Environmental heterogeneity overrides host phylogenetic distance in shaping the microbiome of Acropora corals

Environmental heterogeneity overrides host phylogenetic distance in shaping the microbiome of Acropora corals | 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 Environmental heterogeneity overrides host phylogenetic distance in shaping the microbiome of Acropora corals LiJing Li, Wen Yu, Fengtong Chang, Kuo Gao, Jingjing Zhang, Junjie Zhou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8795967/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coral ecosystems are among the most representative symbiosis systems, with profound scientific significance for understanding the relationships between host and their microbiome. Environmental filtering and host phylogeny play essential roles in maintaining the microbiome of coral holobionts, yet their relative contribution to community assembly remains unsettled. In this study, we analyzed the bacterial composition of 170 samples from 47 Acropora species across two geomorphologically contrasting habitats in the South China Sea, the steep-sloped Meiji Reef and the flat Beiwai Reef. Our results demonstrate that Acropora corals from the two microhabitats host specialized bacterial assemblages distinct from those in the surrounding seawater, which are primarily shaped by deterministic processes. Within Acropora genus, bacterial communities associated with hosts showed significant differences in taxonomic composition between the two habitats, and environmental drivers such as dissolved oxygen and primary production outweighed host phylogenetic signals in shaping bacterial community structure. Despite differences in microbial community composition, similar metabolic pathways were enriched in both habitats, representing core functional stability across environments. Co-occurrence network analysis further revealed that corals in these two habitats employed distinct topological strategies to achieve microbiome functional stability. These findings indicate that taxonomic flexibility combined with functional stability forms a stable yet adaptable strategy, allowing Acropora to adjust to diverse environmental conditions. Our study highlights the vital role of environmental-microbial synergy in coral resilience and offers a theoretical foundation for microbe-informed reef restoration amid rapid global change. Coral microbiome Bacterial community assembly Assemblage plasticity Deterministic processes Environmental filtering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Coral reef ecosystems are widely recognized as an important natural system for investigating host-associated microbiomes in natural environments (Pollock et al. 2018 ; van Oppen and Blackall 2019 ; Mohamed et al. 2023 ). In this context, reef-building corals function as holobionts, and their adaptability depends not only on the host’s physiological state but also on a symbiotic consortium comprising zooxanthellae, bacteria, archaea, viruses, and other microorganisms (Bourne et al. 2016 ; Stévenne et al. 2021 ; Voolstra et al. 2024 ). Within this functional unit, bacterial communities play critical roles in the host’s health and ecological adaptation by mediating key nutrient cycling processes (van Oppen and Blackall 2019 ; Mohamed et al. 2023 ; Voolstra et al. 2024 ), metabolic complementarity (Kullapanich et al. 2021 ; Mohamed et al. 2023 ; Messer et al. 2024 ), pathogen antagonism (Mascuch et al. 2023 ; Azizah et al. 2024 ), immune modulation (Palmer et al. 2011 ; Barno et al. 2021 ; Pogoreutz et al. 2022 ), and signal regulation (Pogoreutz et al. 2022 ; He et al. 2024 ; Messer et al. 2024 ). This highly coordinated symbiotic relationship endows corals with a microbe-mediated buffer, facilitating adaptation to diverse environmental conditions. Yet, how corals acquire their microbial partners from the environment, and whether closely related corals share a conserved core functional microbiome, remain unresolved questions in coral microbiome studies. Corals harbor highly specific core microbial assemblages that are distinct from those of the surrounding seawater (Glasl et al. 2019a ; Weber et al. 2019 ; Zhang et al. 2021a ). Previous studies have suggested that either environmental filtering (Epstein et al. 2019 ; van Oppen and Blackall 2019 ; Pei et al. 2023 ) or host-specific filtering (Pollock et al. 2018 ; Glasl et al. 2019b ; Zhang et al. 2021a ; Ricci et al. 2022 ) is the main driver of microbial community assembly in the coral microbiome. Proponents of environmental filtering argue that external factors, including depth (Pattarach et al. 2024 ; Vohsen and Herrera 2024 ), and water quality (Glasl et al. 2019a ; Nelson et al. 2023 ; Zhu et al. 2023 ), are the primary drivers of microbial community assembly (Gantt et al. 2024 ). For instance, a recent study suggests that reef environments can substantially reshape microbial partners within closely related or genetically identical coral populations, highlighting the remarkable plasticity of the symbiotic microbiome in response to localized environmental fluctuations (Kriefall et al. 2022 ). In contrast, other studies emphasize the dominant role of host-driven selection in shaping microbial assemblages, demonstrating that corals can maintain stable core microbiomes across vast geographic distances (Epstein et al. 2019 ; Ziegler et al. 2019 ; Galand et al. 2023 ). The concept of phylosymbiosis suggests that host-specific physiological traits and phylogeny impose stringent constraints on microbial recruitment, thereby buffering the holobiont against environmental fluctuations (Pollock et al. 2018 ; Mohamed et al. 2023 ). The tension between environmentally driven adaptive plasticity along ecological gradients and structural consistency imposed by host evolutionary history limits our comprehensive understanding of how coral holobionts achieve functional stability and metabolic equilibrium within complex and dynamic reef landscapes (Ziegler et al. 2019 ). Staghorn corals ( Acropora spp.), renowned for their rapid growth and exceptional ecological resilience, contribute to maintaining the structural complexity of coral reefs (Siqueira et al. 2022 ). Over 140 Acropora species have been described globally (Rassmussen et al. 2025 ; Hoeksema et al. 2026 ), with more than 60 documented in the South China Sea, where many species show sympatric distributions but exhibit pronounced differences in community composition across contrasting habitats (Huang et al. 2020 , 2025 ). For instance, a previous study found that habitats with higher spatial complexity and steeper depth gradients typically support higher Acropora species richness and more heterogeneous coral communities (Sannassy Pilly et al. 2022 ). The Meiji (hereafter MJ) and Beiwai (hereafter BW) coral reefs are located at the same latitude yet exhibit sharply divergent geomorphology. BW reef is relatively flat and shallow, with depths ranging from 5 to 15 m, whereas MJ reef features a much steeper slope, extending from 5 to 50 m. Previous studies have revealed that both reefs host multiple Acropora species (Zhao et al. 2013 ; Huang et al. 2025 ), which offer a natural contrast for investigating mechanisms underlying symbiont microbiome assembly at the intrageneric scale. In this study, we systematically investigated the characteristics of the symbiotic bacterial communities of Acropora corals across the heterogeneous benthic habitats of MJ and BW reefs in the South China Sea. Multiscale community analysis was conducted to unravel the relative roles of stochastic and deterministic processes in shaping the host-associated bacterial communities, and to clarify the relative influences of local environmental factors and host phylogenetic signals. Integrated analyses enabled us to assess the extent of symbiotic bacterial plasticity and its role in maintaining stable core metabolism and facilitating holobiont adaptation to diverse habitats. Our findings advance the theoretical understanding of how bacterial communities contribute to the adaptive resilience of Acropora and provide insights for the conservation and restoration of coral reef ecosystems under rapid global change. Methods Sample collection and environmental parameters This study conducted field investigations and sampling of Acropora communities in the Meiji Reef (MJ) and the North Outer sandbar (BW) of Yongshu Reef in the South China Sea from 2023 to 2024 (Fig. 1 a). The topography of the MJ reef area is mainly characterized by cliffs, with a large water depth span and rapid currents. The coral reefs in this area are densely distributed and species-rich. In contrast, the topography in the BW reef area is relatively gentle, with reef-building corals distributed sparsely. The number of species (especially the genus Acropora ) was significantly lower than that in the MJ reef area. During sample collection, detailed photographs of each individual coral colony were taken for record, and fragments approximately 5–10 cm length were cut off. After the samples were removed from the water, they were immediately placed in liquid nitrogen for rapid freezing, then transferred to the laboratory and stored for a long time in a -80°C ultra-low temperature refrigerator until DNA extraction was performed. Classification of coral species was performed based on macroscopic morphology and skeletal microstructure, utilizing established taxonomic guides like Corals of the World and regional records to determine specific characteristics such as cup spacing (Zou R 2001 ; Veron et al. 2016 ; Huang H 2021 ; Zhang et al. 2025 ). Seawater samples (10 L each) were collected simultaneously at each sampling point to obtain environmental microorganisms and determine physicochemical parameters. Environmental factor data were compiled by combining in situ measurements with data extracted from the Copernicus Marine MyOcean Viewer ( https://data.marine.copernicus.eu/viewer ; spatial resolution 1/12°; Table S1 , S2). The resulting environmental variables included seawater temperature (Temp), salinity (Sal), pH, dissolved oxygen (DO), nitrate concentration (NO₃), dissolved inorganic carbon (DIC), phosphate concentration (PO₄), silicate concentration (SI), chlorophyll-a levels (Chla), seawater velocity (SWV), and net primary production (NPPV). Reef-building coral cover was quantified by frame-by-frame analysis of coral transect videos recorded at each sampling point. DNA extraction, 16S rRNA gene sequencing, and processing Approximately 2 g of coral samples were placed in a 5 mL centrifuge tube. Then, 2 mL of DNA lysis buffer and 40 µL of Proteinase K were added, and the mixture was digested in a metal heating block at 56°C for 6 h. Subsequently, the coral bone fragments were removed. The lysate was centrifuged at 10,000 rpm for 8 min. The supernatant was discarded, and the microbial cell pellet was retained. An appropriate amount of glass beads (0.5 mm in diameter) and 500 µL DNA lysis buffer were added, followed by homogenization at 4,500 rpm for 45 s using a benchtop tissue homogenizer. After the samples were kept on ice until bubbles dissipated, genomic DNA was extracted using the CTAB method and then purified using the DNA Clean & Concentrator Kit (Zymo Research, USA). DNA concentration and purity were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA), and samples meeting quality criteria were stored at -20°C until future use. Seawater samples (10 L) were filtered using a peristaltic pump through 3µm prefilter followed by a 0.2µm membrane filter to collect microorganisms. The 0.2 µm filter was retained and aseptically cut into small pieces, mixed with DNA lysis buffer and 0.5 mm glass beads, and homogenized. Subsequent procedures, including DNA extraction, purification, and DNA quantification and purity assessment, were performed as described for the coral samples. Using genomic DNA as the template, the V3-V4 region of the bacterial 16S rRNA gene was amplified using the primer pair 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'), as previously described (Mori et al. 2014 ; Xu et al. 2016 ). PCR was performed on an ABI GeneAmp 9700 thermal cycler. The resulting amplicons were subjected to paired-end sequencing on the Illumina MiSeq platform. The raw sequencing reads were processed using QIIME 2 2021.2 process (Bolyen et al. 2019 ). The cutadapt (Martin 2011 ) plugin was used to remove adapter and primer sequences from the raw reads. Based on the quality profiles, sequences were denoised and chimeras were removed using the DADA2 (via q2-dada2), and amplicon subsequence variants (ASVs) and their feature (abundance) table were generated (Callahan et al. 2016 ). Taxonomic assignment was performed against the SILVA database (Release 138) using the QIIME 2 