Phytoplankton recruitment of specific microbial assemblages and phylosymbiotic patterns

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Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Phytoplankton recruitment of specific microbial assemblages and phylosymbiotic patterns View ORCID Profile Joo-Hwan Kim , Jin Ho Kim , Zhun Li , Hyeon Ho Shin , Young Kyun Lim , Seung Ho Baek , Pengbin Wang , Ro Young Park , Soo Hwan Jin , Myung-Soo Han , View ORCID Profile Bum Soo Park doi: https://doi.org/10.1101/2025.02.10.637405 Joo-Hwan Kim 1 Department of Life Science, College of Natural Sciences, Hanyang University , Seoul 04763, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joo-Hwan Kim Jin Ho Kim 2 Department of Earth and Marine Science, College of Ocean Sciences, Jeju National University , Jeju 63243, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhun Li 3 Biological Resource Center/Korean Collection for Type Cultures, Korea Research Institute of Bioscience and Biotechnology , Jeongeup, 56212, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hyeon Ho Shin 4 Division of Fisheries Life Science, Pukyong National University , Busan 48574, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Young Kyun Lim 5 Ocean Climate Response & Ecosystem Research Department, Korea Institute of Ocean Science & Technology , Busan 49111, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Seung Ho Baek 6 Ecological Risk Research Department, Korea Institute of Ocean Science & Technology , Geoje 53201, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pengbin Wang 7 Key Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources , Hangzhou, 310012, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ro Young Park 1 Department of Life Science, College of Natural Sciences, Hanyang University , Seoul 04763, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Soo Hwan Jin 1 Department of Life Science, College of Natural Sciences, Hanyang University , Seoul 04763, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Myung-Soo Han 1 Department of Life Science, College of Natural Sciences, Hanyang University , Seoul 04763, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bum Soo Park 1 Department of Life Science, College of Natural Sciences, Hanyang University , Seoul 04763, Republic of Korea 8 Hanyang Institute of Bioscience and Biotechnology, Hanyang University , Seoul 04763, Republic of Korea 9 Research Institute for Convergence of Basic Science, Hanyang University , Seoul 04763, Republic of Korea 10 Hanyang Institute of Advanced BioConvergence, Hanyang University , Seoul 04763, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bum Soo Park For correspondence: parkbs{at}hanyang.ac.kr Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Phytoplankton and bacteria represent two major pillars of the carbon cycle in marine ecosystems. While their interactions are known to be tightly linked, the specific mechanisms underlying these interactions remain largely unexplored. Evidence of host-specific microbial assemblages could serve as a foundation for studies of detailed host-microbe interactions, yet such research remains limited in phytoplankton. Here, we not only investigate the microbial assemblages of six phytoplankton species, including multiple strains of dinoflagellates and diatoms, but also samples of phytoplankton blooms from the field. Our results reveal the presence of host-specific microbial assemblages in phytoplankton, with members of the core microbial lineages (MCGs) playing pivotal roles in shaping host-specific microbial assemblages and contributing to network structures. Deterministic processes, particularly host genotypes, were the dominant factors shaping microbial assemblages, overriding environmental influences. Consequently, microbial composition reflected the evolutionary relationships of the host species, demonstrating phylosymbiotic patterns. These findings suggest that studying MCGs will be a crucial foundation for investigating specific phytoplankton-microbe interactions and highlight the ecological and evolutionary importance of host-microbial specificity in phytoplankton. Introduction Microbial communities in ecosystems play crucial roles in host nutrition, metabolism, protection from pathogens, and regulation of physiological processes such as immunity and development 1 , 2 , 3 , 4 , 5 . Host-specific microbial assemblages have been documented in a wide range of organisms, such as vertebrates 6 , 7 , 8 , 9 , 10 , 11 , invertebrates 9 , 12 , 13 , and plants 14 , 15 , providing clear evidence of host-microbiota coevolution and mutual benefits. Research into host-specific microbiomes is critical for understanding how these microbial partnerships influence the fitness, ecological dynamics, and persistence of host species. In some cases, host specificity is observed as phylosymbiosis, in which microbial assemblages reflect the evolutionary relationships of their hosts 15 , 16 , 17 , 18 . However, this pattern is not universal, as not all marine invertebrates and terrestrial mammals exhibit phylosymbiosis 19 , 20 . As key primary producers in aquatic ecosystems, phytoplankton are responsible for approximately half of Earth’s oxygen production and are crucial for the biogeochemical cycling of carbon, nitrogen, sulphur, and iron 21 , 22 , 23 . Simultaneously, bacteria play central roles in the decomposition and remineralization of organic matter, forming the functional backbone of these ecosystems 24 , 25 . Phytoplankton and bacteria are closely intertwined, with phytoplankton providing dissolved organic carbon, and bacteria supplying essential nutrients such as bioavailable nitrogen and phosphorus, as well as growth-promoting compounds such as vitamins 25 , 26 , 27 . These reciprocal interactions are particularly evident during algal blooms, during which bacterial communities actively support phytoplankton growth and shape bloom dynamics 27 , 28 , 29 , 30 , 31 . Recent findings have shown that bacteria can promote the growth of specific phytoplankton by regulating their aging physiology, revealing intricate mechanisms underlying these interactions 32 . However, the details of specific interactions between phytoplankton and bacteria remain largely unexplored. A promising starting point for studying these intricate relationships is to examine whether phytoplankton hosts harbour distinct host-specific microbial assemblages. Although some studies provide evidence for host-specific microbial assemblages in phytoplankton 29 , 33 , 34 , 35 , 36 , 37 , 38 , in part because many analyses were limited to comparisons within a single species or between species using only one strain per species, most research focuses on diatoms, despite the critical ecological roles of dinoflagellates in aquatic systems 39 , 40 . Many studies relied on laboratory strains, leaving host-specific microbial assemblages in natural environments largely unexplored. In this study, we address these gaps by investigating host-specific microbial assemblage patterns in both laboratory-cultured strains and samples of phytoplankton blooms collected from the field. We examined six microalgal species, including diatoms and dinoflagellates, across multiple strains and growth stages, analysing core microbial lineages. Our results provide strong evidence for host-specific microbial assemblages in phytoplankton, even in highly mixed aquatic environments, and identify potential phylosymbiotic patterns between phytoplankton hosts and their microbial assemblages. Results Host-specificity of microbial assemblages across phytoplankton species Microbial assemblages associated with six phytoplankton species exhibited distinct compositions depending on the host species ( Fig. 1a ). Among dinoflagellates, the dominant bacterial taxa were Rhodobacterales (Alphaproteobacteria) and Alteromonadales (Gammaproteobacteria). Sub-dominant taxa varied by species: Flavobacteriales (Flavobacteriia) were more abundant in Alexandrium catenella (Group I) and Gymnodinium catenatum , while members of Cytophagales (Cytophagia) showed higher relative abundance in A. catenella and A. pacificum (Group IV). In diatoms, Rhodobacterales consistently dominated in Cylindrotheca closterium and Coscinodiscuss granii , whereas Alteromonadales dominated in Pseudo-nitzschia pungens . Sub-dominant taxa also varied by species, including Bacillales (Bacilli) in Cylindrotheca closterium , Vibrionales (Gammaproteobacteria) in P. pungens , and a mix of Alteromonadales and Flavobacteriales in Coscinodiscuss granii . Dinoflagellates were significantly enriched in nine bacterial families, including Alteromonadaceae (Gammaproteobacteria), Flavobacteriaceae (Flavobacteriia), and Flammeovirgaceae (Cytophagia), compared with diatoms (linear discriminant analysis [LDA] score > 3.12, adjusted p-value < 0.05). In contrast, Bacillaceae (Bacilli) was the only significantly enriched family in diatoms (Supplementary Fig. 1a). Alpha diversity was significantly higher in dinoflagellates than in diatoms, with dinoflagellates associated with a greater Shannon index (2.54 vs. 1.46) and Chao1 values (125.6 vs. 65.18) (p < 0.001) (Supplementary Fig. 1b and c). This indicates that dinoflagellates harbour a more diverse array of microbial lineages. Download figure Open in new tab Fig. 1: Host-specificity of microbial assemblages in six phytoplankton species, confirmed in both laboratory-cultured strains and field-collected phytoplankton blooms. a: Order-level composition of microbiomes across strains and growth stages of the six phytoplankton hosts. b: PCoA of microbiomes associated with the six phytoplankton species, based on Bray–Curtis dissimilarity. Ellipses represent 95% confidence intervals for each host species. PERMANOVA results indicate significant clustering by host species. c: PCoA highlighting microbiome differences among six phytoplankton species by strain and growth phase. Dashed ellipses indicate growth-phase separation, while solid ellipses represent strain differences. PERMANOVA significance is shown with *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, not significant. d: PCoA of microbiomes in field-collected blooms of four phytoplankton species ( Akashiwo sanguinea, Alexandrium monilatum , Margalefidinium polykrikoides , Phaeocystis globosa ) sampled across Korea, China, and the US. Plots display microbiomes along PCoA axes 1 and 2 (left) and axes 1 and 3 (right). Data for samples from China, the US, and some Korean coastal regions were retrieved from the NCBI (Supplementary Table 3). To minimize variability from different