Abstract
Octocorals are an important component contributing to habitat forming that enhances the three-dimensional structural complexity and facilitates benthic–pelagic coupling for marine benthic ecosystems. Studies on octocorals in marginal environments could provide insight to researchers and conservationists in the face of the deteriorating seascape under the Anthropocene. The octocoral communities in Hong Kong persist under marginal living conditions created by freshwater influx from the western Pearl River Estuary, transitioning from the turbid, hyposaline conditions of the western waters to the relatively oligotrophic oceanic waters of the East. This study employed genome re-sequencing to investigate the population structure and genetic diversity of three widely distributed octocoral species, Dendronephthya gigantea, Dendronephthya spinifera, and Echinomuricea spinifera, along the water gradient of Hong Kong to investigate the effect of a marginal turbid environment on the population structure of octocorals. Two octocoral genera exhibited differences in vertical distribution in response to sedimentation and turbidity, in which the soft coral Dendronephthya in western waters were restricted to shallower depths while the gorgonian Echinomuricea could occupy deeper waters in the western region despite heavy siltation. A low level of genetic differentiation was reported in D. gigantea in the turbid western region while no significant population differentiation was observed for D. spinifera and E. spinifera between sampling regions. Moreover, low genetic diversity in terms of nucleotide diversity and heterozygosity with highly negatively skewed Tajima’s D was reported for all three studied octocoral species in Hong Kong. The lack of population differentiation and low genetic diversity could reflect a small founder population selected by turbid marginal conditions settled in Hong Kong and expand the population with the aid of asexual reproduction. Despite their low genetic diversity, which suggests low evolutionary potential and intrinsic vulnerability toward diseases and disturbances, octocoral populations in Hong Kong could be selected under a marginal environment and exhibit resilience to suboptimal conditions. This study underscores the necessity of prioritizing marginal octocoral communities in conservation frameworks, as they represent critical units for understanding species persistence under environmental stress and may serve as essential gene sources for regional biodiversity in an increasingly unstable global climate.
1 Introduction
Octocoral communities are important marine habitats that provide shelter and nursery grounds for rheophilic fishes and macroinvertebrates (). Furthermore, octocorals facilitate benthic–pelagic coupling by capturing pelagic food particles and recycling nutrients into the system in the form of mucus and metabolic waste, or through directly grazing by other benthic fauna (; Leal et al., 2014). The ecologically important octocoral communities worldwide are threatened by climate change and anthropogenic disturbances (Steinberg et al., 2020).
Coral communities inhabiting marginal environments have increasingly become a focal point for understanding how reefs and associated organisms respond to future environmental change (Perry and Larcombe, 2003; ; ; Schleyer et al., 2018). Among these marginal reefs are those that exist in regions with naturally or anthropogenically high turbidity. The turbidity affects the health of reefs in multifaceted ways, including light attenuation limiting photosynthesis, hindering settlement of coral larvae (), smothering coral recruits and adults (Weber et al., 2012), and phase shifting toward algal-dominated communities due to excess nutrient imported (). Marginal reefs under turbidity are predicted to expand globally under the Anthropocene due to rising sea levels, more extreme rainfalls, and changing land use practices (Statham, 2012; ; ; ; ; Zweifler et al., 2021). Studies on marginal reefs in turbid settings can provide valuable insights into how octocorals persist in changing environments, which are predicted to be a growing concern around the world in the coming decades.
Hong Kong is a subtropical metropolis situated on the eastern side of the Pearl River Estuary along the southern coast of China. Freshwater influx and sedimentation create a turbid environment in which the suboptimal conditions retard coral growth, leading to coral communities in Hong Kong being spatially and bathymetrically restricted (; Perry and Larcombe, 2003). Azooxanthellate octocoral communities constitute the dominant habitats beyond 12 m depth in areas with moderate to high sedimentation (). The seascape in Hong Kong is heavily influenced by the interplay of freshwater discharge from the Pearl River Estuary in the West and oceanic waters carried by coastal currents from the South China Sea in the East (). The freshwater influx from Pearl River brings in sediment, excess nutrients, and pollutants into the marine environment that create an environmental gradient characterized by more turbid waters with higher nutrient concentration in the West, transitioning to oligotrophic oceanic waters in the East (; Lai et al., 2016), and Hong Kong waters were divided into an estuarine zone in the West, a transitional zone in the South, and an oceanic zone in the East (Morton and Morton, 1983). The water quality along this West-to-East gradient shapes compositional difference in octocoral communities (; Yeung et al., 2014, 2021). The environmental gradient could also potentially drive the differentiation in population genetic structure through isolation by environment, where genetic differentiation increases between populations as environmental differences increase (Wang and Bradburd, 2014). A meta-analysis of population genetic studies demonstrated population structuring shaped by environmental gradient where clinal genetic differentiation and substantially lower genetic diversity were observed in the majority of marine taxa inhabiting the hyposaline Baltic Sea compared to the adjacent oceanic Atlantic region, suggesting that genetic structuring of populations in the Baltic Sea is driven by selection and bottlenecks due to marginal conditions (). On the other hand, the genetic structure of dominant scleractinian coral Platygyra sinensis in Hong Kong using the isoelectric focusing (IEF) technique on six enzyme systems revealed genetic similarity across sampling sites (Ng and Morton, 2003).
Population genetics and genomics are critical for revealing the processes that shape connectivity and population structure, assessing genetic diversity in populations, and identifying populations with potential adaptive variation in response to environmental extremes (; Munday et al., 2013; Oleksiak and Rajora, 2019; ). However, population genetics in marine studies are predominantly focused on reef-forming scleractinian corals (Aurelle et al., 2022; Primov et al., 2024; Shinzato et al., 2015), and studies on octocorals remain scarce and limited despite their ecological importance. Therefore, this study aims to employ genome-resequencing techniques to investigate population genetics and diversity of octocorals in Hong Kong waters. It is anticipated that the environmental gradient, particularly the more turbid, nutrient−rich western waters influenced by the Pearl River Delta, will be associated with increased genetic differentiation and reduced genetic diversity driven by demographic bottlenecks, founder effects, or strong environmental filtering. Three widely distributed species, including two soft corals, Dendronephthya gigantea (Verrill, 1864) and Dendronephthya spinifera (), and one gorgonian, Echinomuricea spinifera Nutting, 1910, occur across Hong Kong from western to eastern waters (personal observation) and were therefore selected for examining genetic diversity and population structure along the environmental gradient in Hong Kong waters. Investigating these species in Hong Kong will provide valuable insights into the management and conservation of octocoral populations in turbid marginal environment, while also serve as indicators of other nearshore tropical reefs facing deteriorating seascapes under climate change (; ).
