Abstract
The giant clam-Symbiodiniaceae symbiosis represents a classic mutualism in coral reef ecosystems, driving photosynthesis and nutrient cycling that fuel reef productivity. However, it remains unclear how association with a host influences the composition and stability of Symbiodiniaceae communities, and the extent to which these communities reflect or diverge from the surrounding free-living pool. Addressing this question, high throughput Symbiodiniaceae ribosome DNA Internal Transcribed Spacer 2 (ITS2) metabarcoding sequencing was performed in 48 seawater and Tridacna maxima mantle samples collected across three atolls of the Nansha Archipelago in the South China Sea. Compared with the highly diverse free-living pool in the surrounding water (680 amplicon sequence variants, ASVs; 11 ITS2 types), symbiotic communities exhibited significantly lower number of Symbiodiniaceae ITS2 types (247 ASVs; five ITS2 types). Free-living communities differed markedly among reefs primarily driven by geographical distance, and concentrations of various nutrients and heavy metals, whereas clam-associated communities remained spatially homogeneous. Symbionts were dominated by Cladocopium C1 and Symbiodinium A3, reflecting the host’s selection of Symbiodiniaceae. Moreover, clam-associated communities were predominantly shaped by deterministic processes and exhibited higher community stability, whereas free-living communities were co-influenced by deterministic and stochastic processes. This low species diversity yet high stability of symbiotic communities implies functional diversity among dominant genera might play a key role in maintaining holobiont resilience, whose stability could be reinforced by the buffered microenvironment created by the giant clam. These findings highlight the importance of host-mediated structuring of Symbiodiniaceae communities for holobiont stability and suggest that maintaining host-symbiont integrity maybe important for reef ecosystem resilience under environmental change.
Introduction
Coral reefs are among Earth’s most productive ecosystems harboring extraordinary biodiversity and underpinning vital economic services (). One of the key engines driving this high productivity is the obligate photosymbiosis between invertebrates and dinoflagellate micro-algae of the family Symbiodiniaceae (also called zooxanthellae), which can translocate more than 90% of their fixed carbon to the hosts under well-lit conditions (; ). However, studies have warned that global climate change could increase the frequency of bleaching events, where temperature stressors cause a breakdown of the symbiosis between hosts and Symbiodiniaceae, and threaten the long-term viability of coral reefs (; ; ). Therefore, elucidating the diversity and specificity of Symbiodiniaceae hosted by invertebrates is important for understanding the holobiont mutualism evolution and its adaptive potential under environmental change.
The family Symbiodiniaceae is phylogenetically diverse, including 11 recognized genera and potentially hundreds of species, only a fraction of which have been formally described to date (, ; ; ). Undescribed lineages are often referred to by alphanumeric designations based on their ITS2 rDNA marker profiles, though resolution with this marker is not perfect (). Unlike their obligately symbiotic counterparts, free-living Symbiodiniaceae exist independently of metazoan hosts in various marine microhabitats, including the water column, sediment, macroalgal beds and fish feces (; ; ). However, some of these free-living taxa are transient and capable of forming symbioses with invertebrates. Biogeographic surveys have revealed that free-living Symbiodiniaceae in seawater exhibit high diversity and pronounced spatial structure with rapid turnover along latitudinal, longitudinal and environmental gradients (), whereas the coral-associated Symbiodiniaceae assemblages show relatively low diversity and a distinct community assembly and stability (). Photosymbiosis diversity has also been well characterized in the sea anemone Anthopleura elegantissima, where symbiont composition is primarily determined by latitude, with the resulting biogeographic gradient coinciding with both temperature and irradiance (). Indeed most of the knowledge of photosymbiont distributions comes from cnidarians which form strict intracellular symbiosis with the microalgae.
Giant clams of Tridacna represent a classic extracellular symbiosis with Symbiodiniaceae and are widely established as a non-cnidarian model for investigating symbiont acquisition and specificity (; ; ). Symbionts reside in the tubular structures of the mantle where they photosynthesize and translocate nutrients to the giant clams (; ). The location of Symbiodiniaceae within giant clams may confer relatively greater protection from environmental fluctuations when compared to corals (). Giant clams acquire Symbiodiniaceae horizontally from ambient seawater in the early ontogeny (; ; ), and can re-establish symbiont populations following bleaching (; ). Consequently, symbionts community composition within clams is influenced by the composition of the free-living pool. Moreover, all giant clam species are protected species under Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), and several species are listed in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species (Version 2025-2, https://www.iucnredlist.org). Given the threatened and endangered status of giant clams, it is urgent to deeply resolve the diversity and distribution of both clam-associated and free-living Symbiodiniaceae in their natural habitats. Among tridacnid species, T. maxima exhibits the broadest geographic distribution (; ; ) and is therefore an ideal system for investigating patterns of host-symbiont selectivity across environmental gradients (; ).
Coral reefs in the South China Sea (SCS) represent one of the most biodiverse but understudied reef systems in the Central Indo-Pacific (). The Nansha Archipelago (NS), the largest archipelago in the SCS, lies at its southernmost margin. Remote distance and restrict access have left NS coral reefs chronically under surveyed when compared with other archipelagos in the SCS (). Symbiodiniaceae diversity assessments have concentrated almost exclusively on the northern SCS (), rendering the NS a critical knowledge gap. Filling this geographic gap is essential to evaluate how environmental gradients influence Symbiodiniaceae composition and host-symbiont interactions across the SCS.
Here, we investigate how host association shapes the composition, diversity, and stability of Symbiodiniaceae communities in T. maxima, and how these host-associated communities relate to the surrounding free-living pool. We further examine how environmental gradients across the NS influence both free-living and host-associated communities, providing insight into the ecological mechanisms that structure symbiont assemblages. This study therefore aims to clarify the roles of host identity and environmental factors in shaping Symbiodiniaceae community assembly, with implications for understanding holobiont resilience in coral reef ecosystems.
Materials and methods
Sample collection and measurement of environmental factors
In May 2024, Symbiodiniaceae were collected from three reef regions of the NS (Yongshu reef, YS; Ximen reef, XM and Meji reef, MJ; Figure 1). Specifically, from each region, 8 water samples and 8 T. maxima samples were collected from a depth of 2–10 m. Mantle tissues (approximately 0.50 cm2) of T. maxima were collected via SCUBA in-situ (; ). Bottom seawater were collected using a Niskin bottle, with 2 L of seawater taken per sample. The seawater was pre-filtered through a 200 μm mesh, followed by vacuum filtration onto a 0.22 μm pore-size polycarbonate membrane (Millipore, USA). Both mantles (samples abbreviated as YST, XMT, and MJT) and water filters (samples abbreviated as YSW, XMW, and MJW) were immediately flash-frozen in liquid nitrogen and stored at -80 °C.