sequencing hypervariable regions, microbiome abundance data were collapsed at the genus level. Beta diversity analysis revealed host-specific microbial assemblages which involve significant differences in the microbial assemblages of each phytoplankton host based on Bray–Curtis dissimilarity and confirmed by principal coordinates analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA, R² = 0.794, p < 0.001) ( Fig. 1b ). Most microbial assemblages were clustered with the microbial assemblages of the same phytoplankton species isolated from different spatiotemporal contexts (Supplementary Table 1, Supplementary Fig. 2b). For example, although the two strains of Cylindrotheca Closterium were separated spatially by 375 km and temporally by 347 days, they were clustered into the same clade in the hierarchical clustering analysis, indicating that they share a conserved microbial assemblage and are distinct from other phytoplankton species (Supplementary Fig. 3). Host-specificity in microbial assemblages was evident not only in Bray–Curtis dissimilarity results but also across other beta diversity metrics, consistently showing statistically significant distinctions (Supplementary Table 2). Distinct microbial compositions were observed across different growth stages of the host species, highlighting growth-specificity of microbial assemblages. For example, in A. catenella , the relative abundance of Rhodobacterales increased, whereas Kiloniellales decreased during the stationary phase compared with the lag phase. This pattern was consistent across all strains and replicates examined ( Fig. 1c ). PERMANOVA analysis supported these observations, showing that the microbial assemblage during the stationary phase of A. catenella strain AC-1 was more similar to the stationary phase microbial assemblage of strain AC-2 than to its own assemblage during the lag or log phases (R² = 0.82, p 0.05), indicating that the growth stage, rather than strain, plays a dominant role in shaping microbial assemblages. These growth phase–dependent variations were more prominent in dinoflagellates but were only observed in Cylindrotheca closterium among diatoms. While the microbiomes of dinoflagellates were highly similar between strains, several strain-specific differences were noted in P. pungens and Coscinodiscuss granii among diatoms ( Fig. 1c ). In P. pungens and Coscinodiscuss granii , no significant differences in microbiome assembly were observed between growth stages, although statistically significant strain-level differences were seen (R² = 0.24, p < 0.05; R² = 0.95, p < 0.001, respectively). Host-specific microbial assemblages were evident in phytoplankton bloom samples taken from the field, with microbial communities clustering distinctly according to the host species of the bloom ( Fig. 1d , Supplementary Fig. 4). For example, microbial assemblages associated with blooms caused by Margalefidinium polykrikoides (found in both Korean coastal waters and those of the United States), Akashiwo sanguinea (Korea), Alexandrium monilatum (United States), and Phaeocystis globosa (China) formed tight clusters distinct from one another in Bray–Curtis dissimilarity-based PCoA analysis (PERMANOVA, R² = 0.36, p < 0.05) (Supplementary Fig. 2, Supplementary Table 3). Microbial assemblages were more strongly differentiated by the host phytoplankton species (R² = 0.36) than by the geographical location of the bloom (R² = 0.27), highlighting the dominant influence of host species on community structure (p < 0.05). Core and host-specific microbial lineages across dinoflagellates and diatoms In laboratory-cultured strains, the number of core microbial lineages (core MLs) varied by host species, ranging from 5 to 33, excluding Coscinodiscuss granii , which lacked observable core MLs ( Fig. 2a ). Host-specific microbial lineages (HS-MLs) which were statistically significantly different and uniquely associated with individual hosts ranged from 1 to 16 ( Fig. 2a , 2b). Notably, nearly all HS-MLs (45 of 47 microbial lineages) overlapped with core MLs, highlighting their strong association ( Fig. 2a ). Among the top 200 microbial lineages for each host species, the proportion of core MLs and HS-MLs, collectively termed the members of the core MLs group (MCG), ranged from 3% to 17%, indicating that MCGs were numerically fewer than non-MCGs ( Fig. 2c ). However, MCGs accounted for a substantial portion of the total rarefied abundance for each host species, contributing 80.8% to 96.2%, suggesting that MCGs, while limited in diversity, dominate the microbial community quantitatively. Download figure Open in new tab Fig. 2: Members of core microbial lineages (MCGs) play a key role in shaping host-specific microbial assemblages across phytoplankton species. a: Number of core MLs and HS-MLs at the bacterial order level for each host species. ND indicates not detected. The central Venn diagram shows the overlap between Core MLs and HS-MLs. b: Microbiome taxa tree highlighting host-specific taxa. The outermost points of the tree represent the relative abundance of each microbial lineage. Nodes are colour-coded to indicate statistically enriched taxa for each host species, with node size reflecting the adjusted p-value (Bonferroni correction). The external bar plot shows linear discriminant analysis scores of differentially abundant taxa, with an effect size threshold of 3.2. Differential abundance analysis was conducted using a two-step approach: a Kruskal–Wallis test to detect taxa with overall group differences, followed by a Wilcoxon rank-sum test for pairwise comparisons. c : Proportion of core MLs and HS-MLs among the top 200 MLs for each host species by number of MLs and relative abundance. Core MLs shown here exclude HS-MLs within the MCGs. d: Venn diagrams of Core MLs for each host species. Circle sizes are proportional to the numbers indicated. Coscinodiscuss granii , which lacks core MLs, is not represented. e: Bacterial composition by lineage group (ML types) within dinoflagellates and diatoms. Percentages were calculated based on the number of microbial lineages. f: Bray– Curtis dissimilarity-based PCoA plots of MCGs (left) and non-MCGs (right) datasets. PERMANOVA results indicate significant clustering by host species. g: Distribution of Bray–Curtis dissimilarity values between and within species for the total microbiome, MCGs, and non-MCGs datasets. Boxplot boundaries represent the first and third quartiles, with the median indicated. Whiskers extend to the furthest data points within 1.5× the interquartile range. Coloured points inside the boxes denote mean values (each calculated from n =630). h: Comparison of between-species and within-species Bray–Curtis dissimilarity values for the total microbiome, MCGs, and non-MCGs datasets. The endpoints of the dumbbells represent the mean values from panel g. The values at the midpoints of the dumbbells represent the differences between between-species and within-species dissimilarities. i: Bacterial functional group abundances across phytoplankton species, as predicted by FAPROTAX analysis. Of the 19 functional groups that differed significantly between dinoflagellates and diatoms, all were more abundant in dinoflagellates on average. The left panel depicts the differences in functional group abundance, while the right panel presents linear discriminant analysis scores alongside adjusted p-values (Benjamini-Hochberg correction). Differential abundance was determined using a two-step approach: first, a Kruskal–Wallis test to identify group-level differences, followed by pairwise comparisons using a Wilcoxon rank-sum test. Boxplot boundaries represent the first and third quartiles, with the median indicated, and whiskers extend to the furthest data points within 1.5× the interquartile range. j: Differential abundance analysis of bacterial functional groups between ML groups (left: MCGs vs. non-MCGs; right: HS-MLs vs. other) using FAPROTAX predictions. Bubble colours indicate significantly enriched functional groups (adjusted p-value ≤ 0.05). To reduce visual complexity, nonsignificant results (p-value > 0.05) and groups with small effect sizes (LDA score < 6.28) are not shown. MCGs shared between different hosts were also observed. The number of MCGs shared between host pairs ranged from 1 to 3, with nearly all shared MCGs found exclusively within dinoflagellates ( Fig. 2d ). Additionally, four MCGs were identified in the dinoflagellates A. catenella , A. pacificum , and G. catenatum , three of which belonged to the family Alteromonadaceae, genus Marinobacter (the fourth was from an unidentified genus within Alteromonadaceae). In contrast, no shared MCGs were found among diatom hosts. Dinoflagellates consistently harboured more MCGs (17–34 per species) compared with diatoms (0–11 per species). Among dinoflagellates, the bacterial taxa associated with MCGs primarily included Rhodobacterales (Alphaproteobacteria), Alteromonadales (Gammaproteobacteria), and Flavobacteriales (Flavobacteriia), closely reflecting the taxonomic composition of all the microbial lineages detected in dinoflagellates ( Fig. 2e ). In contrast, diatoms exhibited a more restricted and simplified taxonomic composition within their MCGs. For example, while Bacilli and Flavobacteriia constituted 9.1% and 8.5%, respectively, of the total microbial lineages observed in diatoms, neither was represented among MCGs. Instead, MCGs in diatoms were dominated by Rhodobacterales (50.0%) and Alteromonadales (43.8%), suggesting that the core microbiome of diatoms is not only smaller but more taxonomically constrained compared with that of dinoflagellates. MCGs played a major role in driving host-specific microbial assemblages in phytoplankton species. Although MCGs collectively accounted for only 77 of the top 242 MLs observed in this study, a PCoA and PERMANOVA based solely on these MLs found stronger differentiation among host species compared with analyses of the remaining 165 MLs (non-MCGs) ( Fig. 2f ). Specifically, weighted UniFrac-based PERMANOVA results for MCG (R² = 0.877, p < 0.0001) showed significantly greater intergroup differences compared with those observed for the remaining MLs (R² = 0.516, p < 0.0001) or the full dataset of 242 MLs (R² = 0.794, p < 0.001). Consistent trends were also observed across beta diversity metrics, such as Bray–Curtis dissimilarity, further supporting the robustness of these findings ( Table 1 ). The difference in Bray–Curtis dissimilarity between species and within species was larger when calculated using MCG compared with calculations using non-MCGs