2 Methodology
2.1 Sample collection and ecological distribution statistics
Specimens of three target species were collected in 19 sites from Hong Kong in 2022–2024. The 19 sites were divided into East (sites 1–5), representing oceanic zone; South (sites 6–11), representing transition zone; and West (sites 12–19), representing estuarine zone in Hong Kong waters, respectively (Figure 1). The division was based on the difference in 3-year annual mean of water quality parameters from 2022 to 2024 retrieved from water monitoring stations (Figure 1) of the Environmental Protection Department (EPD) of the Hong Kong SAR Government, which showed a consistent pattern of lower salinity and higher turbidity and nutrient content in the West than in the East, with the South being intermediate in general (Supplementary Table 1). At each site, sample collections were conducted by SCUBA diving that descended along the slope and searched from shallow to deeper water until reaching the maximum safe depth for visibility, so that the local depth profile was covered as much as possible at every site. Owing to the low and scattered densities of octocorals in Hong Kong, all encountered colonies of the target species were sampled at each site, excluding tightly aggregated individuals. Sampled colonies were separated for at least 2 m to minimize the chance of repeated sampling of the same genet produced by asexual fragmentation. From each colony, approximately 4 cm of branch was collected, live photos of specimens were taken using an Olympus TG-6 underwater camera, and depth of collection was recorded prior to the collection. A total of 52 Dendronephthya (East: 15, South: 18, and West: 19) and 47 Echinomuricea samples (East: 16, South: 13, and West: 18) were collected, and among 52 Dendronephthya samples, 25 individuals were identified as D. gigantea and 27 individuals were identified as D. spinifera (Figure 2; Supplementary Table 2).
Figure 1
Figure 2
Sampled coral fragments were preserved in 95% ethanol and delivered to the laboratory for extraction of genomic content. The species identity of the samples collected was validated by their micromorphology, anthocodial arrangement, and sclerite structures. For the examination of sclerite structures, small sections of tissue from the colony were extracted by sterile forceps and then dissolved by household bleach (5% sodium hypochlorite) on a microscope slide to examine them under an upright microscope. Identification for each genus and species followed Kükenthal (1905), Thomson and Mackinnon (1910), and . The spatial distribution among three species were compared by chi-square test on the presence/absence of species from sampling sites. For bathymetrical distribution, the sampling depth of each genus was visualized by boxplots. The assumption of normality and the homogeneity of variance for depth distribution data were checked by the Shapiro–Wilk test and Levene’s test, respectively. The depth distribution of both genera violated the assumption of normality (p-value < 0.01), and additionally, Dendronephthya violated the assumption of homogeneity of variance (p-value < 0.05) (Supplementary Table 3). Consequently, Kruskal–Wallis tests and Dunn’s tests on post-hoc comparison of each region pair with Benjamini–Hochberg correction were employed to compare the bathymetric distributions across regions for each genus (Figure 3; Supplementary Table 3).
Figure 3
2.2 DNA extraction and genome re-sequencing
Prior to DNA extraction, the preserved samples were rinsed with 95% ethanol to remove any potential contaminants on sample surface. Subsequently, tissue was extracted by sterilized forceps and genomic DNA was then extracted using the Qiagen DNeasy Blood & Tissue Kit (Germany) following the protocol provided, including RNase treatment to minimize the RNA content in the library preparation during high-throughput sequencing. The quality of genomic DNA extracted was checked using agarose gel electrophoresis to examine the integrity of DNA, and a Thermo Fisher Scientific Nanodrop Microvolume Spectrophotometer (USA) was used to estimate the DNA concentration. Qualified genomic DNA of octocoral samples were sent to Novogene (China) for sequencing. De novo library preparation with a 350-bp insert size from the individual genome was sequenced by Illumina Novaseq 6000 (150-bp pair-end reads).
2.3 SNP identification and detection
Adaptor sequence from raw reads were trimmed by Trimmomatics 0.32 (Bolger and Lohse, 2014). Clean reads of each sample were mapped to the reference genome of the most closely related species available using BWA 0.5.9 (Li and Durbin, 2009). Reads from Dendronephthya samples were mapped to the D. gigantea genome (Jeon et al., 2019), resulting in mapped read depth samples ranging from 16.07× to 33.00× with an average of 24.87× (Supplementary Table 4a). Reads from Echinomuricea samples were mapped to the confamilial Paramuricea clavata genome (Ledoux et al., 2020), resulting in mapped read depth samples ranging from 3.88× to 18.96× with an average of 5.84× (Supplementary Table 4b). The mapped read depth of E. spinifera samples was substantially lower than Dendronephthya samples due to the use of the distantly related Paramuricea clavata genome as reference. This could bias SNP detection toward conserved regions and underestimate genetic diversity parameters in downstream analyses. Therefore, diversity estimates for E. spinifera should be interpreted with caution.
Unmapped reads and duplicated reads were removed using SAMtools 0.1.18 (Li et al., 2009). Optical duplicates were removed by the MarkDuplicates function of Genome Analysis Toolkit 4.1.8.1 (GATK4) (Mckenna et al., 2010). Subsequent bam files were processed by GATK4 with the default setting for initial variant site identification. Consensus calls were further filtered by including only no-missing-call SNPs and used as a known site for base quality recalibration using GATK4 with default settings. The resulting recalibrated bam files were used as new input for variant calling with GATK4. A total of 32,732,341 SNPs were called from Dendronephthya samples while 3,706,915 SNPs were called from Echinomuricea samples. The resulting variant matrices were filtered by VCFtools 0.1.16 (Danecek et al., 2011) and conservative filtering was applied to minimize genotyping error while retaining sufficient SNPs for analysis. A minimum mean depth greater than 10 (--min-meanDP 10) was set to reduce allele dropout, 5% missing data (--max-missing 0.95) was set to avoid biased allele frequency, including only biallelic sites with a minor allele frequency (MAF) greater than or equal to 5% (--maf 0.05) to minimize sequencing artifacts, and a distance between each site of greater than 10 (--thin 10) was set to reduce the impact of physically linked sites, to create the “Filtered dataset”, with 6,197,630 and 72,458 SNPs retained for Dendronephthya and Echinomuricea, respectively. Subsequently, pairwise linkage disequilibrium and Hardy–Weinberg equilibrium (HWE) were calculated with Plink 1.9 (Purcell et al., 2007). SNPs exhibited extended LD in which a correlated (r2 = 0.2) pair of SNPs within a window of 100 kb and extensive deviation (p < 0.001) from HWE were excluded from the dataset to avoid artifacts due to LD, creating “Pruned datasets” with 65,342 and 30,050 SNPs for Dendronephthya and Echinomuricea, respectively.