Figure 1
Sea surface temperature (SST) data for each sampling site were obtained from the National Marine Data Center and the National Science and Technology Resource Sharing Service Platform of China at a spatial resolution of approximately 5 km (http://mds.nmdis.org.cn). Inorganic nutrient concentrations, including potassium ions (K+), nitrate-nitrogen (NO3-N), ammonium-nitrogen (NH4-N), phosphorus (P), total phosphorus (TP), and total nitrogen (TN) of the water were measured after collection and filtration on board following the protocols outlined in the Specifications for Oceanographic Survey in China (GB/T 12763.4-2007) and Methods of Seawater Analysis (). Concentrations of heavy metal elements, including nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), and lead (Pb) were analyzed using an inductively coupled plasma mass spectrometry and an atomic fluorescence spectrometer (AFS-920).
DNA extraction and PCR amplification
Total DNA was extracted from each mantle sample using the E.Z.N.A.® Tissue DNA Kit (Omega), and water filter using the FastDNA® SPIN Kit for Soil (MP Biomedicals, California, USA) according to the manufacturer’s instructions in each case. The ITS2 region of ribosomal DNA was amplified with Symbiodiniaceae specific primers ITSintFor2 (5’-GAATTGCAGAACTCCGTG-3’) () and ITS2-reverse (5’-GGGATCCATATGCTTAAGTTCAGCGGGT-3’) (). PCR was performed in a 50 µL volume containing approximately 50 ng template DNA, 25 μL of 2× Taq Platinum PCR Master Mix, 0.2 µM of each primer, and ddH2O filling the system to 50 μL. PCR conditions were as follows: initial denaturation at 94 °C for 5 min, followed by 35 cycles of 94 °C for 30 s, 51 °C for 30 s, and 72 °C for 30 s, with a final extension at 72 °C for 5 min. Subsequently, 3 µL of PCR product was electrophoresed on a 2% agarose gel to confirm the amplicon size. Successfully amplified Symbiodiniaceae ITS2 products were purified using the QIAquick PCR Purification Kit (QIAGEN, Hilden, Germany).
High-throughput sequencing, data processing and annotation
Sequencing libraries were constructed using the NEXTFLEX® Rapid DNA-Seq Kit according to the manufacturer’s protocol. Paired-end 250 bp sequencing was performed on an Illumina NovaSeq 6000 platform (Illumina, USA). Raw sequencing data were demultiplexed according to their unique tags for each sample. Sequences were then processed using the DADA2 pipeline (v1.30.1) () in R (v4.4.2) (). DADA2 was employed for Amplicon Sequence Variant (ASV) inference due to its ability to learn error rates from the data and resolve true biological sequences at single-nucleotide resolution, thereby reducing spurious taxa compared to threshold-based OTU clustering methods (; , ). Sequence quality was initially assessed to determine appropriate filtering parameters. Paired-end reads were subsequently quality-filtered using the filterAndTrim function to remove sequences containing ambiguous bases (N) and truncate low-quality ends at specified lengths. The filtered reads were then used to infer sequencing error models, with error rates estimated separately for forward and reverse reads. Following dereplication of the filtered sequences, the paired-end reads were denoised and merged to generate ASVs for each sample. Then chimeric sequences were removed using the removeBimeraDenovo function.
After obtaining the ASV representative sequences, all ASV sequences were searched against the NCBI non-redundant nucleotide (nt) database with Blast (v2.10.0) with a query coverage of 95% and E-value<1e-5 (). Only ASVs annotated as Symbiodiniaceae ITS2 were retained. The annotation results were manually checked, and misannotations were removed through manual verification.
To minimize the impact of sequencing depth variation on downstream analyses, all samples were rarefied to the read count of the sample with the lowest sequencing depth using the “vegan” package (v2.6.10) in R (). The resulting ASV abundance table was used for subsequent analyses.
Phylogenetic analysis
The ASV sequences were first aligned in the R by “msa” package (v1.38.0, Bioconductor), which interfaced with MAFFT for multiple sequence alignment. Maximum-likelihood phylogenetic inference was subsequently performed with IQ-TREE (v2.4.0.). Model selection was carried out using the built-in ModelFinder algorithm (-m MFP), and the optimal nucleotide substitution model (TIM+F+I+G4) was determined according to the Bayesian Information Criterion (BIC). Phylogenetic tree reconstruction was conducted under this best-fit model, with nodal support values assessed through 1000 ultrafast bootstrap approximations (-bb 1000).
Diversity and community structure analyses
Alpha diversity indices for Symbiodiniaceae community at each sampling site were calculated using the “vegan” package (v2.6.10). ASV diversity and richness was assessed with the Shannon index () and the Observed richness index Chao1 estimator (), respectively. Differences in alpha diversity among samples from different reef regions were tested for significance using the Kruskal-Wallis rank-sum test ().
For beta diversity analysis, principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity was performed to ordinate and visualize differences in Symbiodiniaceae community composition among samples, with plots generated using the “ggplot2” (v4.0.0) package. Significant differences in community structure among samples from different reef regions were evaluated using permutational multivariate analysis of variance (PERMANOVA) implemented in the “vegan” package (v2.6.10) accordingly ().
Community composition was determined based on taxonomic annotation of the Symbiodiniaceae ASVs. Stacked bar plots illustrating the relative abundance of Symbiodiniaceae genera in each sample using the “qiime taxa barplot” command in QIIME2 (). Relative abundances of the dominant ITS2 sequences in each sample were visualized as a heatmap with the “pheatmap” package (v1.0.12) in R. “DESeq2” package (v1.46.0) () was used to identify ASVs with significantly different abundances between clam-associated and free-living communities (Fold Change>2, adjusted P-value<0.05).
Correlation analyses between community composition and environmental variables
To address potential multicollinearity among environmental variables, variance inflation factor (VIF) analysis was conducted, and only variables with VIF values<10 were retained for subsequent analyses (). The relative contributions of individual environmental factors on Symbiodiniaceae community were examined using canonical correspondence analysis (CCA) ().
Community compositional differences were first quantified using a Bray-Curtis dissimilarity matrix. Environmental distance was calculated as the Euclidean distance among samples from standardized environmental variables, whereas geographic distance was calculated from sampling-site longitude and latitude coordinates as great-circle distances. Mantel tests and partial Mantel tests based on Pearson correlation coefficients were then performed to assess correlations between community structure differences (Bray-Curtis dissimilarity matrix) and geographic distance or environmental distance (Euclidean distances matrix) (; ; ). Mantel test were further employed to evaluate relationships between alpha diversity indices (Shannon index and Chao1), the relative abundances of key genera (Symbiodinium, Cladocopium, Durusdinium), and environmental factors. All analyses were performed in R (v4.4.2) using the “vegan” (v2.6.10), “linkET” (v0.1.0) and “ggplot2” (v4.0.0) packages.