or the total microbiome, highlighting the high contribution of MCGs to host specificity ( Fig. 2g and h ). View this table: View inline View popup Download powerpoint Table 1. PERMANOVA results comparing microbial community structure across groups and distance metrics. Functional group differences between dinoflagellate and diatom microbiomes and between core MLs group and other MLs Functional annotation of prokaryotic taxa (FAPROTAX) analysis revealed significant differences in bacterial functional groups between dinoflagellates and diatoms ( Fig. 2i , Supplementary Fig. 5). Notably, all functional groups showing significant differences were more enriched in dinoflagellates compared with diatoms. The functional group with the greatest difference between these two microalgal groups, based on LDA effect size, was “hydrocarbon degradation”. Other notable functional groups enriched in dinoflagellates included those related to carbon metabolism, such as “methylotrophy” and “methanol oxidation”, as well as groups associated with nitrogen and sulphur recycling and biopolymer degradation. Core MLs exhibited distinct functional profiles compared with other MLs, with chemoheterotrophic functions being significantly enriched across all host species ( Fig. 2j ). Additionally, hydrocarbon degradation was a prominent function observed in the core MLs of most hosts. Some functional differences among core MLs were also noted across hosts. For example, core MLs in A. catenella were enriched in “xylanolysis”, while those in A. pacificum exhibited functions associated with “intracellular parasites“. In G. catenatum , “methylotrophy“-related functions were prominent, whereas core MLs in P. pungens were enriched in functions related to “fermentation” and “nitrate reduction“. Functions observed in HS-MLs overlapped significantly with those in core MLs, including key functions enriched in specific hosts, such as hydrocarbon degradation and chemoheterotrophy. Network analysis of microbiomes by microalgal host species Co-occurrence network analysis of the microbiomes associated with each microalgal host revealed that bacterial network connectivity within dinoflagellate microbiomes was generally weaker compared with diatom microbiomes ( Fig. 3a ). Specifically, the clustering coefficient was significantly lower in dinoflagellates (0.57) than in diatoms (0.73) ( Fig. 3b ). Although the average degree (31.68 in dinoflagellates versus 65.46 in diatoms) and connectance (0.16 in dinoflagellates versus 0.33 in diatoms) also differed, these differences were not statistically significant. Download figure Open in new tab Fig. 3: Properties of co-occurrence networks and the significance of MCG nodes in phytoplankton-associated microbiomes. a: Co-occurrence networks constructed from the top 200 microbial lineages by abundance for each of the six phytoplankton hosts. The upper row represents microbiomes associated with dinoflagellates, while the lower row depicts those of diatoms. Nodes are colour-coded to distinguish MCGs from non-MCGs lineages. Coscinodiscuss granii contains only non-MCG nodes as no MCGs were identified. b: Comparison of three network properties (clustering coefficient, connectance, and degree) between dinoflagellate-and diatom-associated microbiomes. Data represent the mean values for each group (n = 3). Error bars indicate standard errors. Numbers above the bars denote p-values. Statistical significance was calculated using a two-sided Student’s t-test. c: Distribution of three node properties (degree, closeness, and betweenness) for MCG and non-MCG nodes within each host’s co-occurrence network. Boxplot boundaries represent the first and third quartiles, with the median indicated. Whiskers extend to the furthest data points within 1.5× the interquartile range. To evaluate statistical significance and address differences in sample sizes between groups, a permutation test with 1,000 resamples was performed. P-values were adjusted using the Benjamini–Hochberg method. Within dinoflagellate microbiome networks, MCG nodes exhibited significantly higher degree, closeness, and betweenness centrality compared with other nodes, emphasizing their critical role in network structure ( Fig. 3c ). In contrast, in diatom microbiomes, other nodes showed higher degrees and closeness centrality compared with MCG nodes, indicating that the relative importance of MCG nodes in diatom networks was lower than in dinoflagellates. Deterministic influences and phylosymbiotic patterns in microalgal microbiome assembly To evaluate the relative contributions of stochastic and deterministic processes in shaping the host-specific microbiome assembly, we analysed the normalized stochasticity ratio (NST) of the Jaccard matrix (NST jac ). This metric distinguishes between stochastic (NST > 50%) and deterministic (NST < 50%) influences on community structure. The results indicate that microbiome structures in both dinoflagellates and diatoms were predominantly shaped by deterministic factors ( Fig. 4a ). Dinoflagellates exhibited a mean NST value of 16.8 ± 3.1%, with a maximum value of 32.3%, reflecting a strong deterministic control. In contrast, diatoms showed a higher mean NST value of 32.4 ± 13.2% and a maximum of 90.1%, suggesting that stochastic influences were more prominent in diatom microbiomes compared with dinoflagellates. Download figure Open in new tab Fig. 4: Microbiome distance in phytoplankton is most strongly correlated with the evolutionary distance between hosts, supporting the observation of phylosymbiotic patterns. a : Comparison of the normalized stochasticity ratio (NST) of the Jaccard matrix between dinoflagellates and diatoms. Values greater than 0.5 indicate that stochastic factors play a dominant role in microbiome assembly, while values below 0.5 indicate a stronger influence of deterministic factors. b: Correlation of Bray–Curtis dissimilarity between host microbiomes with phylogenetic, physicochemical, and spatiotemporal distances. The phylogenetic distance between hosts was calculated using pairwise distances based on LSU rDNA sequences (1,031 bp). All distance types, except for Bray–Curtis dissimilarity and phylogenetic distance, were measured as Euclidean distances and normalized to a maximum value of 1. The R² values for each distance type’s correlation with Bray–Curtis dissimilarity are displayed in radar plots, categorized by dinoflagellates, diatoms, and total data. All significant correlations had P < 0.001. Lines show loess regression fits between Bray–Curtis dissimilarity and each type of distance, with shaded areas represent the 95% confidence intervals. c: Phylosymbiotic analysis comparing topological congruence between the rooted host phylogeny and microbiota dendrogram. The host phylogenetic tree was constructed using LSU rDNA sequences (1,031 bp), and the microbiota dendrogram was generated by UPGMA clustering based on Bray–Curtis dissimilarity. Topological congruences were quantified using the normalized Robinson–Foulds (nRF) and normalized matching cluster (nMC) metrics, with values ranging from 0 (complete congruence) to 1 (complete incongruence). Note: LSU rDNA sequence data for Coscinodiscuss granii was unavailable in NCBI, so the sequence from its closely related species, Coscinodiscuss wailesii , was used instead . Correlation analysis to identify deterministic factors influencing microbial assemblages revealed that the dissimilarity between microalgal-associated microbiomes was not significantly correlated with physicochemical factors, including temperature, salinity, pH, dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), chlorophyll-a (Chl-a), or spatiotemporal distance at the time of host strain isolation. However, a strong positive correlation was observed with the phylogenetic distance between hosts (R = 0.61, p < 0.001) ( Fig. 4b ). Specifically, microbiome similarity increased with decreasing evolutionary distance between hosts. This relationship was notably stronger in dinoflagellates (R = 0.80, p < 0.001) compared with diatoms (R = 0.33, p < 0.001), indicating a more pronounced influence from host genetic relatedness on microbiome assembly in dinoflagellates. To further investigate phylosymbiotic patterns, we performed Robinson–Foulds and matching cluster analyses, which showed complete congruence (normalized distance = 0.0) between the molecular phylogeny of the host phytoplankton and the dendrogram topology of their microbiome dissimilarity across both evaluation methods (nRF = 0.00, p < 0.01; nMC = 0.00, p < 0.01) ( Fig. 4c ). The microbiome dendrograms could be divided into two major groups: dinoflagellates and diatoms. Within these groups, the microbiomes reflected the phylogenetic traits of their hosts, with dinoflagellate microbiomes clustering into unarmoured and armoured subgroups, while diatom microbiomes separated into pennate and centric subgroups. Additionally, congruence was also observed when using unweighted UniFrac to calculate microbiota distances, although no significant congruence was detected with weighted UniFrac ( Table 2 ). View this table: View inline View popup Download powerpoint Table 2. The normalised Robinson–Foulds (nRF) and the normalized Matching Cluster metric analysis across Bray–Curtis, weighted and unweighted UniFrac beta-ersity metrics. Discussion In this study, through comparative analyses of both laboratory-cultured strains and field-collected bloom samples, we found evidence suggesting that microbial assemblage in phytoplankton exhibited a degree of host-specificity. We found that strains of the same microalgal species, isolated from spatiotemporally distant locations, exhibited highly similar microbiome assemblies. Even phytoplankton strains isolated from the same time and location, such as A. catenella (AC-2), A. pacificum (AP-2), and G. catenatum (GC-1), shared only 4 MLs, and these MLs were consistent detected in MLs of strains isolated from different times and locations, such as A. catenella (AC-1), A. pacificum (AP-1), and G. catenatum (GC-2), indicating microbial assemblages in phytoplankton are highly preserved and have their own specificity (Supplementary Fig. 6). The region surrounding phytoplankton cells, known as the phycosphere, is rich in organic matter, which is known to support active interactions with bacteria 27 , 41 . Our findings suggest that the phytoplankton phycosphere is remarkably robust in maintaining its microbial assemblages, likely facilitated by active host-microbe interactions within this microenvironment. While previous studies have explored host-specific microbial