2.4 Population genetic structure and diversity analysis
The population genetic structure of D. gigantea, D. spinifera, and E. spinifera were evaluated by phylogenomic analysis, principal component analysis (PCA), admixture analysis, and pairwise FST and GST using the “Pruned dataset”. The resulting vcf files of the “Pruned dataset” of Dendronephthya and Echinomuricea samples were converted to the PHYLIP format by vcf2phylip v.2.0 (Ortiz, 2019) for phylogenomic analysis. Maximum likelihood (ML) trees were constructed for both Dendronephthya and Echinomuricea by IQ-TREE 2.0.3 with 1,000 ultrafast bootstraps (Minh et al., 2013; Nguyen et al., 2015) with the best-fit substitution model determined by ModelFinder (-m MFP) implemented in IQ-TREE (). The TVM model was determined as the best-fit model based on Bayesian information criterion (BIC) and was applied for the phylogenomic analysis with ascertainment bias correction (ASC) to avoid overestimation of branch length and biases in tree topology selection (Lewis, 2001). The final trees were visualized by iTol v.6.6 (Letunic and Bork, 2021). PCA of the variance-standardized relationship matrix was calculated from the “Pruned dataset” by Plink v.1.9 under the default setting (Purcell et al., 2007) to visualize the population structure of three species. Admixture analysis was conducted to quantify the proportion of ancestry in each sample. Admixture proportions of individuals were estimated from the “Pruned dataset” using ADMIXTURE v.1.3.0 (Alexander et al., 2009). This study tested potential ancestry of K values from 1 to 5. The cross-validation (CV) error for each K value was estimated and compared to determine the best value of K that exhibits the lowest CV error value. Pairwise FST for the three regions was calculated from the “Pruned dataset” using the R package “StAMPP” version 1.6.3 (Pembleton et al., 2013), with 1,000 bootstrap replicates to calculate the 95% confidence interval (CI). CIs that did not contain zero were considered to be statistically significant. Pairwise Hedrick’s G′ST () among sampling regions were also calculated to complement FST using R package “mmod” version 1.3.3 (Winter, 2012).
Genetic diversity of three octocoral species from each sampling regions were examined from the “Filtered dataset” to include the full set of high-quality variants for calculating genetic diversity. Nucleotide diversity (π), heterozygosity rate (HO), inbreeding coefficient (FIS), and Tajima’s D were calculated by VCFtools 0.1.16 () on the samples from sampling regions “East”, “South”, and “West”, respectively, to investigate the genetic diversity of three octocoral species by sampling regions.
3 Results
3.1 Distribution of Dendronephthya and Echinomuricea
Regarding the spatial distribution of Dendronephthya and Echinomuricea, both genera were present across East, South, and West regions; however, there was little overlap in their sampling sites within regions. Most collections from each site were exclusive to one genus, and Waglan Island was the only sampling site from which both genera were collected (Supplementary Table 2). The chi-square test also confirmed the disparity in geographical distribution for two genera with a p-value < 0.0001. From field observations, Dendronephthya occupied sites with a rocky substratum while Echinomuricea occupied sites with a silty substratum, suggesting niche differentiation between the two genera in their microhabitats. At the species level, the two species of Dendronephthya collected in this study showed no significant difference in spatial distribution based on chi-square test (p-value = 0.190), suggesting sympatry for the two species.
Regarding the bathymetric distribution, both genera exhibited a wide range of distribution along the depth. Dendronephthya ranged from 6.9 to 22.3 m with a mean depth of 14.1 m while Echinomuricea ranged from 8.4 to 21.5 m with a mean depth of 14.2 m, suggesting that both genera have the potential to occupy a similar depth range. However, the two genera showed differences in their vertical distribution across sampling regions. The depth distribution of Dendronephthya was significantly different among sampling regions according to Kruskal–Wallis tests (p-value ≤ 0.001; Supplementary Table 3). Dendronephthya sampled from western water (range: 6.9–14.4 m; median: 10.4 m) in this study occupied significantly shallower water than those observed in southern (range: 10.0–22.3 m; median: 16.1 m) and eastern water (range: 13.2–20.8 m; median: 15.0 m) (Figure 3). On the other hand, there was no significant difference in depth (p-value = 0.0955) for Echinomuricea across regions (range: 8.4–21.5 m; median: 13.7 m) (Figure 3).
3.2 Population genetic structure of Dendronephthya and Echinomuricea
3.2.1 Delineation of Dendronephthya species
For the two Dendronephthya species (D. gigantea and D. spinifera) analyzed in this study, the species boundaries between congeneric taxa were tested to validate the taxonomic identity of each species and detect the presence of potential hybrids or cryptic species prior to the downstream population genetic analysis. The ML tree constructed from the “Pruned dataset” of all 52 Dendronephthya samples resolved two reciprocally monophyletic clades with high bootstrap support corresponding to the two species (Figure 4A). The clear distinction between the two species was further supported by the PCA plot of the variance-standardized relationship matrix (Figure 4B). In addition, admixture analysis of the two species was conducted to examine the potential admixture among the Dendronephthya samples (Figure 4C). The CV error value was the lowest at K = 2 (Figure 4D), indicating the highest likelihood that the samples originated from two distinct ancestries, which corresponded to two Dendronephthya species collected, and no admixture signal was detected. Samples identified as D. gigantea and D. spinifera were consistently recovered as two distinct genetic groups from phylogenetic and admixture analyses, support treating D. gigantea and D. spinifera as distinct species in the context of this study. The genome-wide SNP dataset was then separated with respect to each species for the subsequent population genetic structure analyses.
Figure 4
3.2.2 Population structure analysis of octocoral species
The ML trees constructed for D. gigantea (Figure 5A), D. spinifera (Figure 6A), and E. spinifera (Figure 7A) revealed no distinct clustering of samples with respect to their sampling regions. Additionally, the PCA plots for D. gigantea (Figure 5B), D. spinifera (Figure 6B), and E. spinifera (Figure 7B) showed similar, overlapping patterns across the three sampling regions, suggesting little differentiation in genetic distance among individuals from each sampling region. The CV error values for the three octocoral species were calculated from the admixture analyses, and all three species exhibited the lowest CV error value at K = 1 (Supplementary Figure 1). This indicated the highest likelihood that the samples from each species originated from a single ancestry.