Community assembly process and stability analyses
The iCAMP framework was employed to evaluate the relative contributions of distinct ecological processes by analyzing phylogenetic relationships across different communities. These processes encompass dispersal limitation (DL), ecological drift (DR), homogenizing dispersal (HD), heterogeneous selection (HeS), and homogeneous selection (HoS). Phylogenetic dissimilarity was quantified using the beta net relatedness index (βNRI), whereas taxonomic dissimilarity was assessed with the Raup-Crick metric based on Bray-Curtis dissimilarity (RCBray). According to the predefined thresholds in the iCAMP framework, variable selection was inferred when βNRI>+1.96 and homogeneous selection when βNRI<−1.96. When |βNRI|≤1.96, dispersal limitation was identified if RCBray>+0.95 and homogenizing dispersal if RCBray<−0.95, while ecological drift was inferred when both |βNRI|≤1.96 and |RCBray|≤0.95. The relative contribution of each ecological process was subsequently determined by weighting the results from all bins according to their relative abundances, and these weighted values were summarized to estimate the overall importance of each process at the community level. Compared to conventional community-level approaches, this phylogenetic-based method provides superior quantitative performance (). The proportions of DL, HD, and DR were primarily regarded as stochastic, and their combined relative importance could be used to quantify the overall stochasticity of community assembly (). All analyses were conducted using the “iCAMP” package (v1.5.12) in R following the protocols described by .
To further explore community stability among free-living and clam-associated Symbiodiniaceae, average variation degree (AVD) was evaluated for each community using the base R function. Lower AVD value indicates higher community stability. Firstly, the variation degree for each ASV, ai was calculated using the following Equation 1, where xi represents the rarefied abundance of that ASV in a sample, is the mean rarefied abundance of the ASV in the sample group, and δi is the standard deviation of the rarefied abundances of the ASV within the sample group.
And the AVD of a sample is the mean of these normalized deviations across all ASVs, which was calculated by the following Equation 2, where k is the number of samples in a group and n is the number of ASVs in each sample group.
Here, 1-AVD values were used to explore the stability with higher values indicating higher community stability, which was performed according to ().
Results
Diversity of free-living and clam-associated Symbiodiniaceae across NS
High-throughput sequencing yielded a total of 3,958,686 raw reads across 48 samples, ranging from 43,761 to 135,904 reads per sample. Following quality filtering and rarefaction, a total of 906 ASVs (Supplementary Table 1) and 46 samples were retained for downstream analyses. No significant differences in alpha diversity indices were detected among MJ, YS and XM Reefs for either free-living or clam-associated Symbiodiniaceae communities, except that the Shannon index was significantly higher in free-living Symbiodiniaceae community at XMW compared to YSW (P < 0.05) (Figure 2A). When comparing free-living and clam-associated Symbiodiniaceae (Figure 2B), 680 ASVs were detected in free-living community, while 247 ASVs were identified in symbiotic communities. Only 21 ASVs were shared by both communities (Figure 2B). No significant difference was detected in ASV diversity (Shannon index), but the ASV richness (Chao1) was higher in the symbiotic community than that of the free-living community (P < 0.05).
Figure 2
Spatial distribution patterns of free-living and clam-associated Symbiodiniaceae across NS
PCoA based on Bray-Curtis dissimilarity revealed significant differences in free-living Symbiodiniaceae community structure among MJW, YSW and XMW, which was confirmed by PERMANOVA (P = 0.004) (Figure 2C; Supplementary Table 2). Further pairwise PERMANOVA comparisons revealed significant dissimilarity between MJW and XMW (P = 0.001) and between YSW and XMW (P = 0.002), whereas MJW and YSW did not differ significantly (P = 0.344) (Supplementary Table 2). In contrast, no significant difference was detected by PERMANOVA (P = 0.477) in clam-associated communities, and samples from MJT, YST and XMT overlapped extensively in PCoA (Figure 2C; Supplementary Table 2).
Taxonomic annotation identified six genera of Symbiodiniaceae in all samples, which were Cladocopium (390 ASVs), Effrenium (226 ASVs), Durusdinium (181 ASVs), Symbiodinium (69 ASVs), Gerakladium (36 ASVs) and Fugacium (4 ASVs). Phylogenetic analysis of the ASVs further supported the resolution of the six Symbiodiniaceae genera (Supplementary Figure 1). Free-living communities showed high compositional variability among reefs (Figure 3A), including 11 ITS types which were Cladocopium C1 (271 ASVs), C3 (119 ASVs), Durusdinium D1a (D. trenchii, 180 ASVs), D1 (D. glynnii, 1 ASV), Symbiodinium A3 (likely S. tridacnidorum, 69 ASVs), Effrenium E1 (E. voratum, 226 ASVs), Fugacium F4.8 (2 ASVs), F7 (2 ASVs), Gerakladium G1 (1 ASV), G4 (26 ASVs) and G6 (9 ASVs). In free-living communities of MJW, Durusdinium (40.36%) and Cladocopium (30.46%) were co-dominant, followed by Effrenium (27.10%) and Gerakladium (2.07%). In YSW, Effrenium was most abundant (71.52%), followed by Durusdinium (20.40%), Cladocopium (4.28%) and Gerakladium (3.76%). In XMW, Cladocopium (81.60%) was dominated, followed by Durusdinium (9.43%), Effrenium (5.97%) and Gerakladium (2.62%). In contrast, only C1 (146 ASVs), C3 (22 ASVs), D1a (10 ASVs), A3 (68 ASVs) and G4 (1 ASV) were detected in clam-associated communities. Cladocopium (57.55% in MJT, 59.78% in XMT, 76.48% in YST) and Symbiodinium (42.18% in MJT, 40.21% in XMT, 23.51% in YST) were the key components of the symbiotic communities in all reefs (Figure 3B). In MJT, the abundance of Durusdinium was relatively higher compared to YST and XMT, accounting for 0.26% of the community. Overall, whereas free-living Symbiodiniaceae communities across NS reefs displayed variable dominance among Cladocopium, Durusdinium and Effrenium, symbiotic communities in T. maxima were overwhelmingly dominated by Cladocopium and Symbiodinium.
Figure 3
Below the genus level, symbiotic Symbiodiniaceae communities were enriched in ITS2 types C1 and A3, whereas free-living Symbiodiniaceae communities exhibited higher abundances of C3, D1a and E1 (Figure 3C). More specifically, 63 ASVs exhibited significantly different abundances between symbiotic and free-living Symbiodiniaceae (Figure 3D). Many A3 and C1 ASVs had significantly higher abundances in symbiotic Symbiodiniaceae communities, while three E1 and two D1a ASVs were significantly more abundant in free-living Symbiodiniaceae communities.