assemblages in phytoplankton, they primarily focused on single species or limited geographical regions 28 , 29 , 36 , 38 , 42 , 43 , 44 . By analysing samples of bloom samples collected from multiple geographically distinct locations in the field, our study provides novel insights into host-specificity across multiple phytoplankton species under natural conditions. Notably, while the similarity of microbial assemblages within the same species was moderately lower in samples of phytoplankton blooms compared with lab-cultured strains, the degree of similarity remained high in natural environments. This difference can be attributed to the methodologies used for sample preparation. Lab-cultured strains, established through single-cell isolation, are expected to retain microbes that are physically closely associated with the host cells. In contrast, field-collected bloom samples, analysed by filtering entire seawater samples, likely included microbes unrelated to the host. As a result, host specificity in microbial assemblages was moderately reduced in field samples. Despite this reduction, microbial assemblages associated with the same phytoplankton species exhibited remarkable similarity across field samples, even when collected from geographically and temporally distant locations. This underscores the robustness of host-specific microbial assemblages in phytoplankton, even under the dynamic conditions of natural environments. The observation that phytoplankton, which inhabit a homogenous water column, maintain host-specific microbial assemblages is particularly intriguing. Most studies reporting host-specific microbial assemblages focused on heterogeneous environments, such as the gut microbiomes of vertebrates and invertebrates, or root-associated microbiomes of terrestrial plants 6 , 13 , 14 , 15 , 18 , 45 , 46 , 47 . Our findings indicate that even phytoplankton, which lack organs such as a gut, can exert a significant influence on the structure of their associated microbiomes. A key factor underlying the host specificity observed in phytoplankton microbiomes is the role of MCGs, which dominate host-associated microbial assemblages and contribute significantly to their distinct composition. Despite representing a smaller fraction of microbial diversity compared with non-MCGs, MCGs dominate host microbiomes and contribute significantly to their host specificity. The majority of MCGs identified in our study belong to the orders Rhodobacterales, Alteromonadales, and Flavobacteriales, which have been frequently associated with phytoplankton cultures and blooms in previous studies 25 , 48 , 49 , 50 . Among these, members of the family Roseobacteraceae (Order Rhodobacterales) dominated the Alphaproteobacteria class, accounting for 86 of 122 microbial lineages. Roseobacteraceae species are known to utilize various organic compounds released by phytoplankton, such as dimethylsulphoniopropionate and amino acids, and to participate in key biogeochemical processes involving carbon, nitrogen, sulphur, and phosphorus cycling. Additionally, they produce vitamins and growth-promoting compounds that benefit phytoplankton 51 , 52 . Alteromonadaceae (Order Alteromonadales) break down diverse polysaccharides, using carbohydrate-active enzymes encoded in polysaccharide utilization loci 53 . This group is particularly adept at degrading complex biopolymers, such as laminarin, alginate, xylan, and mannans, facilitated by their large genomes and extensive enzymatic repertoire. Similarly, Flavobacteriia use polysaccharides and proteins as primary sources of carbon and energy, efficiently recycling organic material and nutrients from phytoplankton blooms by processing photosynthetic byproducts or degrading algal cell walls 30 , 54 , 55 , 56 . Together, these MCGs are likely to play complementary roles in the remineralization of organic matter within the phytoplankton microenvironment, potentially contributing to localized nutrient cycling and energy flow. Beyond the dominance of MCGs in host microbiomes, their ecological significance lies in their diverse functional capabilities, which can be characterized using FAPROTAX predictions and reveal key metabolic traits crucial for interactions with phytoplankton hosts. Across all host species, MCGs exhibited distinct functional profiles compared with non-MCGs, with chemoheterotrophic functions being significantly enriched. This indicates that MCGs are likely highly dependent on host-derived organic matter, and suggests a commensal or even mutualistic relationship between phytoplankton hosts and their associated MCGs. Hydrocarbon degradation was a prominent function consistently observed in the MCGs of most hosts, far exceeding its representation in non-MCGs. Among dinoflagellates, the genus Marinobacter (family Alteromonadaceae) was commonly detected in the MCGs of all host species. Marinobacter is a well-known hydrocarbon-degrading bacterium, and one lineage, OTU22, was found to share 99.57% of its gene sequence with Marinobacter algicola , a hydrocarbon-degrading bacterium previously isolated from Alexandrium tamarense and Gymnodinium catenatum 57 . This highlights the inclusion of hydrocarbon-degrading bacteria in the core microbiome of dinoflagellates, a result consistent with previous studies 42 , 58 . Such associations suggest a particularly close ecological relationship between these microbial lineages and dinoflagellates. Some phytoplankton species, including dinoflagellates, are known to produce hydrocarbons 59 , 60 , 61 , 62 , which may explain the frequent presence of bacteria capable of hydrocarbon degradation. However, it remains unclear whether such bacteria are associated with all phytoplankton because they metabolize host-derived hydrocarbons or because they provide essential benefits critical for host survival. Previous studies have shown that hydrocarbon-degrading bacteria can promote phytoplankton growth by supplying essential nutrients or growth-promoting compounds 63 , 64 . Further research is needed to clarify whether these interactions constitute a mutualistic relationship and elucidate the underlying mechanisms driving these associations. As revealed by a microbiome co-occurrence network analysis, the MCG nodes in dinoflagellates played more significant roles in network connectivity compared with other nodes. This suggests that MCGs may act as ecological bridges, not only mediating interactions with the host but facilitating connections among other bacterial taxa. MCGs appeared to contribute substantially to maintaining the overall microbial assemblages of their hosts. Meanwhile, the co-occurrence network results for dinoflagellates, in which bacterial networks were weaker than those observed in diatoms, imply that dinoflagellate-associated bacterial communities are more tightly regulated by the host. This pattern resembles the weakening of microbial network stability seen under environmental stress, in which microbial assemblages across samples exhibit minimal variation and high similarity 65 . The weaker network connectivity observed in dinoflagellate microbiomes can be attributed to their host-specific microbial assemblages, which are more pronounced compared with those in diatoms. This is supported by the larger number of MCGs found in dinoflagellates and the higher similarity values within species than between species. The within-species similarity observed in our study aligns with that reported previously in research on microalgal microbiomes. The bacterial community structures associated with dinoflagellates were largely consistent at the order level, while diatoms exhibited greater variability. For example, in A. catenella , both our study and previous research identified Rhodobacterales, Cytophagales, and Flavobacteriales as dominant taxa 44 . In contrast, during the exponential phase of Cylindrotheca closterium , Rhodobacterales consistently emerged as the dominant taxon, aligning with our findings. However, unlike A. catenella , the composition of secondary dominant taxa in Cylindrotheca closterium varied between studies 43 . In P. pungens , earlier studies reported Rhodobacterales was the dominant taxon, with minimal representation of Alteromonadales. In our study, however, Alteromonadales was dominant, with Rhodobacterales being subdominant 33 . This suggests that diatom microbiomes are less conserved and more dynamic compared with dinoflagellate microbiomes. These findings align with previous reports indicating that diatom microbiomes, while generally conserved, can vary depending on environmental conditions at the time of strain isolation 43 , 66 . This flexibility in microbiome composition was most pronounced in Coscinodiscuss granii , which lacked observable core MLs, whereas the microbiomes of the other two diatom species were more stable. This observation does not entirely align with studies reporting a highly conserved microbiome structure in diatoms 33 , 34 , 35 . However, the taxonomic diversity within diatoms, categorized into three major classes — Coscinodiscophyceae, Mediophyceae, and Bacillariophyceae — provides a plausible explanation 67 , 68 . In this study, Coscinodiscuss granii belongs to Coscinodiscophyceae, while the other two species are members of Bacillariophyceae. The variation in microbiome conservation observed across diatom species and studies may reflect evolutionary and coevolutionary dynamics between hosts and their microbiomes, underscoring the need for further research to elucidate these relationships 47 , 69 , 70 . The reason for the greater microbial assemblage similarity among dinoflagellate hosts compared to diatom hosts is uncertain. Many animals and plants regulate their microbiomes to benefit the host by producing metabolites, such as adhesive molecules, antimicrobial peptides, and hormones 15 , 71 , 72 , 73 . Similarly, host-specific microbial assemblages in phytoplankton are thought to be strongly shaped by host-derived metabolites. For example, diatoms selectively modulate surrounding bacteria through the production of secondary metabolites 74 . Other studies demonstrated that manipulating the ratios of representative metabolites produced by two phytoplankton species can not only alter the bacterial community composition in natural seawater consortia but also produce predictable outcomes 75 , 76 . This serves as an example of how microbial assemblages can be regulated by phytoplankton hosts. The relatively strong host specificity observed in the microbiomes of dinoflagellates compared with that in diatoms may suggest that dinoflagellate-derived