Figure 5
Figure 6
Figure 7
The pairwise FST values among sampling regions were generally low, being either negative or close to zero (magnitude of 10-3–10-4) for all three species (Table 1A). However, the 95% CIs of pairwise FST values for D. gigantea between the western region and eastern or southern regions excluded zero (Table 1A), suggesting subtle but significant genetic differentiation. Furthermore, pairwise G′ST values revealed slightly elevated genetic differentiation for D. gigantea in the western region compared to the eastern (G′ST = 0.0715) and southern (G′ST = 0.0706) regions (Table 1B). For both D. spinifera and E. spinifera, all pairwise G′ST values among regions exhibited insignificant genetic differentiation (G′ST < 0.05) (Table 1B), consistent with the pattern in the FST analysis.
Table 1
| A | ||
|---|---|---|
| Dendronephthya gigantea | ||
| Regions | East | West |
| West | 0.0053 (0.0045, 0.006) | – |
| South | −0.0037 (−0.0007, 0.0001) | 0.0013 (0.0005, 0.0021) |
| Dendronephthya spinifera | ||
|---|---|---|
| Regions | East | West |
| West | −0.0014 (−0.001, 0) | – |
| South | −0.0052 (−0.002, −0.0008) | −0.0022(−0.0026, −0.0019) |
| Echinomuricea spinifera | ||
|---|---|---|
| Regions | East | West |
| West | −0.0006 (−0.0015, 0.0003) | – |
| South | 0.0016 (−0.0005, 0.0037) | 0.0012 (−0.0011, 0.0032) |
| B | ||
|---|---|---|
| Dendronephthya gigantea | ||
| Regions | East | West |
| West | 0.0715 | – |
| South | 0.0396 | 0.0706 |
| Dendronephthya spinifera | ||
|---|---|---|
| Regions | East | West |
| West | 0.0260 | – |
| South | 0.0279 | 0.0220 |
| Echinomuricea spinifera | ||
|---|---|---|
| Regions | East | West |
| West | 0.0222 | – |
| South | 0.0288 | 0.0297 |
Tables showing pairwise (A) FST (95% CIs) and (B) G′ST values between sampling regions.
Significant pairwise FST values (i.e., 95% CI did not contain zero) are indicated in bold.
3.2.3 Genetic diversity of octocoral species
The values for the genetic diversity indices are summarized in Table 2. The three target species across three sampling regions exhibited similar patterns of low genetic diversity in terms of nucleotide diversity, heterozygosity, and the inbreeding coefficient. The median of nucleotide diversity of D. spinifera (4.63 × 10−4) was slightly higher than that of D. gigantea (3.44 × 10−4). Conversely, D. gigantea had slightly higher heterozygosity (0.0549) and lower inbreeding coefficient (0.0409) than D. spinifera (HO = 0.0409; FIS = 0.19).
Table 2
| East | South | West | Overall | |
|---|---|---|---|---|
| Dendronephthya gigantea | ||||
| Nucleotide diversity (π) | 3.15 × 10−4 | 3.56 × 10−4 | 3.47 × 10−4 | 3.44 × 10−4 |
| Heterozygosity (HO) | 0.122 | 0.103 | 0.163 | 0.0549 |
| Inbreeding coefficient (FIS) | 0.143 | 0.195 | 0.188 | 0.176 |
| Tajima’s D | −2.12 | −2.20 | −1.78 | −2.39 |
| Dendronephthya spinifera | ||||
| Nucleotide diversity (π) | 4.24 × 10−4 | 4.31 × 10−4 | 4.64 × 10−4 | 4.63 × 10−4 |
| Heterozygosity (HO) | 0.159 | 0.128 | 0.0893 | 0.0409 |
| Inbreeding coefficient (FIS) | 0.170 | 0.137 | 0.123 | 0.190 |
| Tajima’s D | −1.99 | −2.19 | −2.42 | −2.61 |
| Echinomuricea spinifera | ||||
| Nucleotide diversity (π) | 7.30 × 10−5 | 6.80 × 10−5 | 6.60 × 10−5 | 6.53 × 10−5 |
| Heterozygosity (HO) | 0.0471 | 0.0620 | 0.0437 | 0.0242 |
| Inbreeding coefficient (FIS) | 0.429 | 0.386 | 0.404 | 0.448 |
| Tajima’s D | −2.44 | −2.29 | −2.50 | −2.44 |
A table summarizing the medians of genetic diversity indices for Dendronephthya gigantea, Dendronephthya spinifera, and Echinomuricea spinifera.
E. spinifera exhibited a substantially lower nucleotide diversity and higher inbreeding coefficient than the two Dendronephthya species. The median of nucleotide diversity of E. spinifera (6.53 × 10−5) was an order of magnitude lower than the two Dendronephthya species (10−4). The exceptionally low genetic diversity observed in Echinomuricea could reflect inherent biological characteristics of the analyzed populations. However, the low diversity reported could partly be an artifact of biased reads mapping to conserved regions in distant reference genome, which tends to underestimate the true genetic diversity of Echinomuricea samples. Thus, the comparison of absolute values of genetic diversity parameters for Echinomuricea should be made with caution. Moreover, Tajima’s D values were highly negatively skewed across all three octocoral species in all sampling regions, suggesting an excess of low-frequency alleles compared to equilibrium conditions that deviate from neutral evolution.