Relationships between Symbiodiniaceae community structures and environmental factors
Mantel tests revealed significant correlations between free-living community dissimilarity and geographical distance (R = 0.20, P < 0.01), as well as between free-living community dissimilarity and environmental variables (R = 0.20, P = 0.02) (Table 1). Partial mantel tests confirmed that either geographical distance (R = 0.17, P = 0.01) or environmental variables (R = 0.18, P = 0.02) remained significant after controlling the other. In symbiotic communities, neither geographical (P = 0.55) nor environmental distance (P = 0.17) was significantly correlated with dissimilarity (Table 1). The CCA further constrained free-living community structure in relation to environmental variables. CCA1 and CCA2 explained 18.64% and 17.61% of the constrained variation, respectively. The variables SST, K, NO3-N, TN and Cu showed strong associations with community structure, as indicated by longer vector lengths and alignment with the CCA1 axis, suggesting these variables were important drivers of variation in free-living Symbiodiniaceae communities (Figure 4A).
Table 1
| Community | Test | Variables | Partial control | R | P |
|---|---|---|---|---|---|
| Symbiodiniaceae community in seawater | Mantel test | Geographic distance | 0.20 | <0.01 | |
| Environmental variables | 0.20 | 0.02 | |||
| Partial Mantel Test | Geographic distance | Environmental variables | 0.17 | 0.01 | |
| Environmental variables | Geographic distance | 0.18 | 0.02 | ||
| Clam-associated Symbiodiniaceae community | Mantel test | Geographic distance | -0.02 | 0.55 | |
| Environmental variables | 0.06 | 0.17 | |||
| Partial Mantel test | Geographic distance | Environmental variables | -0.03 | 0.65 | |
| Environmental variables | Geographic distance | 0.07 | 0.18 |
Correlation between the dissimilarity of Symbiodiniaceae community structures and geographic distances or environmental variables using mantel test and partial mantel test.
Figure 4
Moreover, in free-living assemblages, the Shannon index and Chao1 were significantly correlated with various nutrients and heavy metals, such as K, Cu and Ni (Pearson correlation analysis, P < 0.01). Additionally, Symbiodinium abundance was correlated with TN, Ni, Cu, Cd and Pb (Pearson correlation analysis, P < 0.05), whereas Cladocopium and Durusdinium abundances showed no significant environmental associations (Figure 4B). In symbiotic assemblages, Chao1 index was significantly correlated with TN, Ni, Cu, Cd and Pb (Pearson correlation analysis, P < 0.05), whereas Cladocopium, Symbiodinium and Durusdinium abundances showed no significant environmental associations (Figure 4C).
Community assembly mechanisms and stability of free-living and clam-associated Symbiodiniaceae communities
Community assembly analyses showed that free-living community composition was driven by deterministic processes (49.34%, primarily HoS and HeS) and stochastic processes (50.66%, primarily DL and DR), while clam-associated Symbiodiniaceae communities were dominated by deterministic processes (99.30%, primarily HoS) (Figure 5A). Further AVD analyses indicated great compositional stability in clam-associated Symbiodiniaceae communities compared to free-living communities. Symbiotic communities exhibited significantly lower AVD values (P < 0.001), corresponding to higher stability indices (Figure 5B).
Figure 5
Discussion
The symbiotic association between T. maxima and Symbiodiniaceae represents a classic example of mutualism in coral reef ecosystems, in which the host provides protected habitat and inorganic nutrients while receiving photosynthetically fixed carbon from Symbiodiniaceae. This study fills a critical gap concerning Symbiodiniaceae communities in the NS region of the SCS and those symbiotic with giant clams. It has been revealed that despite a spatially heterogeneous environmental Symbiodiniaceae pool, T. maxima consistently host a Cladocopium-Symbiodinium consortium that varies little among reefs within the NS region, reflecting strong host selection, and a markedly more stable community relative to the free-living assemblage.
Environmentally driven spatial distribution patterns of free-living Symbiodiniaceae
Our results show that the free-living Symbiodiniaceae communities of NS are dominated by Cladocopium, Effrenium, Durusdinium and Gerakladium. The high abundance of Cladocopium and Durusdinium is consistent with previous findings from the Xisha Archipelago of the SCS, and however, no Effrenium was detected in Xisha (). Cladocopium and Durusdinium appear to remain the most abundant genera across the SCS. Both genera also prevail in water column of the Southern Great Barrier Reef in the western Pacific (). Nevertheless, at the global scale, free-living Symbiodiniaceae communities in reef sediments exhibit distinct biogeographic patterns (). For instance, free-living Fugacium is more abundant in the northern Gulf of Mexico (in the Atlantic) than in the Pacific. These large-scale biogeographical differences can likely be attributed to historical selection during the Plio-Pleistocene glaciation, and mirror local environments (; ; ). Moreover, differential abundance analysis revealed significant enrichment of Effrenium ASVs in the free-living Symbiodiniaceae communities in our study. This pattern is highly consistent with the results of , in which sediment samples from Great Barrier Reef were significantly enriched in Effrenium, indicating that E. voratum represents typical free-living members in reef environments.
Pronounced spatial heterogeneity has been observed in free-living Symbiodiniaceae communities across reefs in this study. Specifically, the community structure at XMW significantly differs from that at MJW and YSW, driven primarily by geographic distance and environmental factors such as SST, NO3-N, TN, K and Cu (Figure 3A). These factors might collectively influence Symbiodiniaceae physiology through their roles in thermal tolerance, nitrogen metabolism, photosynthetic electron transport and trace metal homeostasis, thereby shaping free-living community composition in the water column (); ; ). Moreover, the differing Symbiodiniaceae communities between XMW and the other two reefs may also be attributed to their difference in hydrodynamic and sedimentary environments. The submerged setting of Ximen reef exposes it to high-energy monsoonal waves, coarse-grained storm deposits, and rapid water exchange, contrasting with Yongshu Atoll’s and Meiji Atoll’s semi-enclosed and low-energy lagoon which are characterized by fine sediment retention and elevated turbidity (; ).
Host specificity and regional variation of clam-associated Symbiodiniaceae
In contrast to the high species diversity of Symbiodiniaceae in the surrounding water, clam-associated Symbiodiniaceae communities are primarily composed of two ITS2 types, C1 and A3 (Figures 3B–D), the latter of which likely corresponds to the species S. tridacnidorum (), and only 21 ASVs are shared by free-living and symbiotic communities, highlighting strong host selectivity similar to that found in corals (). Variation in the relative abundance of Cladocopium and Symbiodinium in different clams within the same reef (Figures 3B, C) reflects variation in T. maxima found in the Gulf of Aqaba, an environmentally unique region of the Red Sea, which supports the idea that T. maxima exhibits moderate symbiont flexibility to facilitate niche adaptation (). However, repeat sampling of giant clams over time, including throughout early development, would be necessary to confirm community stability and explore potential changes during ontogeny.