feature-classifier plugin. Sequences assigned to chloroplasts and mitochondria were removed using the q2-taxa plugin (Quast et al. 2012 ; Yilmaz et al. 2014 ; Bokulich et al. 2018 ). Archaeal reads accounted for < 1% of the total sequences and were therefore excluded from downstream analyses. The ASV table was rarefied to 5,000 reads per sample to microbial community composition and diversity. Microbial diversity and community composition statistical Alpha diversity indices (Chao1, Shannon, and Simpson) of bacterial communities in the two reef areas, including Acropora corals (MJ-C: MJ Reef corals; BW-C: BW Reef corals) and surrounding seawater (MJ-S: MJ reef seawater; BW-S: BW reef seawater), were calculated using the R package MicrobiotaProcess (Xu et al. 2023 ). Pairwise differences among the four groups were tested using the Wilcoxon rank-sum test, and the p-values were corrected for multiple comparisons via the Holm method. β diversity was calculated using the rarefied ASV table to generate the Bray-Curtis dissimilarity index, which was used to quantify differences in community structure (Bray and Curtis 1957 ). Principal coordinate analysis (PCoA) was used to reduce the dimensionality of the Bray-Curtis dissimilarity matrix, and the distribution patterns of microbial communities in different groups were visualized in two-dimensional space. The statistical significance of the differences in community composition among the different groups was analyzed by permutational multivariate analysis of variance (PERMANOVA) using the adonis2 function in the vegan (v2.7-2) package, with 999 permutations. Data visualization was performed using the ggplot2 package (Villanueva and Chen 2019 ). Venn diagrams were generated using the R package VennDiagram (Chen and Boutros 2011 ) to illustrate shared and unique ASVs among the four groups. Assembly, biomarkers, and functional potential Neutral community model (NCM) analysis was applied to ASV abundance data from coral samples to assess the relative importance of stochastic and deterministic processes in the assembly of the Acropora -associated bacterial community (Sloan et al. 2006 ). Nonlinear least-squares fitting was performed using minpack.lm package to estimate NCM parameters, including the mobility rate (m) and the coefficient of determination (R²), with R² used to evaluate model fit. The model predicts the theoretical relationship between the mean relative abundance of each ASV in the overall community and its occurrence frequency across samples. ASVs that fall outside the model’s 95% prediction interval were considered to deviate significantly from the neutral prediction and were therefore interpreted as being predominantly shaped by deterministic processes. Higher R² values indicate better agreement between the observed data and the neutral model, implying a stronger fit of the neutral (stochastic) model to the community assembly patterns. Key differentially abundant microbial taxa between reef areas were identified using LEfSe (Segata et al. 2011 ) based on genus-level relative abundance profiles. Taxa enriched in Acropora corals from MJ-C versus BW-C were defined by an LDA score > 3.5. To infer the potential ecological functions of the bacterial communities, PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) was used to predict functional profiles from the 16S rRNA gene ASV sequences and their relative abundances (Douglas et al. 2020 ). Based on the prediction results, the top 20 functional pathways by relative abundance were selected for subsequent analysis, and the Wilcoxon rank-sum test was used to compare the differences in the functional pathways of bacterial communities in reef areas of different geomorphic types. The obtained p-values were adjusted for multiple comparisons using the FDR method. Functional pathways with an FDR-adjusted p < 0.05 were considered statistically significant. Co-occurrence network and module-based functional analysis To explore potential associations within coral-associated bacterial communities, microbial co-occurrence networks (Newman 2003 ; Zhao et al. 2024 ) were constructed for MJ and BW reef areas. For each reef area, ASV data from all Acropora coral samples were collapsed to the genus level, and only genera present in at least 10% of the samples were retained for network construction. Subsequently, Spearman’s rank correlation among genera was calculated, and statistically significant strong associations (Spearman’s r > 0.6, p < 0.05) were retained as network edges. Network complexity and potential stability were evaluated by calculating key topological parameters, and community modules were identified using the fast modularity optimization algorithm. Network visualization was performed using Gephi (Bastian et al. 2009 ). Nodes were colored according to module affiliation, and only statistically significant co-occurrence associations were retained for visualization. To explore potential functional substructures, modules (sub-communities) containing more than 10 nodes were extracted for downstream analyses. Functional profiles for each module were predicted at KEGG Level 3. The top 20 predicted functions from each module were combined into a single feature set and visualized as a heatmap; Bray–Curtis dissimilarities were calculated based on column-wise standardized values, and hierarchical clustering was performed using average linkage. Environmental and host phylogenetic correlation analysis Associations between key microbial groups and environmental variables were evaluated using Mantel tests (Mantel 1967 ). Relative abundance data of key microbial groups identified by differential analysis were combined with standardized environmental data for subsequent analyses. Both datasets were averaged at the sampling site level prior to analysis. Mantel tests were performed using Spearman’s rank correlation between the distance matrices, with Euclidean distances calculated from standardized environmental variables. The correlation coefficient ( r ) was classified into grades of 0.1 and 0.5, and the p-values were divided into three grades ( p < 0.01, 0.01 ≤ p < 0.05, p ≥ 0.05). Spearman correlations among environmental factors were calculated, and combined visualization was conducted using the linkET software package. An Acropora coral phylogenetic tree was constructed to assess the role of host phylogeny in shaping the associated microbial community. The tree was built using genome-wide data from Gault et al. (Gault et al. 2021 ) corresponding to the Acropora species sampled in this study. Two distance matrices were constructed and compared. The Bray-Curtis dissimilarity matrix of the microbial community was calculated from ASV abundance data, and a host phylogenetic distance matrix was derived from the host phylogenetic tree. The correlation between the two matrices was evaluated using a Mantel test with Spearman’s rank correlation, and statistical significance was assessed by permutation testing (999 permutations), which was used to examine the relationship between host phylogenetic distance and microbial community composition. Results Different habitats shape Acropora coral assemblages The MJ and BW reef areas represented different habitats of Acropora corals, as indicated by a total of 12 environmental parameters and overall reef-building coral coverage (Fig. S1 ; Table S1 ,2). Compared to BW reef, the MJ reef exhibited significantly lower values of dissolved inorganic carbon (DIC), silicate concentration (SI), and total alkalinity (TA), but higher values of salinity (Sal), dissolved oxygen (DO), chlorophyll a (Chl a ), net primary production (NPPV), and seawater velocity (SWV) ( p 0.05). Twice SCUBA surveys conducted in May 2023 and 2024 revealed that the two reefs harbor distinct assemblages of Acropora species. The MJ reef hosts 45 species, almost twice as many as at the BW reef (23). Moreover, all the species recorded at BW reef are also found at MJ reef, with the exception of A. latistella and A. retusa (Fig. 1 b). Acropora species harbor distinct bacterial communities compared to the environment A total of 170 coral samples were collected, covering 47 Acropora species distributed at the two reefs, together with background seawater samples for comparison (MJ: 7; BW: 9). DNA was extracted and 16S rRNA genes were amplified, yielding 24,340 bacterial amplicon sequence variants (ASVs) from coral and seawater samples. Venn diagrams showed that the four groups of samples shared a limited number of ASVs (0.559%; Fig. 2 a). A substantial proportion of ASVs were exclusively found in coral samples from MJ (52.4%) and BW (25.3%), whereas much smaller fractions were obtained in seawater samples (5.26% from MJ and 5.41% from BW). Comparative analysis showed that coral samples host higher bacterial taxonomic diversity than surrounding seawater samples at both habitats. The MJ coral samples harbored a total of 56 bacterial phyla, covering 591 families and 1,431 genera, while the BW coral samples contained 54 bacterial phyla, comprising 525 families and 1,155 genera. In contrast, the MJ seawater samples harbored 27 bacterial phyla, containing 157 families and 276 genera, whereas BW seawater harbored 33 bacterial phyla, including 221 families and 376 genera (Supplementary material 2). At the phylum level, the coral-associated bacterial communities were dominated by Pseudomonadota (MJ-C: 76.32%, BW-C: 70.03%) and Bacteroidota (MJ-C: 2.74%, BW-C: 8.22%). In contrast, the seawater samples displayed different profiles, with Pseudomonadota (MJ-S: 50.27%, BW-S: 48.27%), Cyanobacteriota (MJ-S: 32.70%, BW-S: 28.92%), and Bacteroidota (MJ-S: 8.50%, BW-S: 14.09%) being the three most abundant phyla (Fig. 2 b). At the genus level, the coral microbiome was dominated by Endozoicomonas (MJ-C: 28.77%, BW-C: 20.82%), Vibrio (MJ-C: 8.21%, BW-C: 7.86%), and Pseudoalteromonas (MJ-C: 5.93%, BW-C: 5.72%; Fig. 2 c). The seawater samples harbored distinct bacterial community dominated by planktonic groups, including Synechococcus CC9902 (MJ-S: 23.03%, BW-S: 7.39%), Alteromonas (MJ-S: 4.35%, BW-S: 6.31%), and Prochlorococcus MIT9313 (MJ-S: 9.49%, BW-S: 21.47%). This clear partitioning of dominant taxa highlights the host-specific nature of the coral-associated bacterial community against the surrounding environment. Alpha -Diversity analyses further revealed significant differences in bacterial community composition between coral and seawater samples, as indicated by the Chao1 and Shannon indices, at MJ reef (MJ-C vs MJ-S; p < 0.05; Fig. 2 d) and BW reef (BW-C vs BW-S; p < 0.05). Instead, the Simpson index was only significant in the BW reef ( p < 0.05). Additionally, PCoA results showed a significant separation between coral-associated and seawater communities across the two coral reefs (PERMANOVA, p < 0.05; Fig. 2 e). Distinct bacterial communities facilitate functional niche partitioning in Acropora microbiome across habitats Neutral community model (NCM) analysis showed that the goodness-of-fit values for bacterial communities associated with Acropora corals across the two habitats were both below 0.5 (Fig. 3 a), indicating that their assembly was predominantly governed by deterministic processes (environmental filtration and host selection). No significant difference in α-diversity (Chao1, Shannon and Simpson) of the bacterial communities was observed between coral samples from the two reefs (Fig. 2 d). However, β-diversity analysis revealed a significant separation between MJ-C and BW-C in the overall structure of the microbiome community ( p < 0.05; Fig. 3 b). The results indicated that the diversity of the bacterial communities associated with Acropora corals between the two habitats were comparable at ASV level, whereas their community compositions were significant different. LEfSe analysis further identified the feature species responsible for the differential coral-associated bacterial communities between the two habitats. The bacterial community of MJ-C samples was predominantly defined by the phylum Pseudomonadota, specifically the family Endozoicomonadaceae. Typical coral symbionts Endozoicomonas and Parendozoicomonas were significantly enriched in MJ-C samples, establishing them as the core taxonomic signatures of this group. Additionally, the enrichment of Algicola further reinforced the dominance of Gammaproteobacteria in this group. In contrast, the BW-C group exhibited a more taxonomically diverse profile associated with complex ecological functions. Key biomarkers spanned multiple phyla, including Bacteroidota (e.g., Flavobacteriaceae and Cytophagales ) and Myxococcota, both of which are mainly involved in organic matter degradation. The BW-C group also enriched microbes affiliated with Alphaproteobacteria involved in nutrient cycling and