4 Discussion
4.1 Vertical distribution of Dendronephthya and Echinomuricea in response to turbidity
The impact of sediment dynamics is often depth-dependent that shallower waters are frequently less susceptible to silt deposition due to “tidal sweeping” (Morgan et al., 2020). Currents of higher velocity in shallower regions generated by diurnal tidal flows resuspend fine particles and winnow sediments away from the benthos. This mechanism is critical for maintaining the ecological stability of shallow-water coral communities in high-sediment environments, such as those in Singapore (Cacciapaglia and van Woesik, 2016). In Hong Kong’s western waters, the observed vertical distribution of octocorals suggests that Dendronephthya is more sensitive to siltation than Echinomuricea. Specifically, in the heavily silted western waters, Dendronephthya colonies were restricted to shallower depths where tidal sweeping mitigates sediment accumulation. The congeneric Dendronephthya hemprichi has been reported to feed mainly of phytoplankton (, ), the availability of which is reduced in turbid waters due to light attenuation. Additionally, the formation of mucus-sediment sheets on octocorals can physically obstruct polyp ingestion. Consequently, the dual pressures of limited food availability and impaired ingestion likely restrict the presence of Dendronephthya in the deeper, sediment-heavy waters of the western region. Moreover, the lobate morphology of Dendronephthya could lead to increased sediment retention, making them more susceptible to sediment induced damage. Heavy siltation is detrimental to the development of corals. Corals respond to sedimentation with rejection behaviors, including mucus production and ciliary action, which lead to a loss of fixed carbon and increased energy expenditure (Riegl and Branch, 1995). Furthermore, the microbial decomposition of organic matter in the sediments could induce localized hypoxia and acidification, triggering tissue degradation (Weber et al., 2012). Collectively, heavy sedimentation imposes chronic stress that may result in retarded growth, tissue loss, mortality, and impaired larval settlements with juvenile corals being particularly vulnerable to these environmental pressures (; Tuttle and Donahue, 2022; ).
In contrast, Echinomuricea exhibits a higher tolerance for heavy siltation. In this study, Echinomuricea was observed inhabiting silty substrata (Figure 2), and unlike Dendronephthya, its depth distribution showed no significant regional variation and can occupy deeper water in the West despite the heavy siltation. This capacity to persist is likely attributed to its upright branching morphology with a thin axis and branches, which minimize sediment retention. Furthermore, Echinomuricea possesses the ability to retract its polyps into calyces to avoid contact with sediment. Such retraction, combined with colonial expansion, has been identified as an effective sediment rejection strategy that facilitates the shedding of mucus-sediment sheets in related gorgonians, such as Suberogorgia suberosa (Tseng et al., 2011). By minimizing retention and maximizing removal efficiency, Echinomuricea can alleviate the physiological stress of sedimentation, allowing it to colonize deeper western waters where heavy siltation is constant.
4.2 Subtle population genetic structure of Dendronephthya gigantea in Hong Kong
Both pairwise FST and G′ST values revealed small but significant genetic differentiation for D. gigantea in the western region. D. gigantea is an internal brooder with negatively buoyant larvae (), which likely have more restricted dispersal than broadcast−spawned, positively buoyant larvae. The observed genetic differentiation of D. gigantea in the western region is therefore consistent with the hypothesis that the environmental gradient in Hong Kong, together with restricted larval dispersal, could promote population divergence. In contrast, D. spinifera and E. spinifera showed little to no regional genetic differentiation in both FST and G′ST, which may reflect recent colonization or limited divergence time. Although species-specific reproductive modes have not been reported for D. spinifera and E. spinifera, and reproductive strategies in octocorals are generally variable, with both broadcast spawning and brooding widely documented (). Even within Dendronephthya, congeners include both broadcast spawners (; ; Larkin et al., 2023) and brooders (, ). It is therefore possible that D. spinifera and E. spinifera are broadcast spawners with higher dispersal capacities, which could weaken the regional population structure. Differences in life−history traits, particularly reproductive mode and larval dispersal, remain a plausible contributing factor, alongside environmental and demographic factors to the contrasting patterns of genetic differentiation observed among the three species in this study.
The genetic diversity values recorded for octocorals in Hong Kong waters are notably lower than those reported for other anthozoans in tropical and subtropical regions. The inbreeding coefficients (FIS) of both Dendronephthya species in this study were substantially higher than those reported for their congeneric Dendronephthya australis (−0.93 to 0.033) in temperate eastern Australia (Williamson et al., 2022). Because population genomic studies on octocorals remain scarce, the genetic diversity of Hong Kong octocorals was compared to scleractinian counterparts, which share similar life-history traits such as long-living, sessile, colonial anthozoan. In this study, the median nucleotide diversity (π) for Dendronephthya species was approximately 4 × 10−4, while E. spinifera exhibited even lower values, approximately 7 × 10−5. The nucleotide diversity values in Hong Kong are orders of magnitude lower than those reported in other studies. For instance, Acropora tenuis exhibits π = 0.0024 across the Great Barrier Reef (Matias et al., 2023) while Pocillopora verrucosa (π = 0.00256) and Stylophora pistillata (π = 0.0011) show higher values in the Red Sea (). A similar trend is evident in observed heterozygosity rates (HO) that the octocorals in this study displayed values ranging from 0.043 and 0.162, which are substantially lower than the heterozygosity rates of scleractinian corals in mature reefs outside of Hong Kong, for example, HO ~ 0.25 for Acropora digitifera and A. tenuis in Okinawa (Zayasu et al., 2021), and HO = 0.195 and HO = 0.195 observed in P. verrucosa and S. pistillata in the Red Sea ().
Hong Kong is a marginal environment for octocoral communities characterized by hyposalinity and heavy sedimentation (). Populations in marginal environments tend to differ demographically compared to core habitats. Generally, constrained by suboptimal conditions, marginal environments support lower population densities than mature, core habitats (Sagarin et al., 2006; Vucetich and Waite, 2003). Furthermore, populations in core habitats face relatively less environmental stress, which can lead to higher fecundity than marginal environment (). With higher density and fecundity, populations in core habitats are expected to produce more offspring and more potential dispersers, leading to asymmetric dispersal from core to marginal habitats. Thus, marginal populations often serve as demographic sinks and the lack of genetic structure among populations of these octocoral species in Hong Kong waters suggests that the populations of each species likely originated from a single source.
Moreover, the exceptionally low genetic diversity with low nucleotide diversity and high inbreeding coefficient, together with highly negatively skewed Tajima’s D values observed in all three octocoral species across sampling regions of Hong Kong, suggests that these octocoral populations might be experiencing different evolutionary pressures driven by multiple potential evolutionary processes or their joint effect, compared to the more diverse coral assemblages in other major reef systems. Colonization toward the range edges in the marginal environments, such as Hong Kong, typically reduces neutral genetic diversity due to founder effects and potential purifying selection against deleterious alleles under adverse environment. This leaves founders with fewer genotypes to establish new populations, resulting in lower nucleotide diversity and heterozygosity compared to core areas (Sagarin et al., 2006). This genetic baseline could be further depressed by recurrent partial die-offs caused by seasonal disturbance like heavy rainfall leading to extensive hyposalinity across seascape (McCorry, 2002; Xie et al., 2017). A similar pattern of lower genetic diversity in the marginal population versus the core population has been observed in the scleractinian coral Acropora hyacinthus expanding toward temperate regions along the Kuroshio Current (Nakabayashi et al., 2019; ). The founder effect followed by subsequent population expansion is further supported by the highly negatively skewed Tajima’s D values in all three octocoral species in Hong Kong. The negative Tajima’s D values revealed an excess of rare alleles relative to the standard neutral model of evolution and the negative signal was observed across the entire genome; it strongly suggests a genome-wide demographic process, such as population expansion, instead of sporadic selection on isolated genes. Clonal propagation was reported in Dendronephthya and gorgonians, which allows rapid population expansion (Lasker, 1990; ) and serves as an effective strategy for population persistence in marginal environments (Meloni et al., 2013).