Moreover, the community structure of Symbiodiniaceae associated with giant clams remains largely consistent across different atolls of NS, with the abundance of Cladocopium exceeding that of Symbiodinium. This aligns with findings from a study on symbionts of T. maxima at Dongsha Atoll in the SCS (), pointing to regional dominance of these two genera within clams across the SCS. However, slight abundance variations in symbiotic Durusdinium are observed across sampling sites in this study. Such geographic heterogeneity of symbiont composition has also been documented elsewhere. For instance, T. maxima in the Red Sea exhibits a strong association with Symbiodinium (), whereas both symbionts Symbiodinium A3 and Cladocopium C1 co-dominate in French Polynesia (). These regional shifts of symbionts are generally attributed to difference in local thermal and light regimes (; ). However, we did not measure the water depth and bottom water temperature at the giant clam sampling sites, making correlation analyses unfeasible.
Host refugia enhances stability of the symbiotic Symbiodiniaceae community
Our study reveals that free-living Symbiodiniaceae communities are regulated by both deterministic and stochastic processes, whereas deterministic processes overwhelmingly dominate the assembly of clam-associated Symbiodiniaceae communities with higher stability (Figures 5A, B). This is analogous to previous findings for coral-associated Symbiodiniaceae and their ambient seawater counterparts (). Stochastic processes have also been reported to disproportionately shape the assembly of planktonic bacterial and archaeal communities in marine environments, owing to the physiological plasticity of prokaryotes and the homogenizing effects of high water connectivity (). Conversely, hosts exert strong deterministic filtering on their Symbiodiniaceae and phycosphere bacterial assemblages (; ), buffering symbionts against ambient environmental variability. In giant clams, the siphonal mantle can accumulate mycosporine-like amino acids to absorb UV radiation (; ), while iridophore cells reflect excess UV and enhance the flux of photosynthetically usable light to symbionts (; ; ; ).
This strong host-driven deterministic filtering also explains the observed mismatch whereby Symbiodinium (particularly type A3) dominates in T. maxima despite its low abundance in the surrounding seawater (Figure 3A). A similar pattern has been documented for Durusdinium, which occurs at relatively low abundances in reef water but dominates within corals (). These findings demonstrate that the host acts as a selective filter that recruits specific symbiotic partners based on its physiological needs, under whose favorable intracellular conditions the symbiotic Symbiodiniaceae proliferate. This deterministic process generates a stable symbiotic community markedly distinct from the surrounding environment.
This host-mediated refugium effect is further corroborated by environmental coefficient analysis. Free-living Symbiodiniaceae in our study showed significant correlations with environmental variables such as heavy metals (Figure 4B), whereas host-associated communities exhibited markedly reduced sensitivity (Figure 4C). This pattern likely arises from efficient host sequestration of toxic compounds, which confines heavy metals primarily to non-symbiotic tissues (e.g., kidney) and thereby minimizes symbiont exposure in the mantle (; ). Such protection therefore enhances stability of the symbiotic Symbiodiniaceae community.
Conservation implications
The symbiosis between T. maxima and Symbiodiniaceae is fundamental to host metabolism and growth and contributes to reef-scale energy flow and nutrient cycling (). Under ongoing ocean warming and acidification, this stable partnership may help giant clams act as critical ecological buffers within coral reef ecosystems (). Conservation strategies should consider the entire holobiont, including both host and symbiont, by limiting harvesting and reducing habitat degradation. Water quality management may benefit from clam-specific tissue thresholds and real-time monitoring of environment stressors, such as heavy metals and nutrient loading, which can impair both host health and symbiont photosynthesis.
Conclusions
It is concluded that the giant clam acts as a powerful biological filter. Free-living Symbiodiniaceae communities in the NS water column are in high diversity and differ markedly among reefs, and are mainly influenced by ambient nutrient concentration. Host-associated communities are lower in richness, diversity and evenness yet form more stable communities than their free-living counterparts. In contrast to the spatial difference of free-living assemblages, host selectivity produces a consistent Cladocopium C1-Symbiodinium A3 dominated consortium whose distribution patterns, along with potential functional specialization, seem to support the ecological adaptation hypothesis. Assembly analyses show stochastic and deterministic processes together structure the free-living pool, whereas deterministic host selection predominantly stabilizes the symbiosis within a buffered low-toxicity mantle microhabitat. Preserving this robust, low-diversity holobiont of T. maxima may help sustain reef photosynthetic function under ongoing climate change.
Statements
Data availability statement
The raw sequences of ITS2 have been deposited into NCBI with the project accession number PRJNA1402030. All other data are contained within this article and the supplementary file.
Ethics statement
The animal study was approved by Animal Experiment Ethics Committee of Institute of Oceanology, Chinese Academy of Sciences. The study was conducted in accordance with the local legislation and institutional requirements.
Author contributions
SY: Formal analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review & editing. MH: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing, Funding acquisition. HC: Formal analysis, Methodology, Writing – review & editing. XY: Formal analysis, Methodology, Writing – review & editing. ZZ: Methodology, Writing – review & editing. ZS: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Ministry of Science and Technology of China (No. 2021YFF0502801), the CAS-ANSO Sustainable Development Research Project (No. CAS-ANSO-SDRP-2024-02), the National Key Research and Development Program of China (2023YFC3108001), and the National Natural Science Foundation of China (42406134).