biofilm formation (e.g., Paracoccaceae ), and other taxa associated with sedimentary or symbiotic environments, such as Candidatus Amoebophilus (Fig. 3 c; Fig. S2 ; Table S5). Environmental filtering overrides host phylogeny in driving the coral bacterial community assembly across habitats Habitat-specific differentiation in coral-associated bacterial communities was evident across reef areas, whereas no corresponding pattern with host phylogenetic relatedness was detected. Coral-associated bacterial communities exhibited a pronounced location-based clustering across the two reef areas regardless of host relatedness, implying that microbiome differentiation aligns more strongly with habitat differences (Fig. 4 a). Consistently, no significant correlation between host phylogenetic distance and microbial community dissimilarity was observed (Bray-Curtis distance) (Mantel test, r = -0.0056, p = 0.496; Fig. 4 b). This suggests that at the observed scale in this study, host kinship is not the primary determinant of microbiome structural differentiation. Among the significantly enriched symbiotic bacteria in the MJ-C group, Algicola exhibited significant positive correlations with pH (Mantel test based on Spearman's rank correlation: r = 0.672, p = 0.013 < 0.05; Fig. 4 c) and Temp ( r = 0.605, p = 0.014 < 0.05). It also displayed a marginal positive but less significant trend with DIC ( r = 0.302, p = 0.055), TA ( r = 0.277, p = 0.069), and SI ( r = 0.287, p = 0.061). Additionally, Endozoicomonas was significantly linked to phosphate levels ( r = 0.565, p = 0.027 < 0.05). In the BW-C group, P3OB-42 was significantly positively correlated with DO ( r = 0.469, p = 0.035 < 0.05) and TA ( r = 0.347, p = 0.045 < 0.05). Besides, Spirochaeta was significantly correlated with COV ( r = 0.564, p = 0.016 < 0.05), NO 3 ( r = 0.554, p = 0.026 < 0.05), and pH ( r = 0.444, p = 0.042 < 0.05). Additionally, Candidatus Amoebophilus and an unclassified member of the Paracoccaceae family showed moderate but non-significant correlation with these 13 factors. This location-based clustering pattern underscores the dominance of environmental filtering over host phylogeny in driving the divergent assembly and structural characteristics of coral microbiomes across the two reef areas. Interaction within the core bacterial community confers functional resilience across habitat heterogeneity Functional prediction using PICRUSt2 indicated that taxonomic divergence between the MJ-C and BW-C groups did not result in significant shifts in core metabolic potential. The enriched functional pathways were highly conserved at KEGG Level 2 between the two groups (Fig. 5 a; Table S6). Carbohydrate and amino acid metabolism, along with energy metabolism, cofactor/vitamin metabolism, and membrane transport were the most abundant functional categories. Although the MJ-C and BW-C groups have recruited taxonomically distinct bacterial communities, both exhibit a host-driven selection pattern that prioritizes functional compatibility with the coral hosts. These results indicated that a flexible coral-associated bacterial community across Acropora enabled them to maintain metabolic stability to adapt to diverse environmental conditions. Despite functional conservation, potential differences in habitat-dependent reorganization of microbial interactions were observed. Co-occurrence network analysis revealed distinct topological structures between the BW and MJ coral bacterial communities. The BW network (N: 321; E: 1163) had more nodes and edges than the MJ (N: 259, E:162) network. Furthermore, the average degree and network density of BW were higher, while the average path length was shorter, indicating a broader interaction among the BW bacterial communities and higher transmission efficiency. However, the MJ network demonstrated higher modularity (MC: 0.735; BC: 0.446) and a greater number of communities (MC: 171; BC: 99), suggesting a more compartmentalized organization with denser within-module associations and fewer between-module links (Fig. 5 b, b; Table S7). Modules with more than 10 member taxa were extracted, yielding five core submodules in both the BW and MJ groups. Functional profiles predicted using PICRUSt2 and compared at KEGG level 3 revealed that, despite marked differences in overall network topology and interaction patterns between the two reefs, the core modules exhibited a one-to-one functional correspondence (Fig. 5 c). Specifically, MJ5 and BW1 exhibited similar functional signatures, with enrichment in ABC transporters and glycine, serine, and threonine metabolism. MJ3 and BW5 were primarily enriched in valine, leucine, and isoleucine degradation, as well as the bacterial secretion system. MJ4 and BW3 were functionally comparable and enriched in flagellar assembly and biofilm formation. In addition, MJ2 resembled BW2 in predicted functions, and MJ1 resembled BW4, with both pairs showing enrichment in carbon fixation pathways and aminoacyl-tRNA biosynthesis. Further examination of these core modules indicated that functionally corresponding module pairs differed in their within-module interaction patterns and taxonomic composition (Fig. S3 , S4). These findings suggest that host-associated bacterial communities and interaction architectures can be reorganized into modules that perform analogous ecological functions, resulting in cross-site functional convergence. Discussion Through multi-scale comparisons across habitats and coral reefs, this study delineated the differentiation patterns and functional adaptations of bacterial communities associated with Acropora corals at the host-environment interface. The findings revealed that coral-associated bacterial communities were markedly distinct from the surrounding seawater, exhibiting pronounced host-specific differentiation. Despite substantial differences in the taxonomic composition of coral-associated bacterial communities across the two reef regions, their predicted core metabolic functional profiles were highly conserved. Notably, at the spatial and species distance scales examined, environmental filtering exerted a far stronger influence on the assembly of coral-associated communities than host phylogenetic signals, suggesting a flexible, adaptive response to local environmental pressures. Comprehensive evidence across phylum, genus, and ASV levels consistently revealed that coral-associated bacterial communities were strongly segregated from the surrounding seawater reservoir (Galand et al. 2023 ; Zhao et al. 2024 ). Large-scale surveys from the Tara Pacific expedition demonstrate that coral-associated microbiomes are significantly more diverse and host-specific than those in the open ocean (Galand et al. 2023 ). Usually, the seawater bacterial community was dominated by free-living photosynthetic cyanobacteria and planktonic heterotrophic bacteria, whereas coral samples were enriched in symbiotic lineages such as Endozoicomonas , Vibrio , and Pseudoalteromona (Kriefall et al. 2022 ; Mohamed et al. 2023 ; Gantt et al. 2024 ). In this work, significant differences in the Chao1 and Shannon indices between the coral and seawater groups were observed (Fig. 2 c-f), indicating that corals do not merely passively trap water-borne microbes but actively filter and reconfigure background microbial communities to suit their specific habitats (Ziegler et al. 2019 ). This deterministic assembly process was likely driven by the unique physiological conditions within the coral holobiont, including the nutrient-rich surface mucus layer and the controlled immune landscape (Mohamed et al. 2023 ). The dominance of Endozoicomonas in coral groups in this work was particularly noteworthy, as recent genomic evidence suggests these symbionts are primed for a symbiotic lifestyle through the modulation of host immunity and the provisioning of essential metabolites (Ding et al. 2016 ; Pogoreutz et al. 2022 ). Our observation that seawater bacterial communities exhibited greater spatial variability, whereas coral groups remained relatively stable across different reef sites further supported the host-selection hypothesis, wherein the host’s internal microenvironments exert stronger constraints on community structure than external environmental fluctuations (Pollock et al. 2018 ; Zhao et al. 2024 ). The host selection mechanism established a fundamental microbial basis for nutrient cycling and environmental resilience of the holobiont (Zhang et al. 2021b ), while simultaneously raising the question of how coral-associated bacteria adjust their community composition across contrasting habitats to sustain holobiont function. In the two habitats, Acropora -associated bacterial communities displayed pronounced taxonomic divergence, with MJ enriched in core Gammaproteobacteria (e.g., Endozoicomonas and Parendozoicomonas ), whereas BW harbored diverse lineages (e.g., Bacteroidota and Myxococcota) (Fig. 3 c). In line with this strong compositional turnover, the neutral community model showed limited explanatory power for both habitats (MJ: 0.298; BW: −0.031; Fig. 2 a), suggesting that community assembly deviated from neutral expectations and that non-neutral processes likely contributed to the observed patterns. Despite their taxonomic differences, functional profiling indicated substantial overlap in predicted functional potentials, consistent with functional redundancy and the maintenance of core metabolic capacity via taxonomic replacement (Hernandez-Agreda et al. 2018 ; Ziegler et al. 2019 ; Fiore et al. 2020 ). Such component replacement allows the corals to fine-tune their microbial partners to local environmental pressures without compromising core physiological homeostasis (Zhang et al. 2021b ; Montaño-Salazar et al. 2023 ). Co-occurrence network analysis further revealed that such functional consistency may be supported by reef-specific interaction architectures, whereby distinct taxa are reorganized into functionally corresponding network modules through different within-community association patterns (Fig. 4 a-c). Together, these results support the view that the coral-associated bacterial communities are both functionally stable and compositionally flexible (Ziegler et al. 2019 ; Zhu et al. 2022 ). By prioritizing the conservation of key functional traits over specific taxonomic identities, Acropora corals employ taxonomic plasticity and bacterial network reorganization as adaptive strategies to different habitats (Camp et al. 2020 ; Strudwick et al. 2024 ; Voolstra et al. 2024 ). This deterministic recruitment of functionally redundant partners ensures that the coral holobionts can navigate environmental heterogeneity while preserving metabolic stability, a strategy that may be crucial for their resilience amid escalating anthropogenic pressures (Cárdenas et al. 2022 ; Zhu et al. 2022 ; Montaño-Salazar et al. 2023 ). Broad-scale meta-analyses across diverse coral families reveal phylosymbiosis evolution between coral hosts and their symbiotic bacterial communities (Pollock et al. 2018 ; Ziegler et al. 2019 ; Voolstra et al. 2021 , 2024 ). Acropora was selected as a representative model because of its ecological prominence as a reef-building genus, which provides a relevant framework for testing microbiome responses to environmental heterogeneity. In this study, conducted at the genus level (46 Acropora species), the design minimizes major host-phylogenetic confounding that often complicates cross-genus or cross-family comparisons, while also improving inference robustness by avoiding reliance on any single host species (Dunphy et al. 2019 ). This framework is well-suited to evaluate whether local environmental gradients can impose sufficiently strong selective pressures to restructure bacterial communities even when host genetic differences are relatively minor within a genus. Such location-driven bacterial communities restructuring aligns with recent evidence from Acropora cervicornis and Millepora spp. clones, which suggests that reef environments can reshape microbial partners even within closely related or genetically identical host populations (Miller et al. 2020 ; Dubé et al. 2021 ). The observed decoupling of taxonomic flexibility and functional conservation in this study reinforces the paradigm that simplicity exists within a diverse microbial biosphere (Ziegler et al. 2019 ). By leveraging functional redundancy through deterministic bacterial assembly, Acropora corals effectively balance differential habitats adaptation with holobiont metabolic stability, a strategy central to their persistence in a rapidly changing ocean. By utilizing Acropora as a representative model, our findings provide a broader conceptual framework for understanding the resilience and conservation of scleractinian corals in