metabolites exert stronger selective pressures and that dinoflagellates may rely more heavily on their microbiomes. Results of a FAPROTAX analysis showed that dinoflagellates harbour a much richer array of bacterial functional groups compared with diatoms. Although the specific benefits these functional groups provide to the host remain unclear, dinoflagellates may depend more heavily on their microbiome’s functions and may apply stricter filtering to recruit beneficial bacteria 77 . The microbial assemblage is influenced by various factors, including selection, dispersal, diversification, and drift 78 , 79 , 80 , 81 . These factors are typically categorized into deterministic and stochastic processes. While traditional niche-based ecological perspectives emphasize that environmental factors play a significant role in shaping communities through deterministic processes 82 , recent studies suggest that stochastic factors may better predict variations in community structure 83 , 84 , 85 . In this study, microbial assemblages observed in both dinoflagellate and diatom microbiomes were predominantly governed by deterministic processes. However, NST analysis of diatom microbiomes revealed a greater prevalence of stochastic influences, with values exceeding the 50% threshold. This suggests that diatom microbiomes may be shaped by priority effects and other stochastic factors 38 , 86 . In our analysis, deterministic environmental factors such as temperature, salinity, pH, DO, COD, TN, TP, and Chl- a , as well as the spatiotemporal location of strain isolation, were considered. However, the factor most strongly correlated with microbiome dissimilarity (Bray–Curtis) between samples was not the physicochemical environment or spatiotemporal distance, but the evolutionary distance between hosts. This aligns with the results of previous studies showing that a host’s genotype can significantly influence its microbiome 38 . It also suggests that microalgal microbiomes are shaped primarily by host filtering, with selection acting as the dominant deterministic factor in microbiome assembly. The proposition that microbiome similarity increases as evolutionary relationships between hosts converge is central to the emerging idea of phylosymbiosis, which has attracted significant attention from ecologists over the past decade 70 , 86 , 87 . Phylosymbiosis has been observed in various organisms, including insects, mammals, and marine invertebrates, although some taxa do not exhibit this pattern 9 , 20 , 88 . To our knowledge, this is the first study to suggest that phytoplankton associated microbiomes may exhibit phylosymbiotic characteristics. We found evidence of phylosymbiosis in phytoplankton microbiomes despite their unicellular structure and microscopic size. This contrasts with findings reported for marine microscopic invertebrates, where such features are absent 20 . However, the congruence between microbiota dendrograms and host phylogenies varied depending on the beta diversity metrics used, highlighting the need for further validation. Given that this study examined only six host species and two strains per species, additional research with a broader range of host taxa and multiple strains is required to confirm the phylosymbiotic nature of phytoplankton-associated microbiomes. Moreover, future studies should explore whether phylosymbiosis is a universal trait among phytoplankton and identify the mechanisms driving this pattern, such as host-derived metabolites or ecological interactions shaping microbial community assembly. Conclusion This study identifies MCGs in phytoplankton microbiomes and describes their contribution to host-specific microbial assemblages. Investigating the interactions between host-specific MCGs and their phytoplankton hosts will be critical for elucidating previously unknown mechanisms of phytoplankton-bacteria interactions. Additionally, phylosymbiotic patterns were observed between phytoplankton and their microbiomes, suggesting a more interdependent co-evolutionary relationship than previously recognized. These findings underscore the necessity of jointly considering phytoplankton and bacterial communities to comprehensively understand their ecological dynamics and evolutionary interplay in marine environments. Methods Microalgal strain collection and bloom sampling To analyse the microbiomes of phytoplankton, three dinoflagellate species and three diatom species were selected (hereafter referred to as “lab microbiomes”). To minimize the impact of environmental factors on microbiome similarity, two strains with different spatiotemporal origins were used for each microalgal host species. For example, two strains of Cylindrotheca closterium were isolated from locations 375 km and 347 days apart. All algal strains were obtained from the Library of Marine Samples of the Korea Institute of Ocean Science and Technology in Geoje, South Korea. Information on the environmental conditions at the time of strain isolation (e.g., temperature, salinity, pH, dissolved oxygen, COD, TN, TP, and Chl-a) was provided (Supplementary Table 1). All strains were cultured in duplicate at 20°C in an F/2 growth medium 89 with a salinity of 31–33 under cool-white fluorescent lamps (photon flux of 100 μE m −2 s −1 ) on a 12-h light/12-h dark cycle. To observe microbiome composition changes across growth stages for each host, samples of 300 mL were taken during the lag, log, and stationary phases based on cell density. The samples were size-fractioned using 47 mm diameter Isopore membrane filters (Millipore, Cork, Ireland) with a pore size of 0.22–3.0 µm, and the filters were stored in an EX buffer (100 mM Tris-HCl, 100 mM Na2-EDTA, 100 mM sodium phosphate, 1.5 M NaCl, 1% CTAB) at −80°C until DNA extraction 90 . To compare microbiomes across field samples (“field microbiomes”), blooms of M. polykrikoides , a major harmful algal bloom dinoflagellate species in Northeast Asia, were collected from four different locations along the Korean coast in 2012 and 2015 (Supplementary Table 4). Additionally, samples of Akashiwo sanguinea (Dinophyceae) blooms from 2011 were included for comparison. Seawater samples were collected at a depth of <1 m using a 4.2 L Van Dorn water sampler (Wildlife Supply Company, MI, USA). Volumes of 50–300 mL of seawater were filtered by gravity onto Isopore filters (Millipore, Cork, Ireland) with a pore size of 5 µm, and the filters were submerged in the EX buffer and stored at −80°C until further analysis. In addition to the field samples, previously published microbiome data from other microalgal blooms were included for comparison. Microbiome data from M. polykrikoides , Alexandrium monilatum , A. sanguinea, and Phaeocystis globosa (Prymnesiophyceae) blooms were retrieved from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (PRJNA731462, PRJNA1061360, PRJNA685631, PRJNA678802) 28 , 91 , 92 , 93 (Supplementary Table 3). DNA extraction and sequencing of 28S rRNA gene Extraction of host algal DNA was performed on cell pellets from the cultured isolates following a protocol supplied with the DNeasy Plant Kit (Qiagen, Valencia, CA, USA). Polymerase chain reaction (PCR) amplification was performed with 50 μL reaction mixtures containing 23 μL of sterile distilled water, 10 of μL of 5× Phusion HF buffer (New England Biolabs, Beverly, MA, USA), 1 μL of a dNTP mixture (2.5 mM each), 2.5 μL of each primer (10 pmol), 1 μL of Phusion DNA polymerase (2.0 units/50 μL), and 10 μL of template DNA. Partial large subunit (LSU) rDNA sequences were amplified using primers described in Scholin et al. (1994). The ITS-5.8S-ITS2 rDNA sequences were determined using primers described in Nézan et al. (2012). PCR cycling for partial LSU and ITS-5.8S-ITS2 rDNA was carried out in an iCycler Thermal cycler (Bio-Rad, Hercules, CA, USA) using the following conditions: ITS-5.8S-ITS2; pre-denaturation 94°C for 2 min, 37 cycles of 94°C for 30 s, 52°C for 30 s, 72°C for 2 min, and a final extension at 72°C for 5 min, LSU; pre-denaturation 94°C for 3.5 min, 36 cycles of 94°C for 50 s, 45°C for 50 s, 72°C for 80 s, and a final extension at 72°C for 10 min. The PCR products were purified using a QIAquick PCR Purification Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions and sequenced (MCLAB, San Francisco, USA). Editing and contig assembly of rDNA sequence fragments were carried out using Bioedit v7.2.5 (Hall, 1999). DNA extraction and sequencing of 16S rRNA gene DNA Extraction For both the lab and field microbiomes, DNA was extracted from filters using a modified CTAB protocol 94 . Briefly, 2 mL microtubes containing membrane filters and an extraction buffer were subjected to three cycles of freezing in liquid nitrogen followed by thawing in a 65°C water bath. Afterward, 8 µL of proteinase K (10 mg mL - 1 in a TE buffer) was added, and the samples were incubated at 37°C for 30 min. Next, 80 µL of 20% sodium dodecyl sulphate was added, and the samples were incubated at 65°C for 2 h. The solution was then gently mixed with an equal volume of chloroform-isoamyl alcohol (24:1) and centrifuged at 10,000 g for 5 min. The aqueous phase was transferred to a fresh 2 mL tube containing 88.8 µL of 3 M sodium acetate (pH 5.1), followed by the addition of 587 µL of isopropanol (≥99%). After centrifugation at 14,000 g for 20 min, the supernatant was discarded, and 1 mL of cold 70% ethanol was added. The samples were then centrifuged at 14,000 g for 15 min, after which the pellets were air-dried at room temperature and dissolved in 100 µL of TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8). 