Acknowledgments
We sincerely thank the divers for their efforts in conducting the underwater sampling, and the support by Oceanographic Data Center, Institute of Oceanology, Chinese Academy of Sciences.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author MH declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2026.1818916/full#supplementary-material
References
1
AllgeierJ. E. (2024). The ecosystem ecology of coral reefs revisited. Annu. Rev. Ecol. Evol. Syst.55, 251–370. doi: 10.1146/annurev-ecolsys-102722-124549
2
AndersonM. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecol.26, 32–46. doi: 10.1111/j.1442-9993.2001.tb00081.x
3
BakerA. C. (2003). Flexibility and specificity in coral-algal symbiosis: diversity, ecology, and biogeography of Symbiodinium. Annu. Rev. Ecol. Evol. Syst.34, 661–689. doi: 10.1146/annurev.ecolsys.34.011802.132417
4
BanaszakA. T.Barba SantosM. G.LaJeunesseT. C.LesserM. P. (2006). The distribution of mycosporine-like amino acids (MAAs) and the phylogenetic identity of symbiotic dinoflagellates in cnidarian hosts from the Mexican Caribbean. J. Exp. Mar. Biol. Ecol.337, 131–146. doi: 10.1016/j.jembe.2006.06.014
5
BellS. L.QuigleyK. M. (2025). Global free-living Symbiodiniaceae biodiversity mirrors local environments. J. Biogeogr.52, e15137. doi: 10.1111/jbi.15137
6
bin OthmanA. S.GohG. H. S.ToddP. A. (2010). The distribution and status of giant clams (family Tridacnidae) - a short review. Raffles Bull. Zool.58, 103–111. doi: 10.1016/j.zool.2009.08.004
7
BolyenE.RideoutJ. R.DillonM. R.BokulichN. A.AbnetC. C.AL-GhalithG. A.et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol.37, 852–857. doi: 10.1038/s41587-019-0209-9
8
BuddA. F. (2000). Diversity and extinction in the cenozoic history of caribbean reefs. Coral Reefs19, 25–35. doi: 10.1007/s003380050222
9
CallahanB.McMurdieP.HolmesS. (2017). Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J.11, 2639–2643. doi: 10.1038/ismej.2017.119
10
CallahanB.McMurdieJ.RosenJ.HanW.JohnsonA.HolmesP. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods13, 581–583. doi: 10.1038/nmeth.3869
11
CamachoC.CoulourisG.AvagyanV.MaN.PapadopoulosJ.BealerK.et al. (2009). BLAST+: architecture and applications. BMC Bioinform.10, 421. doi: 10.1186/1471-2105-10-421
12
Castro-SanguinoC.SánchezJ. A. (2012). Dispersal of Symbiodinium by the stoplight parrotfish Sparisoma viride. Biol. Lett.8, 282–286. doi: 10.1098/rsbl.2011.0836
13
ChaoA. (1984). Nonparametric estimation of the number of classes in a population. Scand. J. Stat.11, 265–270.
14
ChecchettoV.SegallaA.AllorentG.La RoccaN.LeanzaL.GiacomettiG. M.et al. (2012). Thylakoid potassium channel is required for efficient photosynthesis in cyanobacteria. Proc. Natl. Acad. Sci. U.S.A.109, 11043–11048. doi: 10.1073/pnas.1205960109
15
ChiH.ShaZ.HeL.HuiM. (2025). Genetic population structure and distribution of the small giant clam Tridacna maxima in Indo-Pacific coral reefs: history dynamics, present status and future trends. Ecol. Evol.15, e71965. doi: 10.1002/ece3.71965
16
ColemanA. W.SuarezA.GoffL. J. (1994). Molecular delineation of species and syngens in volvocacean green algae (chlorophyta). J. Phycol.30, 80–90. doi: 10.1111/j.0022-3646.1994.00080.x
17
DaviesS. W.GamacheM. H.Howe-KerrL. I.KriefallN. G.BakerA. C.BanaszakA. T.et al. (2023). Building consensus around the assessment and interpretation of Symbiodiniaceae diversity. PeerJ11, e15023. doi: 10.7717/peerj.15023
18
DavyS. K.AllemandD.WeisV. M. (2012). Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev.76, 229–261. doi: 10.1128/MMBR.05014-11
19
DeboerT. S.ErdmannM. V.AmbariyantoA.BarberP. H. (2012). Patterns of Symbiodinium distribution in three giant clam species across the biodiverse Bird’s Head region of Indonesia. Mar. Ecol. Prog. Ser.444, 117–132. doi: 10.3354/meps09413
20
DixonP. (2003). VEGAN, a package of R functions for community ecology. J. Veg. Sci.14, 927–930. doi: 10.1111/j.1654-1103.2003.tb02228.x
21
DonnerS. D.SkirvingW. J.LittleC. M.OppenheimerM.Hoegh-guldbergO. (2005). Global assessment of coral bleaching and required rates of adaptation under climate change. Glob. Change Biol.11, 2251–2265. doi: 10.1111/j.1365-2486.2005.01073.x
22
ErenA.MorrisonH.LescaultP.ReveillaudJ.VineisJ.SoginM. (2015). Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J.9, 968–979. doi: 10.1038/ismej.2014.195
23
FalkowskiP. G.DubinskyZ.MuscatineL.PorterJ. W. (1984). Light and the bioenergetics of a symbiotic coral. BioScience34, 705–709. doi: 10.2307/1309663
24
FittW.TrenchR. (1981). Spawning, development, and acquisition of zooxanthellae by Tridacna squamosa (Mollusca, Bivalvia). Biol. Bull.161, 213–235. doi: 10.2307/1540800
25
Frias-torresS. (2017). Captive bred, adult giant clams survive restoration in the wild in Seychelles, Indian Ocean. Front. Mar. Sci.4, 97. doi: 10.3389/fmars.2017.00097
26
FujiseL.SuggettD. J.StatM.KahlkeT.BunceM.GardnerS. G.et al. (2021). Unlocking the phylogenetic diversity, primary habitats, and abundances of free-living Symbiodiniaceae on a coral reef. Mol. Ecol.30, 343–360. doi: 10.1111/mec.15719
27
GhoshalA.EckE.GordonM.MorseD. E. (2016). Wavelength-specific forward scattering of light by Bragg-reflective iridocytes in giant clams. J. R. Soc Interface13, 20160285. doi: 10.1098/rsif.2016.0285
28
GodéréI.GaertnerJ.-C.DassiéE. P.BelamyT.MaihotaN.BaudrimontM.et al. (2023). Metallic trace element contamination of the giant clam Tridacna maxima in French Polynesia. Mar. pollut. Bull.196, 115639. doi: 10.1016/j.marpolbul.2023.115639
29
GohG.ToddP. (2010). The distribution and status of giant clams (family Tridacnidae) - a short review. Raffles Bull. Zool.58, 103–111. doi: 10.1016/j.zool.2009.08.004
30
GrasshoffK.KremlingK.EhrhardtM. (2007). Methods of Seawater Analysis (New York: Wiley-VCH).