an era of rapid environmental change. We demonstrate that the coral holobiont’s capacity to navigate environmental heterogeneity is underpinned by a flexible yet stable bacterial assemblage strategy that maintains core metabolic functions by recruiting taxonomically diverse but functionally redundant microbial lineages (Ziegler et al. 2019 ; Voolstra et al. 2024 ). This suggests that, across a wide range of branching corals, the microbiome acts as a dynamic buffer, enabling refined physiological adjustments to local stressors. From a conservation perspective, this study shifts the focus from merely preserving host genetic diversity to maintaining the environmental–microbial synergy that facilitates adaptive transition (Hu et al. 2021 ; Kriefall et al. 2022 ; Mohamed et al. 2023 ). Given that environmental filtering, rather than host phylogeny, is the primary driver of community assembly at these scales, management strategies must prioritize protecting water quality and habitat heterogeneity (Gantt et al. 2024 ; Zhao et al. 2024 ). This ensures that the basic bacterial library remains intact, allowing hosts to continue filtering and reconfiguring specific symbiotic partners necessary for their adaptation and survival. Declarations Ethics approval and consent to participate Coral sampling was carried out following approval from the Animal Experiment Ethics Committee in the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), China. Consent for publication Not applicable. Funding This work was supported by the Ministry of Science and Technology of China (2021YFF0502800), the National Natural Science Foundation of China (NSFC; grant nos. 42476109, 42276163 and 32370462). Availability of data and materials All data supporting the findings of this study are provided in the Supplementary Information and Supplementary Datasets. Specifically, Supplementary Dataset 1 presents a microbial species abundance table (Level 1 to Level 7) annotated based on 16S rRNA gene sequences.; Supplementary Dataset 2 contains the phylogenetic tree of 46 Acropora coral species. The raw 16S rRNA gene sequencing data have been deposited in the Genome Sequence Archive (GSA) in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: XXXXX) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/s/4k8zk5Ac. Competing interests The authors declare no competing interests. Authors' contributions Z.Z. and S.H. conceived and supervised the project. W.Y., K.G., J.Z., Y.H., H.L.,and Z.Z. collected the samples. L.L., F.C. and J.Z. performed the experiments. L.L. and F.C. did the statistical analyses. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8795967","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600879710,"identity":"1e892b6c-6b9b-4378-863a-2fe82a02e59c","order_by":0,"name":"LiJing Li","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"LiJing","middleName":"","lastName":"Li","suffix":""},{"id":600879713,"identity":"c30b3dcd-2e45-4632-bfe1-f239df96f6e2","order_by":1,"name":"Wen Yu","email":"","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Yu","suffix":""},{"id":600879715,"identity":"d7a89569-4c73-487c-bbbc-6e13a9c9af11","order_by":2,"name":"Fengtong Chang","email":"","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":false,"prefix":"","firstName":"Fengtong","middleName":"","lastName":"Chang","suffix":""},{"id":600879716,"identity":"fb8631d1-2fd4-4509-9806-cd45a66fb446","order_by":3,"name":"Kuo Gao","email":"","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":false,"prefix":"","firstName":"Kuo","middleName":"","lastName":"Gao","suffix":""},{"id":600879717,"identity":"58a0a209-80d7-4861-bd0f-b656bb44dcf4","order_by":4,"name":"Jingjing Zhang","email":"","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Zhang","suffix":""},{"id":600879719,"identity":"6d22f20c-5eae-4cda-904e-b28b49a1dea8","order_by":5,"name":"Junjie Zhou","email":"","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Zhou","suffix":""},{"id":600879720,"identity":"9794515b-a30e-4bfe-9f8b-3057898f7049","order_by":6,"name":"Yisi Hu","email":"","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":false,"prefix":"","firstName":"Yisi","middleName":"","lastName":"Hu","suffix":""},{"id":600879722,"identity":"90f7c28a-64bd-45a8-93bb-2ec8a7c46018","order_by":7,"name":"Hao Luo","email":"","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Luo","suffix":""},{"id":600879725,"identity":"672853b7-8295-4761-ab5e-2cf44ed08dd5","order_by":8,"name":"Shengwei Hou","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shengwei","middleName":"","lastName":"Hou","suffix":""},{"id":600879728,"identity":"7b865671-f4f8-425c-a86c-bc7c0d056fa7","order_by":9,"name":"Zhiwei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACCTB5gIeBgbHxAQODBYMBKVqaDUBcorWACDYJorTIz25+9vDLnzsyBseb2yq/5kjImzMwP/zAUHMHpxbGOcfMjWV4nvEYnDnYdlt2m4ThzgY2YwmGY89wamGWSDCTlpA4zGN2I7HttuQ2CcYNBxjMGBgbDuPUwiaR/k1awgCo5f7DtmKgFvsNB9i/4dXCI5FjJvkhAWQLYxvjx20SiRsO8OC3RUIip0ya4cBhHvszic3SjNskknc28xRLJBzDrUV+Rvo2yR9/DttLth9/+PHnNhvb7eztGz98qMGtBRwEPCgMZiBOwKsBGNA/0BmjYBSMglEwCpABABoCVY6pFP0qAAAAAElFTkSuQmCC","orcid":"","institution":"Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)","correspondingAuthor":true,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-05 10:55:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8795967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8795967/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104016042,"identity":"356cc562-b2df-426c-8caa-6684eb10b4ad","added_by":"auto","created_at":"2026-03-05 17:02:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362701,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the sample collection for \u003cem\u003eAcropora\u003c/em\u003e corals at MJ Reef and BW Reef in the South China Sea. (a) Spatial distribution of the sampling sites for \u003cem\u003eAcropora\u003c/em\u003e corals; (b) Stacked bar chart showing the species composition of \u003cem\u003eAcropora\u003c/em\u003esamples across the two reef areas.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/14265b0a212d5de0df136958.png"},{"id":104402172,"identity":"3973fc33-fed3-472c-9c6c-07d2738fd8bc","added_by":"auto","created_at":"2026-03-11 12:14:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":284280,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-scale comparative analysis of bacterial community composition and diversity between \u003cem\u003eAcropora\u003c/em\u003ecorals and surrounding seawater. (a) Venn diagram showing the distribution of shared and unique ASVs among the four groups, highlighting highly host-specific taxonomic assemblages and limited overlap with the surrounding water column. Taxonomic composition of the top 20 bacterial taxa at the phylum (b) and genus (c) levels across MJ coral (MJ-C), BW coral (BW-C), MJ seawater (MJ-S), and BW seawater (BW-S) groups. (d) \u003cem\u003eAlpha\u003c/em\u003e diversity indices (Chao1, Shannon, and Simpson) comparing coral and seawater microbiomes in the MJ reef and BW reef areas. Pairwise differences were tested using two-sided Wilcoxon rank-sum tests, with p-values adjusted for multiple comparisons using the Holm method. Different letters above the boxplots indicate groups that differ significantly based on Holm-adjusted pairwise comparisons (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). (e) Beta diversity analysis based on Bray-Curtis dissimilarities visualized via PCoA for MJ reef and BW reef samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/abdbadf6ee1ef7ef1ce7bea6.png"},{"id":104402262,"identity":"d9e45736-d789-410a-a256-7f7cb6bebb0e","added_by":"auto","created_at":"2026-03-11 12:14:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":287500,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity assembly mechanisms, diversity, and functional potential of \u003cem\u003eAcropora\u003c/em\u003e coral bacteria across different reef areas. (a) Neutral community model (NCM) fit for MJ-C and BW-C microbial communities. (b) PCoA plot based on Bray-Curtis dissimilarities revealing significant taxonomic segregation between the two reef areas (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). (c) LEfSe analysis (LDA \u0026gt; 3.5, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05) identifying key biomarker taxa significantly enriched in MJ-C (pink) and BW-C (blue).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/a5aa538a255bc6a78c37f312.png"},{"id":104016044,"identity":"a44d95de-bcca-478a-aaee-cd63632d1d64","added_by":"auto","created_at":"2026-03-05 17:02:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355806,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental and host drivers of microbial differentiation in \u003cem\u003eAcropora\u003c/em\u003e corals across different reef regions. (a) Integration of host phylogeny and microbial composition across reef sites. Inner and outer rings display relative abundances of the top 10 microbial genera in MJ and BW reef samples, respectively, arranged according to host phylogenetic trees. (b) Mantel test illustrating the relationship between host phylogenetic distance and microbial community dissimilarity (Bray-Curtis distance). (c) Heatmap showing Spearman correlations between differentially enriched microbial genera (identified by LEfSe) and key environmental factors. Temp, temperature; DO, dissolved oxygen; TA, total alkalinity; NO₃, nitrate concentration; PO₄, phosphate concentration; SI, silicate concentration; DIC, dissolved inorganic carbon; NPPV, net primary productivity; Chla, chlorophyll-a; COV, coral cover; SWV, water velocity; Sal, salinity; pH, acidity.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/73d9f7993c8ba9f1ee426cae.png"},{"id":104016049,"identity":"6c87e3a0-18a0-4c9d-bde7-ee311855eae2","added_by":"auto","created_at":"2026-03-05 17:02:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":314069,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork-based variation in \u003cem\u003eAcropora\u003c/em\u003ecoral bacterial structure and functional potential across reef areas: (a) Bubble plot displaying the relative abundance of the top 20 KEGG Level 2 functional pathways predicted by PICRUSt2. (b) Co-occurrence network of the staghorn coral associated bacteria at the MJ reef (up) BW reef (down). Nodes represent bacterial taxa, while edges denote significant correlations. Colored nodes indicate modules with more than 10 members in the microbial abundance network, whereas gray nodes represent modules with fewer than 10 members. Additionally, modules sharing the same color in MJ and BW indicate functional similarity. (c) Functional profiles of the 10 core communities extracted from MJ-C and BW-C networks, showing the merged set of top 20 KEGG Level 3 pathways (predicted by PICRUSt2) for each community and highlighting shared core metabolic functions as well as module-specific functional enrichments. Modules 1–5 of MJ are numbered MJ1–MJ5, while modules 1–5 of BW are numbered BW1–BW5.