16S rRNA Gene Sequencing Lab microbiome Sequencing libraries were prepared according to Illumina 16S Metagenomic Sequencing Library protocols to amplify the V3 and V4 regions of the 16S rRNA gene. Genomic DNA (2 ng/10 ng input) was amplified by PCR using a 5× reaction buffer, 1 mM dNTP mix, 500 nM each of the universal forward/reverse primers, and Herculase II Fusion DNA Polymerase (Agilent Technologies, Santa Clara, CA, USA). The conditions for the first PCR cycle were 95°C for 3 min for initial heat activation, followed by 25 cycles of 30 s at 95°C, 30 s at 55°C, and 30 s at 72°C, with a final extension at 72°C for 5 min. The universal primer pair with Illumina adapter overhang sequences used for the initial PCR amplifications were as follows: forward primer (5□-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3□) and reverse primer (5□-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3□). The first PCR product was purified using AMPure XP beads (Agencourt Bioscience, Beverly, MA, USA). Next, 10 µL of the first PCR product was used for a second round of PCR to construct the final library, incorporating NexteraXT indexed primers. The conditions for the second PCR cycle were identical to the first, except for 10 cycles. The PCR products were again purified with AMPure beads, and the final purified product was quantified using quantitative PCR (KAPA Library Quantification Kit for Illumina Sequencing Platforms) and validated using TapeStation D1000 ScreenTape (Agilent Technologies, Waldbronn, Germany). Sequencing was performed on the Illumina HiSeq platform (Illumina, San Diego, CA, USA). Field microbiome The extracted DNA samples were processed to generate amplicons for variable region 4 (V4) of the nuclear small subunit (SSU) rRNA gene using the universal bacterial/archaeal primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), which contain a 6 bp error-correcting barcode unique to each sample. The amplified samples were sequenced on an Illumina MiSeq platform with the 515F and 806R primers. Operational taxon unit (microbial lineage) analysis Lab microbiome The raw data were demultiplexed by index sequences to generate FASTQ files for each sample. Adapter sequences were trimmed using the fastp tool 95 , and error correction was performed for overlapping reads. The paired-end data for each sample were merged into single sequences using FLASH (v1.2.11) 96 . Sequences shorter than 400 bp or longer than 500 bp were discarded. The assembled sequences were processed using QIIME (v1.9) 97 and UCLUST 98 for microbial lineage picking in a closed-reference approach. The NCBI 16S Microbial DB was used as the reference. Finally, the microbial lineage abundance table was rarefied to the minimum number of sequences per sample. Field microbiome The raw sequencing data were processed in R using the EasyAmplicon pipeline package and incorporating various bioinformatics tools 99 . After removing barcodes and primers, paired-end sequences were merged, and quality filtering was applied. Dereplication of full-length sequences was performed, and sequences with a minimum unique size of 10 were retained. MLs were clustered at 97% similarity using de novo chimera removal. Taxonomic classification of the representative microbial lineages was performed against the SILVA 138.2 database 100 with a confidence threshold of 60%. Finally, the microbial lineage abundance table was rarefied to the minimum number of sequences per sample. The raw sequencing reads were deposited at the NCBI Short Read Archive under BioProject ID PRJNA1180708. Statistical analysis All statistical analyses were conducted in RStudio (v2024.04.2+764) using R (v.4.3.2). Microbial community comparisons were performed using the phyloseq (v.1.46.0) 101 , Microbiotaprocess (v.1.17.1) 102 , and vegan (v.2.6.6.1) 103 packages based on ML abundance, taxonomy, and phylogenetic tree data. Visualizations were generated using the ggplot2 package (v.3.5.1) 104 as needed. Prior to all analyses, chloroplast-and mitochondria-related microbial lineages were filtered out, and microbial lineage abundance was rarefied. For all analyses except co-occurrence networks, FAPROTAX, and phylosymbiosis, MLs with an average relative abundance of less than 0.005% were excluded. All p-values indicating the significance of microbiome differences between groups were adjusted using the Bonferroni or Benjamini-Hochberg correction. All PERMANOVA permutation tests were conducted with 9,999 permutations. To evaluate the statistical power of detecting group differences in beta diversity via PERMANOVA, a post hoc power analysis was performed using G*Power (v.3.1.9.7) 105 . The analysis was based on a total sample size of 72, with 6 groups, each consisting of 2 strains, and 6 repeated measurements per strain. An effect size (f) of 0.35, an α error probability of 0.05, and a correlation among repeated measures of 0.5 were applied. The power analysis yielded a noncentrality parameter λ of 15.12 and a critical F value of 2.3538, resulting in an achieved power (1-β) of 0.836 (83.6%). These results indicate that the study had adequate statistical power to detect significant between-group differences at a 5% significance level. Network analysis was carried out using the ggClusterNet R package 106 . Microbial functional prediction was performed using FAPROTAX based on taxonomic data using Python script 107 . To assess the impact of stochastic processes on the microbiome, we applied the normalized stochastic ratio on an NST jac using the NST R package 108 , with host taxonomy (dinoflagellate or diatom) as the grouping variable and strain origin as the meta-group. A correlation analysis of Bray–Curtis dissimilarity between phytoplankton strain microbiomes and the strain’s environmental and spatiotemporal information (Supplementary Table 1) was performed after log-transforming the data and normalizing Euclidean distances between samples to a maximum value of 1. Data for microbiome dissimilarity within the same strain across growth phases and replicates were excluded from the analysis. Evolutionary distance and phylosymbiosis analysis Evolutionary distance and host phylogeny Large subunit ribosomal DNA sequences (total length 1031 bp) were used to calculate evolutionary distances and construct phylogenetic trees among phytoplankton hosts (Supplementary Table 1). For dinoflagellates, LSU sequences from six strains were analysed in this study, with one representative sequence per species chosen to maximize the length of the comparison region (Supplementary Table 4). For diatoms, LSU sequences were downloaded from the NCBI, except for Coscinodiscus granii , for which no LSU sequences were available; those of the closely related Coscinodiscus wailesii were used instead (Supplementary Table 4). The evolutionary history was inferred using the maximum likelihood method and the Tamura-Nei model 109 . A bootstrap consensus tree based on 500 replicates 110 was generated to represent the evolutionary history of the analysed taxa. A discrete gamma distribution was used to model evolutionary rate variation among sites (5 categories (+G, parameter = 0.6128)). All evolutionary analyses, including model testing, were performed in MEGA11 111 . Microbiota dendrograms To obtain representative microbiota profiles for each species, the microbial lineage abundance data for each strain and growth phase of the six microalgal hosts were collapsed into a single profile for each species. Beta diversity distances between the six species’ microbiomes were calculated using the Bray–Curtis method. Distance matrices were then clustered using the UPGMA method to generate dendrograms reflecting interspecific microbiome relatedness. To assess the impact of different beta diversity measures on congruence with host phylogeny, dendrograms were generated using weighted UniFrac and unweighted UniFrac distance methods. Robinson–Foulds and matching cluster congruency analyses Congruence between host phylogeny and microbiota dendrograms was quantified using normalized Robinson– Foulds and normalized matching cluster methods. The analyses were performed using a Python script, with 100,000 random trees used as the comparison baseline 9 . Author contributions B.S.P. and J.-H.K. conceptualized the project. J.-H.K., J.H.K., Z.L., and Y.K.L. performed the wet laboratory experiments. H.H.S. collected phytoplankton strains. B.S.P., J.-H.K., and J.H.K. collected field phytoplankton bloom samples. J.-H.K. and P.W. conducted the metagenomic analyses. J.-H.K., R.Y.P., and S.H.J. curated the metadata of public samples. J.-H.K., J.H.K., and Z.L. designed the statistical analyses. B.S.P., H.H.S., S.H.B., and M.-S.H. acquired funding. J.-H.K., J.H.K., and Z.L. wrote the manuscript. B.S.P. supervised the work. All authors read and approved the manuscript. Competing interests The authors declare no competing interests. Acknowledgements This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. RS-2023-00209356 and No. 2022R1C1C1003582), and the Korea Institute of Marine Science & Technology (KIMST) funded by the Ministry of Oceans and Fisheries (RS-2023-00256330, Development of risk managing technology tackling ocean and fisheries crisis around the Korean Peninsula by Kuroshio Current). Footnotes ↵ a Ministry of Environment, Government Complex-Sejong, Sejong 30103, Republic of Korea ↵ b Mirae Shangtae (Co., Ltd.), R&D Headquarters, 10, songi-ro 24-gil, Songpa-gu, Seoul 05829, Republic of Korea ↵ * Co-first authors The supplementary data has been provided as a separate file. References 1. ↵ Douglas AE . Multiorganismal insects: diversity and function of resident microorganisms . Annual review of entomology 60 , 17 – 34 ( 2015 ). OpenUrl CrossRef PubMed 2. ↵ Douglas AE . Fundamentals of microbiome science: how microbes shape animal biology . Princeton University Press ( 2018 ). 3. ↵ Flórez LV , Biedermann PH , Engl T , Kaltenpoth M . Defensive symbioses of animals with prokaryotic and eukaryotic microorganisms . Natural Product Reports 32 , 904 – 936 ( 2015 ). OpenUrl CrossRef PubMed 4. ↵ Kissoyan KA , et al. Natural C. elegans microbiota protects against infection via production of a cyclic lipopeptide of the viscosin group . Curr Biol 29 , 1030 – 1037 . e1035 ( 2019 ). OpenUrl CrossRef PubMed 5. ↵ Obeng N , Bansept F , Sieber M , Traulsen A , Schulenburg H. Evolution of Microbiota–Host Associations: The Microbe’s Perspective . Trends in Microbiology 29 , 779 – 787 ( 2021 ). OpenUrl CrossRef PubMed 6. ↵ Ochman H , et al. Evolutionary relationships of wild hominids recapitulated by gut microbial communities . PLoS biology 8 , e1000546 ( 2010 ). OpenUrl CrossRef PubMed 7. ↵ Phillips CD , et al. Microbiome analysis among bats describes influences of host phylogeny, life history, physiology and geography . Molecular ecology 21 , 2617 – 2627 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 8. ↵ Moeller AH , et al. Rapid changes in the gut microbiome during human evolution . Proc Natl Acad Sci USA 111 , 16431 – 16435 ( 2014 ). OpenUrl Abstract / FREE Full Text 9. ↵ Brooks AW , Kohl KD , Brucker RM , van Opstal EJ , Bordenstein SR . Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history . PLoS biology 14 , e2000225 ( 2016 ). OpenUrl CrossRef PubMed 10. ↵ Pratte ZA , Besson M , Hollman RD , Stewart FJ . The gills of reef fish support a distinct microbiome influenced by host-specific factors . Applied and environmental microbiology 84 , e00063 – 00018 ( 2018 ). OpenUrl PubMed 11. ↵ Sylvain F-É , et al. Fish skin and gut microbiomes show contrasting signatures of host species and habitat . Applied and environmental microbiology 86 , e00789 – 00720 ( 2020 ). OpenUrl PubMed 12. ↵ Easson CG , Thacker RW . Phylogenetic signal in the community structure of host-specific microbiomes of tropical marine sponges . Frontiers in microbiology 5 , 532 ( 2014 ). 