4.3 Vulnerable octocoral refugia in marginal environments
Marginal communities are distinct ecosystems characterized by their resilience under suboptimal conditions, leading to hypotheses that they could act as potential refugia and gene sources for adaptation under climate change (; Nakabayashi et al., 2019; ). It is predicted that the marine ecosystems will face constant and worsening threats of freshwater runoff due to anthropogenic activities and climate change (Röthig et al., 2023). The octocoral communities in Hong Kong persist in a highly turbid setting, facing hyposalinity and heavy sedimentation. Broad physiological tolerance and pronounced phenotypic traits such as morphology, symbiotic associations, and stress-response pathways can underpin resilience to climate change. Additionally, D. gigantea exhibited moderate genetic differentiation in the western region where it is under constant freshwater influx and sedimentation loads, suggesting the genetic divergence could be driven by the local environmental gradient. Because our analyses focus on putatively neutral SNPs, they are primarily informative about demographic history and connectivity rather than direct adaptive divergence. Consequently, any environmentally driven divergence must currently be considered hypothetical, and future work incorporating samples from core reefs as a baseline is essential to test adaptation directly. Possible approaches include transcriptomic analyses of gene expression responses to hyposalinity and sedimentation () and ex situ experiment coupled with genome-wide association studies (GWAS) to identify candidate loci under selection (Manullang et al., 2025). Such studies would help clarify whether Hong Kong populations are truly adapted to turbid marginal environments and would provide mechanistic support for treating these areas as potential climate change refugia. Additionally, the low genetic diversity in these marginal populations may indicate potential intrinsic vulnerability due to reduced evolutionary potential when responding toward unpredictable anthropogenic disturbances and climate change such as diseases and extreme weather (Van Oppen and Gates, 2006; ; Nakabayashi et al., 2019; ). In future conservation work, higher priority should be placed on these underappreciated yet potentially vulnerable marginal populations. Such marginal octocoral communities might play a disproportionate role in sustaining regional biodiversity by providing habitat that remains viable from intensifying climate change and anthropogenic disturbance.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
Ethics statement
Ethical approval was not required for the study involving animals in accordance with the local legislation and institutional requirements because local law does not require an approval for collection of octocoral samples in non-protected regions.
Author contributions
TJ: Conceptualization, Project administration, Investigation, Software, Methodology, Writing – original draft, Visualization, Data curation. LT: Writing – review & editing, Funding acquisition, Resources, Project administration, Conceptualization, Supervision.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The authors acknowledge a grant from the Marine Ecology Enhancement Fund (MEEF) (MEEF2022008) to LMT.
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.
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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.2026.1847700/full#supplementary-material
References
1
AdamT. C.BurkepileD. E.HolbrookS. J.CarpenterR. C.ClaudetJ.LoiseauC.et al. (2021). Landscape‐scale patterns of nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts. Ecol. Appl.31, e2227. doi: 10.1002/eap.2227
2
AguilarC.RainaJ. B.FôretS.HaywardD. C.LapeyreB.BourneD. G.et al. (2019). Transcriptomic analysis reveals protein homeostasis breakdown in the coral Acropora millepora during hypo-saline stress. BMC Genomics20, 148. doi: 10.1186/s12864-019-5527-2
3
AlexanderD. H.NovembreJ.LangeK. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19(9), 1655–1664. doi: 10.1101/gr.094052.109
4
AurelleD.PratlongM.OuryN.HaguenauerA.GélinP.MagalonH.et al. (2022). Species and population genomic differentiation in Pocillopora corals (Cnidaria, Hexacorallia). Genetica150(5), 247–262. doi: 10.1007/s10709-022-00165-7
5
BaumsI. B. (2008). A restoration genetics guide for coral reef conservation. Mol. Ecol.17, 2796–2811. doi: 10.1111/j.1365-294X.2008.03787.x
6
BhuyanM. S.JenzriM.AdikariD. (2025). Impacts of sedimentation on coral health and reef ecosystems: A comprehensive review. Mar. pollut. Bull.221, 118480. doi: 10.1016/j.marpolbul.2025.118480
7
BolgerA. M.LohseM.UsadelB. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30(15), 2114–2120. doi: 10.1093/bioinformatics/btu170
8
BrunnerC. A.UthickeS.RicardoG. F.HoogenboomM. O.NegriA. P. (2021). Climate change doubles sedimentation-induced coral recruit mortality. Sci. Total Environ.768, 143897. doi: 10.1016/j.scitotenv.2020.143897
9
BruttoS.ArculeoM.Stewart GrantW. (2011). Climate change and population genetic structure of marine species. Chem. Ecol.27, 107–119. doi: 10.1080/02757540.2010.547486
10