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/a7bd348abb7b6de747808633.png"},{"id":106961146,"identity":"eb722cf8-11cb-4c69-9af7-274c90d273fb","added_by":"auto","created_at":"2026-04-15 09:24:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2243731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/85973ba2-9e52-4a1c-bbfd-79a874fb7e96.pdf"},{"id":104402436,"identity":"8b361de6-e873-40a2-af7c-462ec6bfe67b","added_by":"auto","created_at":"2026-03-11 12:15:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1259724,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/7e51e0df64c7070934125835.docx"},{"id":104016047,"identity":"0994563d-e5ae-4c16-b127-4716142fc882","added_by":"auto","created_at":"2026-03-05 17:02:13","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":128786,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/5a7f7def8ac142748b567ec6.xlsx"},{"id":104016051,"identity":"ae41e917-ac88-4900-9697-31ccdd03fb21","added_by":"auto","created_at":"2026-03-05 17:02:14","extension":"qzv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16038100,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataset1.qzv","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/0a66ef826d4ac863a36920d5.qzv"},{"id":105751777,"identity":"00c8ab74-9888-4d9f-a81c-893e65c76b27","added_by":"auto","created_at":"2026-03-30 15:43:38","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2953,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataset2.txt","url":"https://assets-eu.researchsquare.com/files/rs-8795967/v1/604e68a0dec4204058c5db77.txt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental heterogeneity overrides host phylogenetic distance in shaping the microbiome of Acropora corals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoral reef ecosystems are widely recognized as an important natural system for investigating host-associated microbiomes in natural environments (Pollock et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; van Oppen and Blackall \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this context, reef-building corals function as holobionts, and their adaptability depends not only on the host\u0026rsquo;s physiological state but also on a symbiotic consortium comprising zooxanthellae, bacteria, archaea, viruses, and other microorganisms (Bourne et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; St\u0026eacute;venne et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Voolstra et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within this functional unit, bacterial communities play critical roles in the host\u0026rsquo;s health and ecological adaptation by mediating key nutrient cycling processes (van Oppen and Blackall \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Voolstra et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), metabolic complementarity (Kullapanich et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Messer et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), pathogen antagonism (Mascuch et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Azizah et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), immune modulation (Palmer et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Barno et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pogoreutz et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and signal regulation (Pogoreutz et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Messer et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This highly coordinated symbiotic relationship endows corals with a microbe-mediated buffer, facilitating adaptation to diverse environmental conditions. Yet, how corals acquire their microbial partners from the environment, and whether closely related corals share a conserved core functional microbiome, remain unresolved questions in coral microbiome studies.\u003c/p\u003e \u003cp\u003eCorals harbor highly specific core microbial assemblages that are distinct from those of the surrounding seawater (Glasl et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Weber et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Previous studies have suggested that either environmental filtering (Epstein et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; van Oppen and Blackall \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pei et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or host-specific filtering (Pollock et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Glasl et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Ricci et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) is the main driver of microbial community assembly in the coral microbiome. Proponents of environmental filtering argue that external factors, including depth (Pattarach et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vohsen and Herrera \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and water quality (Glasl et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Nelson et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), are the primary drivers of microbial community assembly (Gantt et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, a recent study suggests that reef environments can substantially reshape microbial partners within closely related or genetically identical coral populations, highlighting the remarkable plasticity of the symbiotic microbiome in response to localized environmental fluctuations (Kriefall et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, other studies emphasize the dominant role of host-driven selection in shaping microbial assemblages, demonstrating that corals can maintain stable core microbiomes across vast geographic distances (Epstein et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Galand et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The concept of phylosymbiosis suggests that host-specific physiological traits and phylogeny impose stringent constraints on microbial recruitment, thereby buffering the holobiont against environmental fluctuations (Pollock et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The tension between environmentally driven adaptive plasticity along ecological gradients and structural consistency imposed by host evolutionary history limits our comprehensive understanding of how coral holobionts achieve functional stability and metabolic equilibrium within complex and dynamic reef landscapes (Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStaghorn corals (\u003cem\u003eAcropora\u003c/em\u003e spp.), renowned for their rapid growth and exceptional ecological resilience, contribute to maintaining the structural complexity of coral reefs (Siqueira et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Over 140 \u003cem\u003eAcropora\u003c/em\u003e species have been described globally (Rassmussen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hoeksema et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), with more than 60 documented in the South China Sea, where many species show sympatric distributions but exhibit pronounced differences in community composition across contrasting habitats (Huang et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For instance, a previous study found that habitats with higher spatial complexity and steeper depth gradients typically support higher \u003cem\u003eAcropora\u003c/em\u003e species richness and more heterogeneous coral communities (Sannassy Pilly et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Meiji (hereafter MJ) and Beiwai (hereafter BW) coral reefs are located at the same latitude yet exhibit sharply divergent geomorphology. BW reef is relatively flat and shallow, with depths ranging from 5 to 15 m, whereas MJ reef features a much steeper slope, extending from 5 to 50 m. Previous studies have revealed that both reefs host multiple \u003cem\u003eAcropora\u003c/em\u003e species (Zhao et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which offer a natural contrast for investigating mechanisms underlying symbiont microbiome assembly at the intrageneric scale.\u003c/p\u003e \u003cp\u003eIn this study, we systematically investigated the characteristics of the symbiotic bacterial communities of \u003cem\u003eAcropora\u003c/em\u003e corals across the heterogeneous benthic habitats of MJ and BW reefs in the South China Sea. Multiscale community analysis was conducted to unravel the relative roles of stochastic and deterministic processes in shaping the host-associated bacterial communities, and to clarify the relative influences of local environmental factors and host phylogenetic signals. Integrated analyses enabled us to assess the extent of symbiotic bacterial plasticity and its role in maintaining stable core metabolism and facilitating holobiont adaptation to diverse habitats. Our findings advance the theoretical understanding of how bacterial communities contribute to the adaptive resilience of \u003cem\u003eAcropora\u003c/em\u003e and provide insights for the conservation and restoration of coral reef ecosystems under rapid global change.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and environmental parameters\u003c/h2\u003e \u003cp\u003eThis study conducted field investigations and sampling of \u003cem\u003eAcropora\u003c/em\u003e communities in the Meiji Reef (MJ) and the North Outer sandbar (BW) of Yongshu Reef in the South China Sea from 2023 to 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The topography of the MJ reef area is mainly characterized by cliffs, with a large water depth span and rapid currents. The coral reefs in this area are densely distributed and species-rich. In contrast, the topography in the BW reef area is relatively gentle, with reef-building corals distributed sparsely. The number of species (especially the genus \u003cem\u003eAcropora\u003c/em\u003e) was significantly lower than that in the MJ reef area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring sample collection, detailed photographs of each individual coral colony were taken for record, and fragments approximately 5\u0026ndash;10 cm length were cut off. After the samples were removed from the water, they were immediately placed in liquid nitrogen for rapid freezing, then transferred to the laboratory and stored for a long time in a -80\u0026deg;C ultra-low temperature refrigerator until DNA extraction was performed. Classification of coral species was performed based on macroscopic morphology and skeletal microstructure, utilizing established taxonomic guides like \u003cem\u003eCorals of the World\u003c/em\u003e and regional records to determine specific characteristics such as cup spacing (Zou R \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Veron et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Huang H \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeawater samples (10 L each) were collected simultaneously at each sampling point to obtain environmental microorganisms and determine physicochemical parameters. Environmental factor data were compiled by combining in situ measurements with data extracted from the Copernicus Marine MyOcean Viewer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.marine.copernicus.eu/viewer\u003c/span\u003e\u003cspan address=\"https://data.marine.copernicus.eu/viewer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; spatial resolution 1/12\u0026deg;; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2). The resulting environmental variables included seawater temperature (Temp), salinity (Sal), pH, dissolved oxygen (DO), nitrate concentration (NO₃), dissolved inorganic carbon (DIC), phosphate concentration (PO₄), silicate concentration (SI), chlorophyll-a levels (Chla), seawater velocity (SWV), and net primary production (NPPV). Reef-building coral cover was quantified by frame-by-frame analysis of coral transect videos recorded at each sampling point.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction, 16S rRNA gene sequencing, and processing\u003c/h3\u003e\n\u003cp\u003eApproximately 2 g of coral samples were placed in a 5 mL centrifuge tube. Then, 2 mL of DNA lysis buffer and 40 \u0026micro;L of Proteinase K were added, and the mixture was digested in a metal heating block at 56\u0026deg;C for 6 h. Subsequently, the coral bone fragments were removed. The lysate was centrifuged at 10,000 rpm for 8 min. The supernatant was discarded, and the microbial cell pellet was retained. An appropriate amount of glass beads (0.5 mm in diameter) and 500 \u0026micro;L DNA lysis buffer were added, followed by homogenization at 4,500 rpm for 45 s using a benchtop tissue homogenizer. After the samples were kept on ice until bubbles dissipated, genomic DNA was extracted using the CTAB method and then purified using the DNA Clean \u0026amp; Concentrator Kit (Zymo Research, USA). DNA concentration and purity were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA), and samples meeting quality criteria were stored at -20\u0026deg;C until future use. Seawater samples (10 L) were filtered using a peristaltic pump through 3\u0026micro;m prefilter followed by a 0.2\u0026micro;m membrane filter to collect microorganisms. The 0.2 \u0026micro;m filter was retained and aseptically cut into small pieces, mixed with DNA lysis buffer and 0.5 mm glass beads, and homogenized. Subsequent procedures, including DNA extraction, purification, and DNA quantification and purity assessment, were performed as described for the coral samples.\u003c/p\u003e \u003cp\u003eUsing genomic DNA as the template, the V3-V4 region of the bacterial 16S rRNA gene was amplified using the primer pair 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'), as previously described (Mori et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). PCR was performed on an ABI GeneAmp 9700 thermal cycler. The resulting amplicons were subjected to paired-end sequencing on the Illumina MiSeq platform. The raw sequencing reads were processed using QIIME 2 2021.2 process (Bolyen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The cutadapt (Martin \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) plugin was used to remove adapter and primer sequences from the raw reads. Based on the quality profiles, sequences were denoised and chimeras were removed using the DADA2 (via q2-dada2), and amplicon subsequence variants (ASVs) and their feature (abundance) table were generated (Callahan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Taxonomic assignment was performed against the SILVA database (Release 138) using the QIIME 2 feature-classifier plugin. Sequences assigned to chloroplasts and mitochondria were removed using the q2-taxa plugin (Quast et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yilmaz et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bokulich et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Archaeal reads accounted for \u0026lt;\u0026thinsp;1% of the total sequences and were therefore excluded from downstream analyses. The ASV table was rarefied to 5,000 reads per sample to microbial community composition and diversity.