13. ↵ Sanders JG , Powell S , Kronauer DJ , Vasconcelos HL , Frederickson ME , Pierce NE . Stability and phylogenetic correlation in gut microbiota: lessons from ants and apes . Molecular Ecology 23 , 1268 – 1283 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 14. ↵ Ofek M , Voronov□Goldman M , Hadar Y , Minz D. Host signature effect on plant root□associated microbiomes revealed through analyses of resident vs. active communities . Environmental microbiology 16 , 2157 – 2167 ( 2014 ). OpenUrl CrossRef 15. ↵ Jones P , Garcia BJ , Furches A , Tuskan GA , Jacobson D . Plant host-associated mechanisms for microbial selection . Frontiers in Plant Science 10 , 862 ( 2019 ). 16. ↵ Dubilier N , Bergin C , Lott C . Symbiotic diversity in marine animals: the art of harnessing chemosynthesis . Nature Reviews Microbiology 6 , 725 – 740 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 17. ↵ Venn AA , Loram JE , Douglas AE . Photosynthetic symbioses in animals . Journal of experimental botany 59 , 1069 – 1080 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 18. ↵ Visconti A , et al. Interplay between the human gut microbiome and host metabolism . Nature communications 10 , 1 – 10 ( 2019 ). OpenUrl CrossRef PubMed 19. ↵ Chandler JA , Morgan Lang J , Bhatnagar S , Eisen JA , Kopp A . Bacterial communities of diverse Drosophila species: ecological context of a host–microbe model system . PLoS genetics 7 , e1002272 ( 2011 ). OpenUrl CrossRef 20. ↵ Boscaro V , et al. Microbiomes of microscopic marine invertebrates do not reveal signatures of phylosymbiosis . Nature Microbiology 7 , 810 – 819 ( 2022 ). OpenUrl CrossRef PubMed 21. ↵ Falkowski PG , et al. The evolution of modern eukaryotic phytoplankton . science 305 , 354 – 360 ( 2004 ). OpenUrl Abstract / FREE Full Text 22. ↵ Field CB , Behrenfeld MJ , Randerson JT , Falkowski P . Primary production of the biosphere: integrating terrestrial and oceanic components . science 281 , 237 – 240 ( 1998 ). OpenUrl Abstract / FREE Full Text 23. ↵ Landa M , et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux . The ISME journal 13 , 2536 – 2550 ( 2019 ). OpenUrl CrossRef PubMed 24. ↵ Bar-On YM , Milo R . The Biomass Composition of the Oceans: A Blueprint of Our Blue Planet . Cell 179 , 1451 – 1454 ( 2019 ). OpenUrl CrossRef PubMed 25. ↵ Buchan A , LeCleir GR , Gulvik CA , González JM . Master recyclers: features and functions of bacteria associated with phytoplankton blooms . Nature Reviews Microbiology 12 , 686 – 698 ( 2014 ). OpenUrl CrossRef PubMed 26. ↵ Croft MT , Lawrence AD , Raux-Deery E , Warren MJ , Smith AG . Algae acquire vitamin B12 through a symbiotic relationship with bacteria . Nature 438 , 90 – 93 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 27. ↵ Seymour JR , Amin SA , Raina J-B , Stocker R . Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships . Nature microbiology 2 , 1 – 12 ( 2017 ). OpenUrl 28. ↵ Fortin SG , Song B , Anderson IC , Reece KS . Blooms of the harmful algae Margalefidinium polykrikoides and Alexandrium monilatum alter the York River Estuary microbiome . Harmful Algae 114 , 102216 ( 2022 ). 29. ↵ Jackrel SL , Yang JW , Schmidt KC , Denef VJ . Host specificity of microbiome assembly and its fitness effects in phytoplankton . The ISME Journal 15 , 774 – 788 ( 2021 ). OpenUrl CrossRef PubMed 30. ↵ Teeling H , et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom . Science 336 , 608 – 611 ( 2012 ). OpenUrl Abstract / FREE Full Text 31. ↵ Kuhlisch C , Shemi A , Barak-Gavish N , Schatz D , Vardi A . Algal blooms in the ocean: hot spots for chemically mediated microbial interactions . Nature Reviews Microbiology 22 , 138 – 154 ( 2024 ). OpenUrl CrossRef PubMed 32. ↵ Deng Y , et al. Bacteria modulate microalgal aging physiology through the induction of extracellular vesicle production to remove harmful metabolites . Nature Microbiology 9 , 2356 – 2368 ( 2024 ). OpenUrl CrossRef PubMed 33. ↵ Sison-Mangus MP , Jiang S , Tran KN , Kudela RM . Host-specific adaptation governs the interaction of the marine diatom, Pseudo-nitzschia and their microbiota . The ISME journal 8 , 63 – 76 ( 2014 ). OpenUrl CrossRef PubMed 34. ↵ Behringer G , Ochsenkühn MA , Fei C , Fanning J , Koester JA , Amin SA . Bacterial Communities of Diatoms Display Strong Conservation Across Strains and Time . Frontiers in Microbiology 9 , ( 2018 ). 35. ↵ Mönnich J , Tebben J , Bergemann J , Case R , Wohlrab S , Harder T . Niche-based assembly of bacterial consortia on the diatom Thalassiosira rotula is stable and reproducible . The ISME Journal 14 , 1614 – 1625 ( 2020 ). OpenUrl CrossRef PubMed 36. ↵ Mars Brisbin M , Mitarai S , Saito MA , Alexander H. Microbiomes of bloom-forming Phaeocystis algae are stable and consistently recruited, with both symbiotic and opportunistic modes . The ISME Journal 16 , 2255 – 2264 ( 2022 ). OpenUrl CrossRef PubMed 37. ↵ Husnik F , Tashyreva D , Boscaro V , George EE , Lukeš J , Keeling PJ . Bacterial and archaeal symbioses with protists . Curr Biol 31 , R862 – R877 ( 2021 ). OpenUrl CrossRef PubMed 38. ↵ Ahern OM , Whittaker KA , Williams TC , Hunt DE , Rynearson TA . Host genotype structures the microbiome of a globally dispersed marine phytoplankton . Proc Natl Acad Sci USA 118 , e2105207118 ( 2021 ). OpenUrl Abstract / FREE Full Text 39. ↵ Cohen NR , et al. Dinoflagellates alter their carbon and nutrient metabolic strategies across environmental gradients in the central Pacific Ocean . Nature Microbiology 6 , 173 – 186 ( 2021 ). OpenUrl CrossRef PubMed 40. ↵ Zheng B , et al. Dinoflagellate vertical migration fuels an intense red tide . Proc Natl Acad Sci USA 120 , e2304590120 ( 2023 ). OpenUrl CrossRef PubMed 41. ↵ Bell W , Mitchell R . Chemotactic and growth responses of marine bacteria to algal extracellular products . The Biological Bulletin 143 , 265 – 277 ( 1972 ). OpenUrl CrossRef Web of Science 42. ↵ Deng Y , Wang K , Hu Z , Hu Q , Tang YZ . Identification and implications of a core bacterial microbiome in 19 clonal cultures laboratory-reared for months to years of the cosmopolitan dinoflagellate Karlodinium veneficum . Frontiers in Microbiology 13 , ( 2022 ). 43. ↵ Stock W , Willems A , Mangelinckx S , Vyverman W , Sabbe K . Selection constrains lottery assembly in the microbiomes of closely related diatom species . ISME communications 2 , 11 ( 2022 ). 44. ↵ Zou S , Yu X , Sun T , Wei L , Wu X . Unveiling bacterial communities linked with Alexandrium catenella, their impact and influential mechanism on paralytic shellfish toxin production . Algal Research 83 , 103698 ( 2024 ). 45. ↵ Bledsoe JW , Waldbieser GC , Swanson KS , Peterson BC , Small BC . Comparison of Channel Catfish and Blue Catfish Gut Microbiota Assemblages Shows Minimal Effects of Host Genetics on Microbial Structure and Inferred Function . Frontiers in Microbiology 9 , ( 2018 ). 46. ↵ Grieneisen L , et al. Gut microbiome heritability is nearly universal but environmentally contingent . Science 373 , 181 – 186 ( 2021 ). OpenUrl Abstract / FREE Full Text 47. ↵ Mallott EK , Amato KR . Host specificity of the gut microbiome . Nature Reviews Microbiology 19 , 639 – 653 ( 2021 ). OpenUrl CrossRef PubMed 48. ↵ Park BS , Kim J-H , Kim JH , Gobler CJ , Baek SH , Han M-S . Dynamics of bacterial community structure during blooms of Cochlodinium polykrikoides (Gymnodiniales , Dinophyceae) in Korean coastal waters. Harmful Algae 48 , 44 – 54 ( 2015 ). OpenUrl PubMed 49. ↵ Hattenrath-Lehmann TK , Gobler CJ . Identification of unique microbiomes associated with harmful algal blooms caused by Alexandrium fundyense and Dinophysis acuminata . Harmful Algae 68 , 17 – 30 ( 2017 ). OpenUrl CrossRef PubMed 50. ↵ Zhang X , et al. Seasonal dynamics of bacterial community and co-occurrence with eukaryotic phytoplankton in the Pearl River Estuary . Marine Environmental Research 192 , 106193 ( 2023 ). 51. ↵ Moran MA , et al. Ecological genomics of marine Roseobacters . Applied and environmental microbiology 73 , 4559 – 4569 ( 2007 ). OpenUrl Abstract / FREE Full Text 52. ↵ Newton RJ , et al. Genome characteristics of a generalist marine bacterial lineage . The ISME journal 4 , 784 – 798 ( 2010 ). OpenUrl CrossRef PubMed 53. ↵ Francis B , Urich T , Mikolasch A , Teeling H , Amann R . North Sea spring bloom-associated Gammaproteobacteria fill diverse heterotrophic niches . Environmental Microbiome 16 , 15 ( 2021 ). 54. ↵ Williams TJ , et al. The role of planktonic F lavobacteria in processing algal organic matter in coastal E ast A ntarctica revealed using metagenomics and metaproteomics . Environmental microbiology 15 , 1302 – 1317 ( 2013 ). OpenUrl CrossRef 55. ↵ Reeves AR , Wang G , Salyers AA . Characterization of four outer membrane proteins that play a role in utilization of starch by Bacteroides thetaiotaomicron . Journal of bacteriology 179 , 643 – 649 ( 1997 ). OpenUrl Abstract / FREE Full Text 56. ↵ González JM , et al. Genome analysis of the proteorhodopsin-containing marine bacterium Polaribacter sp. MED152 (Flavobacteria) . Proc Natl Acad Sci USA 105 , 8724 – 8729 ( 2008 ). OpenUrl Abstract / FREE Full Text 57. ↵ Green DH , Bowman JP , Smith EA , Gutierrez T , Bolch CJ . Marinobacter algicola sp. nov., isolated from laboratory cultures of paralytic shellfish toxin-producing dinoflagellates . International journal of systematic and evolutionary microbiology 56 , 523 – 527 ( 2006 ). OpenUrl CrossRef PubMed 58. ↵ Lawson CA , Raina JB , Kahlke T , Seymour JR , Suggett DJ . Defining the core microbiome of the symbiotic dinoflagellate, Symbiodinium . Environmental Microbiology Reports 10 , 7 – 11 ( 2018 ). OpenUrl CrossRef PubMed 59. ↵ Andelman JB , Suess MJ . Polynuclear aromatic hydrocarbons in the water environment . Bulletin of the World Health Organization 43 , 479 ( 1970 ). 60. ↵ Gunnison D , Alexander M . Basis for the resistance of several algae to microbial decomposition . Applied Microbiology 29 , 729 – 738 ( 1975 ). OpenUrl CrossRef PubMed Web of Science 61. ↵ Marlowe I , Green JC , Neal A , Brassell S , Eglinton G , Course P . Long chain (n-C37–C39) alkenones in the Prymnesiophyceae. Distribution of alkenones and other lipids and their taxonomic significance . British Phycological Journal 19 , 203 – 216 ( 1984 ). OpenUrl CrossRef 62. ↵ McKay WA , Turner MF , Jones BMR , Halliwell CM . Emissions of hydrocarbons from marine phytoplankton—Some results from controlled laboratory experiments . Atmospheric Environment 30 , 2583 – 2593 ( 1996 ). OpenUrl CrossRef 63. ↵ Park BS , Erdner DL , Bacosa HP , Liu Z , Buskey EJ . Potential effects of bacterial communities on the formation of blooms of the harmful dinoflagellate Prorocentrum after the 2014 Texas City “Y” oil spill (USA) . Harmful Algae 95 , 101802 ( 2020 ). 