31
GuibertI.LecellierG.TordaG.PochonX.Berteaux-lecellierV. (2020). Metabarcoding reveals distinct microbiotypes in the giant clam Tridacna maxima. Microbiome8, 57. doi: 10.1186/s40168-020-00835-8
32
Hoegh-guldbergO.MumbyP. J.HootenA. J.SteneckR. S.GreenfieldP.GomezE.et al. (2007). Coral reefs under rapid climate change and ocean acidification. Science318, 1737–1742. doi: 10.1126/science.1152509
33
HoltA. L.VahidiniaS.GagnonY. L.MorseD. E.SweeneyA. M. (2014). Photosymbiotic giant clams are transformers of solar flux. J. R. Soc Interface11, 20140678. doi: 10.1098/rsif.2014.0678
34
HuangD.LicuananW. Y.HoeksemaB. W.ChenC. A.AngP. O.HuangH. (2015). Extraordinary diversity of reef corals in the South China Sea. Mar. Biodivers.45, 157–168. doi: 10.1007/s12526-014-0236-1
35
IpY. K.BooM. V.MiesM.ChewS. F. (2022). The giant clam Tridacna squamosa quickly regenerates iridocytes and restores symbiont quantity and phototrophic potential to above-control levels in the outer mantle after darkness-induced bleaching. Coral Reefs41, 35–51. doi: 10.1007/s00338-021-02199-3
36
IshikuraM.AdachiK.MaruyamaT. (1999). Zooxanthellae release glucose in the tissue of a giant clam, Tridacna crocea. Mar. Biol.133, 665–673. doi: 10.1007/s002270050507
37
IshikuraM.KatoC.MaruyamaT. (1997). UV-absorbing substances in zooxanthellate and azooxanthellate clams. Mar. Biol.128, 649–655. doi: 10.1007/s002270050131
38
ItohA.KabeN.KuwaeS.OuraE.HisamatsuS.NakanoY.et al. (2017). Multi-element profiling analyses of symbiotic zooxanthellae and soft tissues in a giant clam (Tridacna crocea) living in the coral reefs and their intake process of Zn and Cd. Bull. Chem. Soc Jpn.90, 520–526. doi: 10.1246/bcsj.20160404
39
JacksonJ. B.JungP.CoatesA. G.CollinsL. S. (1993). Diversity and extinction of tropical american mollusks and emergence of the isthmus of Panama. Science260, 1624–1626. doi: 10.1126/science.260.5114.1624
40
JiaS.WuZ.LiY.WangY.CaiZ.ShenJ. (2023). Environmental heterogeneity contributes to population genetic diversity and spatial genetic structure of coral-algal symbiosis of Platygyra daedalea in the northern South China Sea. Ecol. Indic.154, 110599. doi: 10.1016/j.ecolind.2023.110599
41
KimbrelJ. A.SamoT. J.WardC.NilsonD.ThelenM. P.SiccardiA.et al. (2019). Host selection and stochastic effects influence bacterial community assembly on the microalgal phycosphere. Algal Res.40, 101489. doi: 10.1016/j.algal.2019.101489
42
KusnadiA.KurniantoD.MadduppaH.ZamaniN. P.IbrahimP. S.HernawanU. E. (2022). Genetic diversity and population structure of the boring giant clam (Tridacna crocea) in Kei Islands, Maluku, Indonesia. Biodiversitas23, 1273–1282. doi: 10.13057/biodiv/d230311
43
LaJeunesseT. C.ParkinsonJ. E.GabrielsonP. W.JeongH. J.ReimerJ. D.VoolstraC. R.et al. (2018). Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol.28, 2570–2580. doi: 10.1016/j.cub.2018.07.008
44
LaJeunesseT. C.TrenchR. K. (2000). Biogeography of two species of Symbiodinium (Freudenthal) inhabiting the intertidal sea anemone Anthopleura elegantissima (Brandt). Biol. Bull.199, 126–134. doi: 10.2307/1542872
45
LaJeunesseT. C.WiedenmannJ.Casado-AmezúaP.D’AmbraI.TurnhamK. E.NitschkeM. R.et al. (2022). Revival of Philozoon Geddes for host-specialized dinoflagellates, ‘zooxanthellae’, in animals from coastal temperate zones of northern and southern hemispheres. Eur. J. Phycol.57, 166–180. doi: 10.1080/09670262.2021.1914863
46
LeeS. Y.JeongH. J.KangN. S.JangT. Y.JangS. H.LaJeunesseT. C. (2015). Symbiodinium tridacnidorum sp. nov. a dinoflagellate common to Indo-Pacific giant clams, and a revisedmorphological description of Symbiodinium microadriaticum Freudenthal, emended Trench & Blank. Eur. J. Phycol.50, 155–172. doi: 10.1080/09670262.2015.1018336
47
LegendreP.FortinM. J. (2010). Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol. Ecol. Resour.10, 831–844. doi: 10.1111/j.1755-0998.2010.02866.x
48
LiJ.WangY. P.GaoS. (2024). In situ hydrodynamic observations on three reef flats in the Nansha Islands, South China Sea. Front. Mar. Sci.11, 1375301. doi: 10.3389/fmars.2024.1375301
49
LimS. S. Q.HuangD.SoongK.NeoM. L. (2019). Diversity of endosymbiotic Symbiodiniaceae in giant clams at Dongsha Atoll, northern South China Sea. Symbiosis78, 251–262. doi: 10.1007/s13199-019-00615-5
50
LimK. K.RossbachS.GeraldiN. R.Schmidt-roachS.SerrãoE. A.DuarteC. M. (2020). The small giant clam, Tridacna maxima exhibits minimal population genetic structure in the Red Sea and genetic differentiation from the Gulf of Aden. Front. Mar. Sci.7, 570361. doi: 10.3389/FMARS.2020.570361
51
LinS.LiL.ZhouZ.YuanH.SaadO. S.TangJ.et al. (2024). Higher genotypic diversity and distinct assembly mechanism of free-living Symbiodiniaceae assemblages than sympatric coral-endosymbiotic assemblages in a tropical coral reef. Microbiol. Spectr.12, e0051424. doi: 10.1128/spectrum.00514-24
52
LiuJ.MengZ.LiuX.ZhangX.-H. (2019). Microbial assembly, interaction, functioning, activity and diversification: a review derived from community compositional data. Mar. Life Sci. Technol.1, 112–128. doi: 10.1007/s42995-019-00004-3
53
LoveM. I.HuberW.AndersS. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.15, 550. doi: 10.1186/s13059-014-0550-8
54
MantelN.ValandR. S. (1970). A technique of nonparametric multivariate analysis. Biometrics26, 547–558. doi: 10.2307/2529108
55
MiesM. (2019). Evolution, diversity, distribution and the endangered future of the giant clam-Symbiodiniaceae association. Coral Reefs38, 1067–1084. doi: 10.1007/s00338-019-01857-x
56
MorishimaS.NozawaY.KoikeK. (2019). Study on expelled but viable zooxanthellae from giant clams, with an emphasis on their potential as subsequent symbiont sources. PloS One14, e0220141. doi: 10.1371/journal.pone.0220141
57
NevatteR. J.GillingsM. R.MorejohnK.AinleyL.LigginsL.PratchettM. S. (2024). Of clams and clades: genetic diversity and connectivity of small giant clams (Tridacna maxima) in the southern pacific ocean. Ecol. Evol.14, e70474. doi: 10.1002/ece3.70474
58
NingD.YuanM.WuL.ZhangY.GuoX.ZhouX.et al. (2020). A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat. Commun.11, 4717. doi: 10.1038/s41467-020-18560-z
59
NitschkeM. R.RossetS. L.OakleyC. A.GardnerS. G.CampE. F.SuggettD. J.et al. (2022). The diversity and ecology of Symbiodiniaceae: A traits-based review. Adv. Mar. Biol.92, 55–127. doi: 10.1016/bs.amb.2022.07.001
60
NortonJ. H.ShepherdM. A.LongH. M.FittW. K. (1992). The zooxanthellal tubular system in the giant clam. Biol. Bull.183, 503–506. doi: 10.2307/1542028
61
PochonX.LaJeunesseT. C. (2021). Miliolidium n. gen, a new Symbiodiniacean genus whose members associate with soritid foraminifera or are free-living. J. Eukaryot Microbiol.9, e12856. doi: 10.1111/jeu.12856
62
PochonX.WeckerP.StatM.Berteaux-lecellierV.LecellierG. (2019). Towards an in-depth characterization of Symbiodiniaceae in tropical giant clams via metabarcoding of pooled multi-gene amplicons. PeerJ7, e6898. doi: 10.7287/peerj.preprints.27313
63
QuigleyK. M.BayL. K.WillisB. L. (2017). Temperature and water quality-related patterns in sediment-associated Symbiodinium communities impact symbiont uptake and fitness of juveniles in the genus Acropora. Front. Mar. Sci.4, 401. doi: 10.3389/fmars.2017.00401
64
R Core Team (2024). R: A language and environment for statistical computing (Vienna, Austria: R Foundation for Statistical Computing). Available online at: https://www.R-project.org/ (Accessed June 14, 2024).