\u003c/p\u003e\n\u003ch3\u003eMicrobial diversity and community composition statistical\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eAlpha\u003c/em\u003e diversity indices (Chao1, Shannon, and Simpson) of bacterial communities in the two reef areas, including \u003cem\u003eAcropora\u003c/em\u003e corals (MJ-C: MJ Reef corals; BW-C: BW Reef corals) and surrounding seawater (MJ-S: MJ reef seawater; BW-S: BW reef seawater), were calculated using the R package MicrobiotaProcess (Xu et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pairwise differences among the four groups were tested using the Wilcoxon rank-sum test, and the p-values were corrected for multiple comparisons via the Holm method. β diversity was calculated using the rarefied ASV table to generate the Bray-Curtis dissimilarity index, which was used to quantify differences in community structure (Bray and Curtis \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). Principal coordinate analysis (PCoA) was used to reduce the dimensionality of the Bray-Curtis dissimilarity matrix, and the distribution patterns of microbial communities in different groups were visualized in two-dimensional space. The statistical significance of the differences in community composition among the different groups was analyzed by permutational multivariate analysis of variance (PERMANOVA) using the adonis2 function in the vegan (v2.7-2) package, with 999 permutations. Data visualization was performed using the ggplot2 package (Villanueva and Chen \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Venn diagrams were generated using the R package VennDiagram (Chen and Boutros \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) to illustrate shared and unique ASVs among the four groups.\u003c/p\u003e\n\u003ch3\u003eAssembly, biomarkers, and functional potential\u003c/h3\u003e\n\u003cp\u003eNeutral community model (NCM) analysis was applied to ASV abundance data from coral samples to assess the relative importance of stochastic and deterministic processes in the assembly of the \u003cem\u003eAcropora\u003c/em\u003e-associated bacterial community (Sloan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Nonlinear least-squares fitting was performed using minpack.lm package to estimate NCM parameters, including the mobility rate (m) and the coefficient of determination (R\u0026sup2;), with R\u0026sup2; used to evaluate model fit. The model predicts the theoretical relationship between the mean relative abundance of each ASV in the overall community and its occurrence frequency across samples. ASVs that fall outside the model\u0026rsquo;s 95% prediction interval were considered to deviate significantly from the neutral prediction and were therefore interpreted as being predominantly shaped by deterministic processes. Higher R\u0026sup2; values indicate better agreement between the observed data and the neutral model, implying a stronger fit of the neutral (stochastic) model to the community assembly patterns.\u003c/p\u003e \u003cp\u003eKey differentially abundant microbial taxa between reef areas were identified using LEfSe (Segata et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) based on genus-level relative abundance profiles. Taxa enriched in \u003cem\u003eAcropora\u003c/em\u003e corals from MJ-C versus BW-C were defined by an LDA score\u0026thinsp;\u0026gt;\u0026thinsp;3.5. To infer the potential ecological functions of the bacterial communities, PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) was used to predict functional profiles from the 16S rRNA gene ASV sequences and their relative abundances (Douglas et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on the prediction results, the top 20 functional pathways by relative abundance were selected for subsequent analysis, and the Wilcoxon rank-sum test was used to compare the differences in the functional pathways of bacterial communities in reef areas of different geomorphic types. The obtained p-values were adjusted for multiple comparisons using the FDR method. Functional pathways with an FDR-adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eCo-occurrence network and module-based functional analysis\u003c/h3\u003e\n\u003cp\u003eTo explore potential associations within coral-associated bacterial communities, microbial co-occurrence networks (Newman \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were constructed for MJ and BW reef areas. For each reef area, ASV data from all \u003cem\u003eAcropora\u003c/em\u003e coral samples were collapsed to the genus level, and only genera present in at least 10% of the samples were retained for network construction. Subsequently, Spearman\u0026rsquo;s rank correlation among genera was calculated, and statistically significant strong associations (Spearman\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained as network edges. Network complexity and potential stability were evaluated by calculating key topological parameters, and community modules were identified using the fast modularity optimization algorithm. Network visualization was performed using Gephi (Bastian et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Nodes were colored according to module affiliation, and only statistically significant co-occurrence associations were retained for visualization. To explore potential functional substructures, modules (sub-communities) containing more than 10 nodes were extracted for downstream analyses. Functional profiles for each module were predicted at KEGG Level 3. The top 20 predicted functions from each module were combined into a single feature set and visualized as a heatmap; Bray\u0026ndash;Curtis dissimilarities were calculated based on column-wise standardized values, and hierarchical clustering was performed using average linkage.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental and host phylogenetic correlation analysis\u003c/h2\u003e \u003cp\u003eAssociations between key microbial groups and environmental variables were evaluated using Mantel tests (Mantel \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). Relative abundance data of key microbial groups identified by differential analysis were combined with standardized environmental data for subsequent analyses. Both datasets were averaged at the sampling site level prior to analysis. Mantel tests were performed using Spearman\u0026rsquo;s rank correlation between the distance matrices, with Euclidean distances calculated from standardized environmental variables. The correlation coefficient (\u003cem\u003er\u003c/em\u003e) was classified into grades of 0.1 and 0.5, and the p-values were divided into three grades (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, 0.01\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05). Spearman correlations among environmental factors were calculated, and combined visualization was conducted using the linkET software package.\u003c/p\u003e \u003cp\u003eAn \u003cem\u003eAcropora\u003c/em\u003e coral phylogenetic tree was constructed to assess the role of host phylogeny in shaping the associated microbial community. The tree was built using genome-wide data from Gault et al. (Gault et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) corresponding to the \u003cem\u003eAcropora\u003c/em\u003e species sampled in this study. Two distance matrices were constructed and compared. The Bray-Curtis dissimilarity matrix of the microbial community was calculated from ASV abundance data, and a host phylogenetic distance matrix was derived from the host phylogenetic tree. The correlation between the two matrices was evaluated using a Mantel test with Spearman\u0026rsquo;s rank correlation, and statistical significance was assessed by permutation testing (999 permutations), which was used to examine the relationship between host phylogenetic distance and microbial community composition.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDifferent habitats shape\u003c/b\u003e \u003cb\u003eAcropora\u003c/b\u003e \u003cb\u003ecoral assemblages\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe MJ and BW reef areas represented different habitats of \u003cem\u003eAcropora\u003c/em\u003e corals, as indicated by a total of 12 environmental parameters and overall reef-building coral coverage (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e,2). Compared to BW reef, the MJ reef exhibited significantly lower values of dissolved inorganic carbon (DIC), silicate concentration (SI), and total alkalinity (TA), but higher values of salinity (Sal), dissolved oxygen (DO), chlorophyll \u003cem\u003ea\u003c/em\u003e (Chl \u003cem\u003ea\u003c/em\u003e), net primary production (NPPV), and seawater velocity (SWV) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, the two reefs showed no significant differences in phosphate concentration (PO₄), temperature (Temp), and pH value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Twice SCUBA surveys conducted in May 2023 and 2024 revealed that the two reefs harbor distinct assemblages of \u003cem\u003eAcropora\u003c/em\u003e species. The MJ reef hosts 45 species, almost twice as many as at the BW reef (23). Moreover, all the species recorded at BW reef are also found at MJ reef, with the exception of \u003cem\u003eA. latistella\u003c/em\u003e and \u003cem\u003eA. retusa\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcropora\u003c/b\u003e \u003cb\u003especies harbor distinct bacterial communities compared to the environment\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 170 coral samples were collected, covering 47 \u003cem\u003eAcropora\u003c/em\u003e species distributed at the two reefs, together with background seawater samples for comparison (MJ: 7; BW: 9). DNA was extracted and 16S rRNA genes were amplified, yielding 24,340 bacterial amplicon sequence variants (ASVs) from coral and seawater samples. Venn diagrams showed that the four groups of samples shared a limited number of ASVs (0.559%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). A substantial proportion of ASVs were exclusively found in coral samples from MJ (52.4%) and BW (25.3%), whereas much smaller fractions were obtained in seawater samples (5.26% from MJ and 5.41% from BW).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparative analysis showed that coral samples host higher bacterial taxonomic diversity than surrounding seawater samples at both habitats. The MJ coral samples harbored a total of 56 bacterial phyla, covering 591 families and 1,431 genera, while the BW coral samples contained 54 bacterial phyla, comprising 525 families and 1,155 genera. In contrast, the MJ seawater samples harbored 27 bacterial phyla, containing 157 families and 276 genera, whereas BW seawater harbored 33 bacterial phyla, including 221 families and 376 genera (Supplementary material 2). At the phylum level, the coral-associated bacterial communities were dominated by Pseudomonadota (MJ-C: 76.32%, BW-C: 70.03%) and Bacteroidota (MJ-C: 2.74%, BW-C: 8.22%). In contrast, the seawater samples displayed different profiles, with Pseudomonadota (MJ-S: 50.27%, BW-S: 48.27%), Cyanobacteriota (MJ-S: 32.70%, BW-S: 28.92%), and Bacteroidota (MJ-S: 8.50%, BW-S: 14.09%) being the three most abundant phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). At the genus level, the coral microbiome was dominated by \u003cem\u003eEndozoicomonas\u003c/em\u003e (MJ-C: 28.77%, BW-C: 20.82%), \u003cem\u003eVibrio\u003c/em\u003e (MJ-C: 8.21%, BW-C: 7.86%), and \u003cem\u003ePseudoalteromonas\u003c/em\u003e (MJ-C: 5.93%, BW-C: 5.72%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The seawater samples harbored distinct bacterial community dominated by planktonic groups, including \u003cem\u003eSynechococcus\u003c/em\u003e CC9902 (MJ-S: 23.03%, BW-S: 7.39%), \u003cem\u003eAlteromonas\u003c/em\u003e (MJ-S: 4.35%, BW-S: 6.31%), and \u003cem\u003eProchlorococcus\u003c/em\u003e MIT9313 (MJ-S: 9.49%, BW-S: 21.47%). This clear partitioning of dominant taxa highlights the host-specific nature of the coral-associated bacterial community against the surrounding environment.