64. ↵ Yang Q , et al. Marinobacter alexandrii sp. nov., a novel yellow-pigmented and algae growth-promoting bacterium isolated from marine phycosphere microbiota . Antonie Van Leeuwenhoek 114 , 709 – 718 ( 2021 ). OpenUrl CrossRef PubMed 65. ↵ Hernandez DJ , David AS , Menges ES , Searcy CA , Afkhami ME . Environmental stress destabilizes microbial networks . The ISME Journal 15 , 1722 – 1734 ( 2021 ). OpenUrl CrossRef PubMed 66. ↵ Ajani PA , Kahlke T , Siboni N , Carney R , Murray SA , Seymour JR . The microbiome of the cosmopolitan diatom Leptocylindrus reveals significant spatial and temporal variability . Frontiers in microbiology 9 , 2758 ( 2018 ). 67. ↵ Medlin LK . Evolution of the diatoms: major steps in their evolution and a review of the supporting molecular and morphological evidence . Phycologia 55 , 79 – 103 ( 2016 ). OpenUrl CrossRef 68. ↵ Medlin LK , Desdevises Y . Phylogenetic reconstruction of diatoms using a seven-gene dataset, multiple outgroups, and morphological data for a total evidence approach . Phycologia 59 , 422 – 436 ( 2020 ). OpenUrl CrossRef 69. ↵ Lewin-Epstein O , Hadany L . Host–microbiome coevolution can promote cooperation in a rock–paper– scissors dynamics . Proceedings of the Royal Society B 287 , 20192754 ( 2020 ). 70. ↵ Richardson LA . Evolving as a holobiont . PLoS biology 15 , e2002168 ( 2017 ). OpenUrl CrossRef PubMed 71. ↵ Franzenburg S , et al. Distinct antimicrobial peptide expression determines host species-specific bacterial associations . Proc Natl Acad Sci USA 110 , E3730 – E3738 ( 2013 ). OpenUrl Abstract / FREE Full Text 72. ↵ McLoughlin K , Schluter J , Rakoff-Nahoum S , Smith AL , Foster KR . Host selection of microbiota via differential adhesion . Cell host & microbe 19 , 550 – 559 ( 2016 ). OpenUrl CrossRef PubMed 73. ↵ Schluter J , Foster KR . The evolution of mutualism in gut microbiota via host epithelial selection . PLoS biology 10 , e1001424 ( 2012 ). OpenUrl CrossRef PubMed 74. ↵ Shibl AA , et al. Diatom modulation of select bacteria through use of two unique secondary metabolites . Proc Natl Acad Sci USA 117 , 27445 – 27455 ( 2020 ). OpenUrl Abstract / FREE Full Text 75. ↵ Fu H , Uchimiya M , Gore J , Moran MA . Ecological drivers of bacterial community assembly in synthetic phycospheres . Proc Natl Acad Sci USA 117 , 3656 – 3662 ( 2020 ). OpenUrl Abstract / FREE Full Text 76. ↵ Landa M , Burns AS , Roth SJ , Moran MA . Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom . The ISME Journal 11 , 2677 – 2690 ( 2017 ). OpenUrl CrossRef PubMed 77. ↵ Bolch CJ , Subramanian TA , Green DH . The toxic dinoflagellate Gymnodinium catenatum (Dinophyceae) requires marine bacteria for growth 1 . J Phycol 47 , 1009 – 1022 ( 2011 ). OpenUrl CrossRef PubMed 78. ↵ Zhou J , Ning D . Stochastic Community Assembly: Does It Matter in Microbial Ecology? Microbiology and Molecular Biology Reviews 81 , doi: 10.1128/mmbr.00002-00017 ( 2017 ). OpenUrl CrossRef 79. ↵ Hubbell SP . The unified neutral theory of biodiversity and biogeography (MPB-32) . Princeton University Press ( 2011 ). 80. ↵ Stegen JC , Lin X , Konopka AE , Fredrickson JK . Stochastic and deterministic assembly processes in subsurface microbial communities . The ISME journal 6 , 1653 – 1664 ( 2012 ). OpenUrl CrossRef PubMed 81. ↵ Stegen JC , et al. Quantifying community assembly processes and identifying features that impose them . The ISME journal 7 , 2069 – 2079 ( 2013 ). OpenUrl CrossRef PubMed 82. ↵ Fargione J , Brown CS , Tilman D . Community assembly and invasion: an experimental test of neutral versus niche processes . Proc Natl Acad Sci USA 100 , 8916 – 8920 ( 2003 ). OpenUrl Abstract / FREE Full Text 83. ↵ Chave J. Neutral theory and community ecology . Ecology letters 7 , 241 – 253 ( 2004 ). OpenUrl CrossRef Web of Science 84. ↵ Chase JM . Stochastic community assembly causes higher biodiversity in more productive environments . Science 328 , 1388 – 1391 ( 2010 ). OpenUrl Abstract / FREE Full Text 85. ↵ Dini-Andreote F , Stegen JC , Van Elsas JD , Salles JF . Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession . Proc Natl Acad Sci USA 112 , E1326 – E1332 ( 2015 ). OpenUrl Abstract / FREE Full Text 86. ↵ Kohl KD . Ecological and evolutionary mechanisms underlying patterns of phylosymbiosis in host-associated microbial communities . Philosophical Transactions of the Royal Society B: Biological Sciences 375 , 20190251 ( 2020 ). 87. ↵ Lim SJ , Bordenstein SR . An introduction to phylosymbiosis . Proceedings of the Royal Society B 287 , 20192900 ( 2020 ). 88. ↵ O’Brien PA , et al. Diverse coral reef invertebrates exhibit patterns of phylosymbiosis . The ISME Journal 14 , 2211 – 2222 ( 2020 ). OpenUrl CrossRef PubMed 89. ↵ 89. Guillard RR. Culture of phytoplankton for feeding marine invertebrates . In: Culture of marine invertebrate animals (ed Smith WL , Chanley , M.H. ). Springer US ( 1975 ). 90. ↵ Park BS , Kim S , Kim J-H , Ho Kim J , Han M-S . Dynamics of Amoebophrya parasites during recurrent blooms of the ichthyotoxic dinoflagellate Cochlodinium polykrikoides in Korean coastal waters . Harmful Algae 84 , 119 – 126 ( 2019 ). OpenUrl CrossRef PubMed 91. ↵ Kim H-J , et al. Co-occurrence between key HAB species and particle-attached bacteria and substrate specificity of attached bacteria in the coastal ecosystem . Harmful Algae 138 , 102700 ( 2024 ). 92. ↵ Xu S , He C , Song S , Li C . Spatiotemporal dynamics of marine microbial communities following a Phaeocystis bloom: biogeography and co□occurrence patterns . Environmental Microbiology Reports 13 , 294 – 308 ( 2021 ). OpenUrl CrossRef PubMed 93. ↵ Xu S , He C , Kang Z , Song S , Li C . The ecological responses of bacterioplankton during a Phaeocystis globosa bloom in Beibu Gulf, China highlighted by integrated metagenomics and metatranscriptomics . Mar Biol 169 , 36 ( 2022 ). 94. ↵ Park BS , Baek SH , Ki J-S , Cattolico RA , Han M-S . Assessment of EvaGreen-based quantitative real-time PCR assay for enumeration of the microalgae Heterosigma and Chattonella (Raphidophyceae) . J Appl Phycol 24 , 1555 – 1567 ( 2012 ). OpenUrl CrossRef 95. ↵ Chen S , Zhou Y , Chen Y , Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor . Bioinformatics 34 : i884 – i890 .) ( 2018 ). OpenUrl CrossRef PubMed 96. ↵ Magoč T , Salzberg SL . FLASH: fast length adjustment of short reads to improve genome assemblies . Bioinformatics 27 , 2957 – 2963 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 97. ↵ Caporaso JG , et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms . The ISME journal 6 , 1621 – 1624 ( 2012 ). OpenUrl CrossRef PubMed 98. ↵ Edgar RC . Search and clustering orders of magnitude faster than BLAST . Bioinformatics 26 , 2460 – 2461 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 99. ↵ Liu YX , et al. EasyAmplicon: An easy□to□use, open□source, reproducible, and community□based pipeline for amplicon data analysis in microbiome research . IMeta 2 , e83 ( 2023 ). OpenUrl CrossRef 100. ↵ Quast C , et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools . Nucleic Acids Res 41 , D590 – D596 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 101. ↵ McMurdie PJ , Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data . PloS one 8 , e61217 ( 2013 ). OpenUrl CrossRef PubMed 102. ↵ Xu S , et al. MicrobiotaProcess: A comprehensive R package for deep mining microbiome . The Innovation 4 , ( 2023 ). 103. ↵ Oksanen J , et al. Package ‘vegan’ . Community ecology package, version 2 , 1 – 295 ( 2013 ). OpenUrl 104. ↵ Wickham H . ggplot2 . Wiley interdisciplinary reviews: computational statistics 3 , 180 – 185 ( 2011 ). OpenUrl CrossRef 105. ↵ Faul F , Erdfelder E , Lang A-G , Buchner A . G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences . Behavior research methods 39 , 175 – 191 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 106. ↵ Wen T , et al. ggClusterNet: An R package for microbiome network analysis and modularity□based multiple network layouts . Imeta 1, e32 ( 2022 ). 107. ↵ Louca S , Parfrey LW , Doebeli M . Decoupling function and taxonomy in the global ocean microbiome . Science 353 , 1272 – 1277 ( 2016 ). OpenUrl Abstract / FREE Full Text 108. ↵ Ning D , Deng Y , Tiedje JM , Zhou J . A general framework for quantitatively assessing ecological stochasticity . Proc Natl Acad Sci USA 116 , 16892 – 16898 ( 2019 ). OpenUrl Abstract / FREE Full Text 109. ↵ Tamura K , Nei M . Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees . Mol Biol Evol 10 , 512 – 526 ( 1993 ). OpenUrl CrossRef PubMed Web of Science 110. ↵ Felsenstein J . Confidence limits on phylogenies: an approach using the bootstrap . evolution 39 , 783 – 791 ( 1985 ). OpenUrl CrossRef GeoRef PubMed Web of Science 111. ↵ Tamura K , Stecher G , Kumar S . MEGA11: molecular evolutionary genetics analysis version 11 . Mol Biol Evol 38 , 3022 – 3027 ( 2021 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted February 16, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Phytoplankton recruitment of specific microbial assemblages and phylosymbiotic patterns Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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Share Phytoplankton recruitment of specific microbial assemblages and phylosymbiotic patterns Joo-Hwan Kim , Jin Ho Kim , Zhun Li , Hyeon Ho Shin , Young Kyun Lim , Seung Ho Baek , Pengbin Wang , Ro Young Park , Soo Hwan Jin , Myung-Soo Han , Bum Soo Park bioRxiv 2025.02.10.637405; doi: https://doi.org/10.1101/2025.02.10.637405 Share This Article: Copy Citation Tools Phytoplankton recruitment of specific microbial assemblages and phylosymbiotic patterns Joo-Hwan Kim , Jin Ho Kim , Zhun Li , Hyeon Ho Shin , Young Kyun Lim , Seung Ho Baek , Pengbin Wang , Ro Young Park , Soo Hwan Jin , Myung-Soo Han , Bum Soo Park bioRxiv 2025.02.10.637405; doi: https://doi.org/10.1101/2025.02.10.637405 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Ecology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17691) Bioengineering (13892) Bioinformatics (41937) Biophysics (21452) Cancer Biology (18588) Cell Biology (25504) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88605) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15156) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)