65
RobertyS.BéraudE.GroverR.Ferrier-pagèsC. (2020). Coral productivity is co-limited by bicarbonate and ammonium availability. Microorganisms8, 640. doi: 10.3390/microorganisms8050640
66
RossbachS.HumeB. C. C.CárdenasA.PernaG.VoolstraC. R.DuarteC. M. (2021). Flexibility in Red Sea Tridacna maxima-Symbiodiniaceae associations supports environmental niche adaptation. Ecol. Evol.11, 3393–3406. doi: 10.1002/ece3.7299
67
SaycoS. L. G.KuriharaH. (2024). Bleaching and recovery in the giant clam Tridacna crocea from the sub-tropical waters of Okinawa. Coral Reefs43, 773–786. doi: 10.1007/s00338-024-02502-y
68
SchlitzerR. (2024). Ocean Data View (Version 5.8.3). (Bremerhaven: Alfred Wegener Institute). Available online at: https://odv.awi.de
69
ShannonC. E. (1948). A mathematical theory of communication. Bell Syst. Tech. J.27, 379–423. doi: 10.1145/584091.584093
70
SheppardC. R. C. (2003). Predicted recurrences of mass coral mortality in the Indian Ocean. Nature425, 294–297. doi: 10.1038/nature01987
71
ShickJ. M.DunlapW. C.ChalkerB. E.BanaszakA. T.RosenzweigT. K. (1992). Survey of ultraviolet radiation-absorbing mycosporine-like amino acids in organs of coral reef holothuroids. Mar. Ecol. Prog. Ser.90, 139–148. doi: 10.3354/meps090139
72
SmouseP. E.LongJ. C.SokalR. R. (1986). Multiple regression and correlation extensions of the mantel test of matrix correspondence. Syst. Biol.35, 627–632. doi: 10.2307/2413122
73
ter BraakC. J. F. (1986). Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology67, 1167–1179. doi: 10.2307/1938672
74
ThébaultE.FontaineC. (2010). Stability of ecological communities and the architecture of mutualistic and trophic networks. Science329, 853–856. doi: 10.1126/science.1188321
75
UmekiM.MorishimaS.KoikeK. (2020). Fecal pellets of giant clams as a route for transporting Symbiodiniaceae to corals. PloS One15, e0243087. doi: 10.1371/journal.pone.0243087
76
WatsonS. A.NeoM. L. (2021). Conserving threatened species during rapid environmental change: using biological responses to inform management strategies of giant clams. Conserv. Physiol.9, coab082. doi: 10.1093/conphys/coab082
77
WeisV. M. (2008). Cellular mechanisms of Cnidarian bleaching: stress causes the collapse of symbiosis. J. Exp. Biol.211, 3059–3066. doi: 10.1242/jeb.009597
78
XunW.LiuY.LiW.RenY.XiongW.XuZ.et al. (2021). Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome9, 35. doi: 10.1186/s40168-020-00985-9
79
YongeC. M. (1936). Mode of life, feeding, digestion and symbiosis with zooxanthellae in the Tridacnidae. G. B. R. Exped. Rep.1, 283–321.
80
ZhaoM. X.YuK. F.ShiQ.ChenT. R.ZhangH. L.ChenT. G. (2013). Coral communities of the remote atoll reefs in the Nansha Islands, southern South China Sea. Environ. Monit. Assess.185, 7381–7392. doi: 10.1007/s10661-013-3107-5
81
ZhouJ.NingD. (2017). Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev.81, e00002‑17. doi: 10.1128/MMBR.00002-17
82
ZhouS.ShiQ.YangH.TaoS.ZhangX.YanH.et al. (2025). Early diagenesis of coral rubble on the high-energy platform of Ximen Reef in the southern South China Sea. Mar. Geol.483, 107510. doi: 10.1016/j.margeo.2025.107510
83
ZuurA. F.IenoE. N.ElphickC. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol.1, 3–14. doi: 10.1111/j.2041-210X.2009.00001.x
Summary
Keywords
community stability, coral reefs, giant clams, host selectivity, Symbiodiniaceae, symbiosis
Citation
Yu S, Hui M, Chi H, Yang X, Zhang Z and Sha Z (2026) Host filtering shapes diversity and community stability of Symbiodiniaceae in Tridacna maxima across the Nansha Archipelago in the South China Sea. Front. Mar. Sci. 13:1818916. doi: 10.3389/fmars.2026.1818916
Received
27 February 2026
Revised
25 May 2026
Accepted
03 June 2026
Published
17 June 2026
Volume
13 - 2026
Edited by
Wei Jiang, Guangxi University, China
Reviewed by
Chinnarajan Ravindran, Council of Scientific and Industrial Research (CSIR), India
Sydney Bell, James Cook University, Australia
Updates
Copyright
© 2026 Yu, Hui, Chi, Yang, Zhang and Sha.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Min Hui, minhui@qdio.ac.cn; Zhongli Sha, shazl@qdio.ac.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.