Buhl-MortensenL.MortensenP. B. (2005). “ Distribution and diversity of species associated with deep-sea gorgonian corals off Atlantic Canada,” in Cold-Water Corals and Ecosystems. Eds. FreiwaldA.RobertsJ. M. ( Springer, Berlin, Heidelberg), 849–879. Erlangen Earth Conference Series. doi: 10.1007/3-540-27673-4_44
11
Buitrago-LópezC.CárdenasA.HumeB. C.GosselinT.StaubachF.ArandaM.et al. (2023). Disparate population and holobiont structure of pocilloporid corals across the Red Sea gradient demonstrate species-specific evolutionary trajectories. Mol. Ecol.32, 2151–2173. doi: 10.1111/mec.16871
12
BurtJ. A.CampE. F.EnochsI. C.JohansenJ. L.MorganK. M.RieglB.et al. (2020). Insights from extreme coral reefs in a changing world. Coral Reefs39, 495–507. doi: 10.1007/s00338-020-01966-y
13
BurtT.BoardmanJ.FosterI.HowdenN. (2016). More rain, less soil: long‐term changes in rainfall intensity with climate change. Earth Surf. Processes Landforms41, 563–566. doi: 10.1002/esp.3868
14
CacciapagliaC.van WoesikR. (2016). Climate‐change refugia: Shading reef corals by turbidity. Glob. Change Biol. 22(3), 1145–1154. doi: 10.1111/gcb.13166
15
CampE. F.SchoepfV.MumbyP. J.HardtkeL. A.Rodolfo-MetalpaR.SmithD. J.et al. (2018). The future of coral reefs subject to rapid climate change: lessons from natural extreme environments. Front. Mar. Sci.5, 4. doi: 10.3389/fmars.2018.00004
16
CartwrightP. J.FearnsP. R.BransonP.CuttlerM. V.O’learyM.BrowneN. K.et al. (2021). Identifying metocean drivers of turbidity using 18 years of MODIS satellite data: implications for marine ecosystems under climate change. Remote Sens.13, 3616. doi: 10.3390/rs13183616
17
ChoiE. J.SongJ. I. (2007). Reproductive biology of the temperate soft coral Dendronephthya suensoni (Alcyonacea: Nephtheidae). Integr. Biosci.11, 215–225. doi: 10.1080/17386357.2007.9647338
18
DahanM.BenayahuY. (1997). Clonal propagation by the azooxanthellate octocoral Dendronephthya hemprichi. Coral Reefs16, 5–12. doi: 10.1007/s003380050053
19
DanecekP.AutonA.AbecasisG.AlbersC. A.BanksE.DePristoM. A.et al. (2011). The variant call format and VCFtools. Bioinformatics27(15), 2156–2158. doi: 10.1093/bioinformatics/btr330
20
DavisT. R.HarastiD.SmithS. D.KelaherB. P. (2016). Using modelling to predict impacts of sea level rise and increased turbidity on seagrass distributions in estuarine embayments. Estuar. Coast. Shelf Sci.181, 294–301. doi: 10.1016/j.ecss.2016.09.005
21
de Oliveira SoaresM. (2020). Marginal reef paradox: A possible refuge from environmental changes? Ocean Coast. Manage.185, 105063. doi: 10.1016/j.ocecoaman.2019.105063
22
DervicheP.MenegottoA.LanaP. (2022). Carbon budget trends in octocorals: a literature review with data reassessment and a conceptual framework to understand their resilience to environmental changes. Mar. Biol.169, 164. doi: 10.1007/s00227-022-04146-4
23
DupreyN. N.YasuharaM.BakerD. M. (2016). Reefs of tomorrow: eutrophication reduces coral biodiversity in an urbanized seascape. Global Change Biol.22, 3550–3565. doi: 10.1111/gcb.13432
24
FabriciusK. E. (2005). Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis. Mar. pollut. Bull.50, 125–146. doi: 10.1016/j.marpolbul.2004.11.028
25
FabriciusK.AldersladeP. (2001). Soft Corals and Sea Fans: A Comprehensive Guide to the Tropical Shallow Water Genera of the Central-West Pacific, the Indian Ocean and the Red Sea (Townsville: Australian Institute of Marine Science).
26
FabriciusK. E.BenayahuY.GeninA. (1995a). Herbivory in asymbiotic soft corals. Science268, 90–92. doi: 10.1126/science.268.5207.90
27
FabriciusK. E.GeninA.BenayahuY. (1995b). Flow‐dependent herbivory and growth in zooxanthellae‐free soft corals. Limnol. Oceanogr.40, 1290–1301. doi: 10.4319/lo.1995.40.7.1290
28
FabriciusK. E.McCorryD. (2006). Changes in octocoral communities and benthic cover along a water quality gradient in the reefs of Hong Kong. Mar. pollut. Bull.52, 22–33. doi: 10.1016/j.marpolbul.2005.08.004
29
FiferJ. E.YasudaN.YamakitaT.BoveC. B.DaviesS. W. (2022). Genetic divergence and range expansion in a western North Pacific coral. Sci. Total Environ.813, 152423. doi: 10.1016/j.scitotenv.2021.152423
30
FisherR.StarkC.RiddP.JonesR. (2015). Spatial patterns in water quality changes during dredging in tropical environments. PLoS One10, e0143309. doi: 10.1371/journal.pone.0143309
31
GlynnP. W.FeingoldJ. S.BakerA.BanksS.BaumsI. B.ColeJ.et al. (2018). State of corals and coral reefs of the Galápagos Islands (Ecuador): Past, present and future. Mar. pollut. Bull.133, 717–733. doi: 10.1016/j.marpolbul.2018.06.002
32
GoodkinN. F.SwitzerA. D.McCorryD.DeVantierL.TrueJ. D.HughenK. A.et al. (2011). Coral communities of Hong Kong: long-lived corals in a marginal reef environment. Mar. Ecol. Prog. Ser.426, 185–196. doi: 10.3354/meps09019
33
HarrisonP. J.YinK.LeeJ. H. W.GanJ.LiuH. (2008). Physical–biological coupling in the Pearl River estuary. Cont. Shelf Res.28, 1405–1415. doi: 10.1016/j.csr.2007.02.011
34
HedrickP. W. (2005). A standardized genetic differentiation measure. Evolution59, 1633–1648. doi: 10.1111/j.0014-3820.2005.tb01814.x
35
HohenloheP. A.FunkW. C.RajoraO. P. (2020). Population genomics for wildlife conservation and management. Mol. Ecol.30, 62–82. doi: 10.1111/mec.15720
36
HolmO. (1895). Beiträge zur kenntniss der alcyonidengattung Spongodes Lesson. Zoologische Jahrbucher (Syst)8(1), 8–57.