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAlpha\u003c/em\u003e-Diversity analyses further revealed significant differences in bacterial community composition between coral and seawater samples, as indicated by the Chao1 and Shannon indices, at MJ reef (MJ-C vs MJ-S; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) and BW reef (BW-C vs BW-S; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Instead, the Simpson index was only significant in the BW reef (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, PCoA results showed a significant separation between coral-associated and seawater communities across the two coral reefs (PERMANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDistinct bacterial communities facilitate functional niche partitioning in\u003c/b\u003e \u003cb\u003eAcropora\u003c/b\u003e \u003cb\u003emicrobiome across habitats\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNeutral community model (NCM) analysis showed that the goodness-of-fit values for bacterial communities associated with \u003cem\u003eAcropora\u003c/em\u003e corals across the two habitats were both below 0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), indicating that their assembly was predominantly governed by deterministic processes (environmental filtration and host selection). No significant difference in α-diversity (Chao1, Shannon and Simpson) of the bacterial communities was observed between coral samples from the two reefs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). However, β-diversity analysis revealed a significant separation between MJ-C and BW-C in the overall structure of the microbiome community (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The results indicated that the diversity of the bacterial communities associated with \u003cem\u003eAcropora\u003c/em\u003e corals between the two habitats were comparable at ASV level, whereas their community compositions were significant different.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLEfSe analysis further identified the feature species responsible for the differential coral-associated bacterial communities between the two habitats. The bacterial community of MJ-C samples was predominantly defined by the phylum Pseudomonadota, specifically the family Endozoicomonadaceae. Typical coral symbionts \u003cem\u003eEndozoicomonas\u003c/em\u003e and \u003cem\u003eParendozoicomonas\u003c/em\u003e were significantly enriched in MJ-C samples, establishing them as the core taxonomic signatures of this group. Additionally, the enrichment of \u003cem\u003eAlgicola\u003c/em\u003e further reinforced the dominance of Gammaproteobacteria in this group. In contrast, the BW-C group exhibited a more taxonomically diverse profile associated with complex ecological functions. Key biomarkers spanned multiple phyla, including Bacteroidota (e.g., \u003cem\u003eFlavobacteriaceae\u003c/em\u003e and \u003cem\u003eCytophagales\u003c/em\u003e) and Myxococcota, both of which are mainly involved in organic matter degradation. The BW-C group also enriched microbes affiliated with Alphaproteobacteria involved in nutrient cycling and biofilm formation (e.g., \u003cem\u003eParacoccaceae\u003c/em\u003e), and other taxa associated with sedimentary or symbiotic environments, such as \u003cem\u003eCandidatus Amoebophilus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec; Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table S5).\u003c/p\u003e\n\u003ch3\u003eEnvironmental filtering overrides host phylogeny in driving the coral bacterial community assembly across habitats\u003c/h3\u003e\n\u003cp\u003eHabitat-specific differentiation in coral-associated bacterial communities was evident across reef areas, whereas no corresponding pattern with host phylogenetic relatedness was detected. Coral-associated bacterial communities exhibited a pronounced location-based clustering across the two reef areas regardless of host relatedness, implying that microbiome differentiation aligns more strongly with habitat differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Consistently, no significant correlation between host phylogenetic distance and microbial community dissimilarity was observed (Bray-Curtis distance) (Mantel test, \u003cem\u003er\u003c/em\u003e = -0.0056, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.496; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This suggests that at the observed scale in this study, host kinship is not the primary determinant of microbiome structural differentiation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the significantly enriched symbiotic bacteria in the MJ-C group, \u003cem\u003eAlgicola\u003c/em\u003e exhibited significant positive correlations with pH (Mantel test based on Spearman's rank correlation: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.672, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) and Temp (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.605, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It also displayed a marginal positive but less significant trend with DIC (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.302, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055), TA (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.277, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.069), and SI (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.287, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.061). Additionally, \u003cem\u003eEndozoicomonas\u003c/em\u003e was significantly linked to phosphate levels (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.565, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the BW-C group, \u003cem\u003eP3OB-42\u003c/em\u003e was significantly positively correlated with DO (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and TA (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.347, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Besides, \u003cem\u003eSpirochaeta\u003c/em\u003e was significantly correlated with COV (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.564, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016\u0026thinsp;\u0026lt;\u0026thinsp;0.05), NO\u003csub\u003e3\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.554, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and pH (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.444, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, \u003cem\u003eCandidatus Amoebophilus\u003c/em\u003e and an unclassified member of the \u003cem\u003eParacoccaceae\u003c/em\u003e family showed moderate but non-significant correlation with these 13 factors. This location-based clustering pattern underscores the dominance of environmental filtering over host phylogeny in driving the divergent assembly and structural characteristics of coral microbiomes across the two reef areas.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInteraction within the core bacterial community confers functional resilience across habitat heterogeneity\u003c/h2\u003e \u003cp\u003eFunctional prediction using PICRUSt2 indicated that taxonomic divergence between the MJ-C and BW-C groups did not result in significant shifts in core metabolic potential. The enriched functional pathways were highly conserved at KEGG Level 2 between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea; Table S6). Carbohydrate and amino acid metabolism, along with energy metabolism, cofactor/vitamin metabolism, and membrane transport were the most abundant functional categories. Although the MJ-C and BW-C groups have recruited taxonomically distinct bacterial communities, both exhibit a host-driven selection pattern that prioritizes functional compatibility with the coral hosts. These results indicated that a flexible coral-associated bacterial community across \u003cem\u003eAcropora\u003c/em\u003e enabled them to maintain metabolic stability to adapt to diverse environmental conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite functional conservation, potential differences in habitat-dependent reorganization of microbial interactions were observed. Co-occurrence network analysis revealed distinct topological structures between the BW and MJ coral bacterial communities. The BW network (N: 321; E: 1163) had more nodes and edges than the MJ (N: 259, E:162) network. Furthermore, the average degree and network density of BW were higher, while the average path length was shorter, indicating a broader interaction among the BW bacterial communities and higher transmission efficiency. However, the MJ network demonstrated higher modularity (MC: 0.735; BC: 0.446) and a greater number of communities (MC: 171; BC: 99), suggesting a more compartmentalized organization with denser within-module associations and fewer between-module links (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, b; Table S7).\u003c/p\u003e \u003cp\u003eModules with more than 10 member taxa were extracted, yielding five core submodules in both the BW and MJ groups. Functional profiles predicted using PICRUSt2 and compared at KEGG level 3 revealed that, despite marked differences in overall network topology and interaction patterns between the two reefs, the core modules exhibited a one-to-one functional correspondence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Specifically, MJ5 and BW1 exhibited similar functional signatures, with enrichment in ABC transporters and glycine, serine, and threonine metabolism. MJ3 and BW5 were primarily enriched in valine, leucine, and isoleucine degradation, as well as the bacterial secretion system. MJ4 and BW3 were functionally comparable and enriched in flagellar assembly and biofilm formation. In addition, MJ2 resembled BW2 in predicted functions, and MJ1 resembled BW4, with both pairs showing enrichment in carbon fixation pathways and aminoacyl-tRNA biosynthesis. Further examination of these core modules indicated that functionally corresponding module pairs differed in their within-module interaction patterns and taxonomic composition (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e, S4). These findings suggest that host-associated bacterial communities and interaction architectures can be reorganized into modules that perform analogous ecological functions, resulting in cross-site functional convergence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough multi-scale comparisons across habitats and coral reefs, this study delineated the differentiation patterns and functional adaptations of bacterial communities associated with \u003cem\u003eAcropora\u003c/em\u003e corals at the host-environment interface. The findings revealed that coral-associated bacterial communities were markedly distinct from the surrounding seawater, exhibiting pronounced host-specific differentiation. Despite substantial differences in the taxonomic composition of coral-associated bacterial communities across the two reef regions, their predicted core metabolic functional profiles were highly conserved. Notably, at the spatial and species distance scales examined, environmental filtering exerted a far stronger influence on the assembly of coral-associated communities than host phylogenetic signals, suggesting a flexible, adaptive response to local environmental pressures.\u003c/p\u003e \u003cp\u003eComprehensive evidence across phylum, genus, and ASV levels consistently revealed that coral-associated bacterial communities were strongly segregated from the surrounding seawater reservoir (Galand et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Large-scale surveys from the \u003cem\u003eTara\u003c/em\u003e Pacific expedition demonstrate that coral-associated microbiomes are significantly more diverse and host-specific than those in the open ocean (Galand et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Usually, the seawater bacterial community was dominated by free-living photosynthetic cyanobacteria and planktonic heterotrophic bacteria, whereas coral samples were enriched in symbiotic lineages such as \u003cem\u003eEndozoicomonas\u003c/em\u003e, \u003cem\u003eVibrio\u003c/em\u003e, and \u003cem\u003ePseudoalteromona\u003c/em\u003e (Kriefall et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gantt et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this work, significant differences in the Chao1 and Shannon indices between the coral and seawater groups were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-f), indicating that corals do not merely passively trap water-borne microbes but actively filter and reconfigure background microbial communities to suit their specific habitats (Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This deterministic assembly process was likely driven by the unique physiological conditions within the coral holobiont, including the nutrient-rich surface mucus layer and the controlled immune landscape (Mohamed et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The dominance of \u003cem\u003eEndozoicomonas\u003c/em\u003e in coral groups in this work was particularly noteworthy, as recent genomic evidence suggests these symbionts are primed for a symbiotic lifestyle through the modulation of host immunity and the provisioning of essential metabolites (Ding et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pogoreutz et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our observation that seawater bacterial communities exhibited greater spatial variability, whereas coral groups remained relatively stable across different reef sites further supported the host-selection hypothesis, wherein the host\u0026rsquo;s internal microenvironments exert stronger constraints on community structure than external environmental fluctuations (Pollock et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The host selection mechanism established a fundamental microbial basis for nutrient cycling and environmental resilience of the holobiont (Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), while simultaneously raising the question of how coral-associated bacteria adjust their community composition across contrasting habitats to sustain holobiont function.