISSN: 0948-3055 , 1616-1564. doi: 10.3354/ame018275 . URL: http://www.int-res.com/abstracts/ame/v18/n3/p275-284/ (visited on 06/30/2025). OpenUrl CrossRef [61]. Å HagstrÖm , J Pinhassi , and Ul Zweifel . “ Biogeographical diversity among marine bacterioplankton ”. en. In: Aquatic Microbial Ecology 21 ( 2000 ), pp. 231 – 244 . ISSN: 0948-3055 , 1616-1564. doi: 10.3354/ame021231 . URL: http://www.int-res.com/abstracts/ame/v21/n3/p231-244/ (visited on 06/30/2025). OpenUrl CrossRef [62]. ↵ Matthew T. Cottrell and David L. Kirchman . “ Natural Assemblages of Marine Proteobacteria and Members of the Cytophaga-Flavobacter Cluster Consuming Low- and High-Molecular-Weight Dissolved Organic Matter ”. en. In: Applied and Environmental Microbiology 66 . 4 ( Apr . 2000 ), pp. 1692 – 1697 . ISSN: 0099-2240 , 1098-5336. doi: 10.1128/AEM.66.4.1692-1697.2000 . URL: https://journals.asm.org/doi/10.1128/AEM.66.4.1692-1697.2000 (visited on 06/30/2025). OpenUrl Abstract / FREE Full Text [63]. ↵ Zhichao Zhou et al. “ Genome diversification in globally distributed novel marine Proteobacteria is linked to environmental adaptation ”. en. In: The ISME Journal 14 . 8 ( Aug . 2020 ), pp. 2060 – 2077 . ISSN: 1751-7362 , 1751-7370. doi: 10.1038/s41396-020-0669-4 . URL: https://academic.oup.com/ismej/article/14/8/2060-2077/7474885 (visited on 06/30/2025). OpenUrl CrossRef PubMed [64]. ↵ Alejandro A. Murillo et al. “ Enhanced metabolic versatility of planktonic sulfur-oxidizing Î 3 -proteobacteria in an oxygen-deficient coastal ecosystem ”. In: Frontiers in Marine Science 1 ( July 2014 ). ISSN: 2296-7745 . doi: 10.3389/fmars.2014.00018 . URL: http://journal.frontiersin.org/article/10.3389/fmars.2014.00018/abstract (visited on 06/30/2025). OpenUrl CrossRef [65]. ↵ Fernanda Mandelli et al. “ A functionally augmented carbohydrate utilization locus from herbivore gut microbiota fueled by dietary-glucans ”. en. In: npj Biofilms and Microbiomes 10 . 1 ( Oct . 2024 ), p. 105 . ISSN: 2055-5008 . doi: 10.1038/s41522-024-00578-6 . URL: https://www.nature.com/articles/s41522-024-00578-6 (visited on 06/30/2025). OpenUrl CrossRef [66]. ↵ Nicolas Terrapon et al. “ Automatic prediction of polysaccharide utilization loci in Bacteroidetes species ”. en. In: Bioinformatics 31 . 5 ( Mar . 2015 ), pp. 647 – 655 . ISSN: 1367-4811 , 1367-4803. doi: 10.1093/17 bioinformatics/btu716. URL: https://academic.oup.com/bioinformatics/article/31/5/647/317967 (visited on 06/30/2025). OpenUrl CrossRef [67]. ↵ Christos A. Ouzounis Karoline Faust et al. “ Microbial Co-occurrence Relationships in the Human Microbiome ”. en. In: PLoS Computational Biology 8 . 7 ( July 2012 ). Ed. by Christos A. Ouzounis , e1002606 . ISSN: 1553-7358 . doi: 10.1371/journal.pcbi.1002606 . URL: https://dx.plos.org/10.1371/journal.pcbi.1002606 (visited on 06/30/2025). OpenUrl CrossRef PubMed [68]. ↵ Javier Tamames et al. “ Quantifying the Relative Importance of Phylogeny and Environmental Preferences As Drivers of Gene Content in Prokaryotic Microorganisms ”. In: Frontiers in Microbiology 7 ( Mar . 2016 ). ISSN: 1664-302X . doi: 10.3389/fmicb.2016.00433 . URL: http://journal.frontiersin.org/Article/10.3389/fmicb.2016.00433/abstract (visited on 06/30/2025). 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Share Deciphering Global Patterns of Marine Microbial Community Assembly and Network Stability Pranathi Ravikumar , Aarti Ravindran , Karthik Raman bioRxiv 2025.09.19.677047; doi: https://doi.org/10.1101/2025.09.19.677047 Share This Article: Copy Citation Tools Deciphering Global Patterns of Marine Microbial Community Assembly and Network Stability Pranathi Ravikumar , Aarti Ravindran , Karthik Raman bioRxiv 2025.09.19.677047; doi: https://doi.org/10.1101/2025.09.19.677047 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Systems Biology Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41910) Biophysics (21436) Cancer Biology (18576) Cell Biology (25480) Clinical Trials (138) Developmental Biology (13368) Ecology (19887) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15598) Genomics (22482) Immunology (17726) Microbiology (40360) Molecular Biology (17163) Neuroscience (88534) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15129) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9817) Zoology (2269)