\u003c/p\u003e \u003cp\u003eIn the two habitats, \u003cem\u003eAcropora\u003c/em\u003e-associated bacterial communities displayed pronounced taxonomic divergence, with MJ enriched in core Gammaproteobacteria (e.g., \u003cem\u003eEndozoicomonas\u003c/em\u003e and \u003cem\u003eParendozoicomonas\u003c/em\u003e), whereas BW harbored diverse lineages (e.g., Bacteroidota and Myxococcota) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In line with this strong compositional turnover, the neutral community model showed limited explanatory power for both habitats (MJ: 0.298; BW: \u0026minus;0.031; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), suggesting that community assembly deviated from neutral expectations and that non-neutral processes likely contributed to the observed patterns. Despite their taxonomic differences, functional profiling indicated substantial overlap in predicted functional potentials, consistent with functional redundancy and the maintenance of core metabolic capacity via taxonomic replacement (Hernandez-Agreda et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fiore et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such component replacement allows the corals to fine-tune their microbial partners to local environmental pressures without compromising core physiological homeostasis (Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Monta\u0026ntilde;o-Salazar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Co-occurrence network analysis further revealed that such functional consistency may be supported by reef-specific interaction architectures, whereby distinct taxa are reorganized into functionally corresponding network modules through different within-community association patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). Together, these results support the view that the coral-associated bacterial communities are both functionally stable and compositionally flexible (Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By prioritizing the conservation of key functional traits over specific taxonomic identities, \u003cem\u003eAcropora\u003c/em\u003e corals employ taxonomic plasticity and bacterial network reorganization as adaptive strategies to different habitats (Camp et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Strudwick et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Voolstra et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This deterministic recruitment of functionally redundant partners ensures that the coral holobionts can navigate environmental heterogeneity while preserving metabolic stability, a strategy that may be crucial for their resilience amid escalating anthropogenic pressures (C\u0026aacute;rdenas et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Monta\u0026ntilde;o-Salazar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBroad-scale meta-analyses across diverse coral families reveal phylosymbiosis evolution between coral hosts and their symbiotic bacterial communities (Pollock et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Voolstra et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eAcropora\u003c/em\u003e was selected as a representative model because of its ecological prominence as a reef-building genus, which provides a relevant framework for testing microbiome responses to environmental heterogeneity. In this study, conducted at the genus level (46 \u003cem\u003eAcropora\u003c/em\u003e species), the design minimizes major host-phylogenetic confounding that often complicates cross-genus or cross-family comparisons, while also improving inference robustness by avoiding reliance on any single host species (Dunphy et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This framework is well-suited to evaluate whether local environmental gradients can impose sufficiently strong selective pressures to restructure bacterial communities even when host genetic differences are relatively minor within a genus. Such location-driven bacterial communities restructuring aligns with recent evidence from \u003cem\u003eAcropora cervicornis\u003c/em\u003e and \u003cem\u003eMillepora\u003c/em\u003e spp. clones, which suggests that reef environments can reshape microbial partners even within closely related or genetically identical host populations (Miller et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dub\u0026eacute; et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The observed decoupling of taxonomic flexibility and functional conservation in this study reinforces the paradigm that simplicity exists within a diverse microbial biosphere (Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By leveraging functional redundancy through deterministic bacterial assembly, \u003cem\u003eAcropora\u003c/em\u003e corals effectively balance differential habitats adaptation with holobiont metabolic stability, a strategy central to their persistence in a rapidly changing ocean.\u003c/p\u003e \u003cp\u003eBy utilizing \u003cem\u003eAcropora\u003c/em\u003e as a representative model, our findings provide a broader conceptual framework for understanding the resilience and conservation of scleractinian corals in an era of rapid environmental change. We demonstrate that the coral holobiont\u0026rsquo;s capacity to navigate environmental heterogeneity is underpinned by a flexible yet stable bacterial assemblage strategy that maintains core metabolic functions by recruiting taxonomically diverse but functionally redundant microbial lineages (Ziegler et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Voolstra et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This suggests that, across a wide range of branching corals, the microbiome acts as a dynamic buffer, enabling refined physiological adjustments to local stressors. From a conservation perspective, this study shifts the focus from merely preserving host genetic diversity to maintaining the environmental\u0026ndash;microbial synergy that facilitates adaptive transition (Hu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kriefall et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohamed et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given that environmental filtering, rather than host phylogeny, is the primary driver of community assembly at these scales, management strategies must prioritize protecting water quality and habitat heterogeneity (Gantt et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This ensures that the basic bacterial library remains intact, allowing hosts to continue filtering and reconfiguring specific symbiotic partners necessary for their adaptation and survival.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoral sampling was carried out following approval from the Animal Experiment Ethics Committee in the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ministry of Science and Technology of China (2021YFF0502800), the National Natural Science Foundation of China (NSFC; grant nos.\u0026nbsp;42476109,\u0026nbsp;42276163 and 32370462).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are provided in the Supplementary Information and Supplementary Datasets. Specifically, Supplementary Dataset 1 presents a microbial species abundance table (Level 1 to Level 7) annotated based on 16S rRNA gene sequences.; Supplementary Dataset 2 contains the phylogenetic tree of 46 \u003cem\u003eAcropora\u003c/em\u003e coral species. The raw 16S rRNA gene sequencing data have been deposited in the Genome Sequence Archive (GSA) in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: XXXXX) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/s/4k8zk5Ac.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.Z. and S.H. conceived and supervised the project. W.Y., K.G., J.Z., Y.H., H.L.,and Z.Z. collected the samples. L.L., F.C. and J.Z. performed the experiments. L.L. and F.C. did the statistical analyses. L.L. and Z.Z. wrote the paper. Z.Z. and S.H. reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAzizah DS, Ismet MS, Cakasana N (2024) Potential of antagonistic activity from associated bacteria from healthy and bleaching \u003cem\u003eAcropora\u003c/em\u003e corals of Blitar waters, East Java, Indonesia. BIO Web Conf 106:5002\u003c/li\u003e\n\u003cli\u003eBarno AR, Villela HDM, Aranda M, Thomas T, Peixoto RS (2021) Host under epigenetic control: a novel perspective on the interaction between microorganisms and corals. BIOESSAYS 43:2100068\u003c/li\u003e\n\u003cli\u003eBastian M, Heymann S, Jacomy M (2009) Gephi: An open source software for exploring and manipulating networks. 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Beijing: Science Press (in Chinese)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coral microbiome, Bacterial community assembly, Assemblage plasticity, Deterministic processes, Environmental filtering","lastPublishedDoi":"10.21203/rs.3.rs-8795967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8795967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoral ecosystems are among the most representative symbiosis systems, with profound scientific significance for understanding the relationships between host and their microbiome. Environmental filtering and host phylogeny play essential roles in maintaining the microbiome of coral holobionts, yet their relative contribution to community assembly remains unsettled. In this study, we analyzed the bacterial composition of 170 samples from 47 \u003cem\u003eAcropora\u003c/em\u003e species across two geomorphologically contrasting habitats in the South China Sea, the steep-sloped Meiji Reef and the flat Beiwai Reef. Our results demonstrate that \u003cem\u003eAcropora\u003c/em\u003e corals from the two microhabitats host specialized bacterial assemblages distinct from those in the surrounding seawater, which are primarily shaped by deterministic processes. Within \u003cem\u003eAcropora\u003c/em\u003e genus, bacterial communities associated with hosts showed significant differences in taxonomic composition between the two habitats, and environmental drivers such as dissolved oxygen and primary production outweighed host phylogenetic signals in shaping bacterial community structure. Despite differences in microbial community composition, similar metabolic pathways were enriched in both habitats, representing core functional stability across environments. Co-occurrence network analysis further revealed that corals in these two habitats employed distinct topological strategies to achieve microbiome functional stability. These findings indicate that taxonomic flexibility combined with functional stability forms a stable yet adaptable strategy, allowing \u003cem\u003eAcropora\u003c/em\u003e to adjust to diverse environmental conditions. Our study highlights the vital role of environmental-microbial synergy in coral resilience and offers a theoretical foundation for microbe-informed reef restoration amid rapid global change.\u003c/p\u003e","manuscriptTitle":"Environmental heterogeneity overrides host phylogenetic distance in shaping the microbiome of Acropora corals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 17:02:08","doi":"10.21203/rs.3.rs-8795967/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4b90853-d741-46a6-9f5e-d4f5bd59171f","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T22:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 17:02:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8795967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8795967","identity":"rs-8795967","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}