37
HwangS. J.SongJ. I. (2007). Reproductive biology and larval development of the temperate soft coral Dendronephthya gigantea (Alcyonacea: Nephtheidae). Mar. Biol.152, 273–284. doi: 10.1007/s00227-007-0679-z
38
HwangS. J.SongJ. I. (2012). Sexual reproduction of the soft coral Dendronephthya castanea (Alcyonacea: Nephtheidae). Anim. Cells Syst.16, 135–144. doi: 10.1080/19768354.2011.622486
39
JeonY.ParkS. G.LeeN.WeberJ. A.KimH. S.HwangS. J.et al. (2019). The draft genome of an octocoral, Dendronephthya gigantea. Genome Biol. Evol. 11(3), 949–953. doi: 10.1093/gbe/evz043
40
JohannessonK.AndreC. (2006). Invited review: Life on the margin: genetic isolation and diversity loss in a peripheral marine ecosystem, the Baltic Sea. Mol. Ecol.15, 2013–2029. doi: 10.1111/j.1365-294X.2006.02919.x
41
KahngS. E.BenayahuY.LaskerH. R. (2011). Sexual reproduction in octocorals. Mar. Ecol. Prog. Ser.443, 265–283. doi: 10.3354/meps09414
42
KalyaanamoorthyS.MinhB. Q.WongT. K.Von HaeselerA.JermiinL. S. (2017). ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods14, 587–589. doi: 10.1038/nmeth.4285
43
KolzenburgR. (2022). The direct influence of climate change on marginal populations: a review. Aquat. Sci.84, 24. doi: 10.1007/s00027-022-00856-5
44
KukenthalW. (1905). Versuch einer Revision der Alcyonarien. II. Die Familie der Nephthyiden. 2. Teil. Die Gattungen Dendronephthya ng und Stereonephthya ng. Zoologische Jahrbucher (Abtheilung fur Systematik, Geographie und Biologie der Thiere)21, 26–32.
45
LaiR. W.PerkinsM. J.HoK. K.AstudilloJ. C.YungM. M.RussellB. D.et al. (2016). Hong Kong’s marine environments: History, challenges and opportunities. Reg. Stud. Mar. Sci.8, 259–273. doi: 10.1016/j.rsma.2016.09.001
46
LarkinM. F.DavisT. R.HarastiD.SmithS. D.AinsworthT. D.BenkendorffK. (2023). A glimmer of hope for an Endangered temperate soft coral: the first observations of reproductive strategies and early life cycle of Dendronephthya australis (Octocorallia: Malacalcyonacea). Mar. Biol.170, 146. doi: 10.1007/s00227-023-04298-x
47
LaskerH. R. (1990). Clonal propagation and population dynamics of a gorgonian coral. Ecology71, 1578–1589. doi: 10.2307/1938293
48
LealM. C.Ferrier-PagèsC.CaladoR.BrandesJ. A.FrischerM. E.NejstgaardJ. C. (2014). Trophic ecology of the facultative symbiotic coral Oculina arbuscula. Mar. Ecol. Prog. Ser.504, 171–179. doi: 10.3354/meps10750
49
LedouxJ. B.CruzF.Gómez-GarridoJ.AntoniR.BlancJ.Gómez-GrasD.et al. (2020). The genome sequence of the octocoral Paramuricea clavata–a key resource to study the impact of climate change in the Mediterranean. G3: Genes Genom. Genet. 10(9), 2941–2952. doi: 10.1534/g3.120.401371
50
LetunicI.BorkP. (2021). Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49(W1), W293–W296. doi: 10.1093/nar/gkab301
51
LewisP. O. (2001). A likelihood approach to estimating phylogeny from discrete morphological character data. Syst. Biol.50, 913–925. doi: 10.1080/106351501753462876
52
LiH.DurbinR. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics25(14), 1754–1760. doi: 10.1093/bioinformatics/btp324
53
LiH.HandsakerB.WysokerA.FennellT.RuanJ.HomerN.et al. (2009). The sequence alignment/map format and SAMtools. Bioinformatics25(16), 2078–2079. doi: 10.1093/bioinformatics/btp352
54
ManullangC.HanaharaN.TariganA. I.AbeY.FurukawaM.MoritaM. (2025). Slight thermal stress exerts genetic diversity selection at coral (Acropora digitifera) larval stages. BMC Genomics26, 36. doi: 10.1186/s12864-024-11194-1
55
MatiasA. M. A.PopovicI.ThiaJ. A.CookeI. R.TordaG.LukoschekV.et al. (2023). Cryptic diversity and spatial genetic variation in the coral Acropora tenuis and its endosymbionts across the Great Barrier Reef. Evol. Appl.16, 293–310. doi: 10.1111/eva.13435
56
McCorryD. (2002). Hong Kong’s Scleractinian Coral Communities (Hong Kong: The University of Hong Kong). (doctoral dissertation). Available online at: https://www.airitilibrary.com/Article/Detail?DocID=U0029-1812201200007897 (Accessed March 3, 2026).
57
McKennaA.HannaM.BanksE.SivachenkoA.CibulskisK.KernytskyA.et al. (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20(9), 1297–1303. doi: 10.1101/gr.107524.110
58
MeloniM.ReidA.Caujapé-CastellsJ.MarreroA.Fernández-PalaciosJ. M.Mesa-CoeloR. A.et al. (2013). Effects of clonality on the genetic variability of rare, insular species: the case of Ruta microcarpa from the Canary Islands. Ecol. Evol.3, 1569–1579. doi: 10.1002/ece3.571
59
MinhB. Q.NguyenM. A. T.Von HaeselerA. (2013). Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol.30, 1188–1195. doi: 10.1093/molbev/mst024
60
MorganK. M.MoynihanM. A.SanwlaniN.SwitzerA. D. (2020). Light limitation and depth-variable sedimentation drives vertical reef compression on turbid coral reefs. Front. Mar. Sci. 7, 571256. doi: 10.3389/fmars.2020.571256
61
MortonB.MortonJ. (1983). The Sea Shore Ecology of Hong Kong Vol. 1 (Hong Kong: Hong Kong University Press).
62
MundayP. L.WarnerR. R.MonroK.PandolfiJ. M.MarshallD. J. (2013). Predicting evolutionary responses to climate change in the sea. Ecol. Lett.16, 1488–1500. doi: 10.1111/ele.12185
63
NakabayashiA.YamakitaT.NakamuraT.AizawaH.KitanoY. F.IguchiA.et al. (2019). The potential role of temperate Japanese regions as refugia for the coral Acropora hyacinthus in the face of climate change. Sci. Rep.9, 1892. doi: 10.1038/s41598-018-38333-5
64
NgW. C.MortonB. (2003). Genetic structure of the scleractinian coral Platygyra sinensis in Hong Kong. Mar. Biol.143, 963–968. doi: 10.1007/s00227-003-1159-8
65
NguyenL. T.SchmidtH. A.Von HaeselerA.MinhB. Q. (2015). IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol.32, 268–274. doi: 10.1093/molbev/msu300
66
OleksiakM. F.RajoraO. P. (2019). Marine Population Genomics: Challenges and Opportunities. 3–35, Population genomics: Marine organisms. (Cham: Springer). doi: 10.1007/13836_2019_70
67
OrtizE. M. (2019). vcf2phylip v2.0: convert a VCF matrix into several matrix formats for phylogenetic analysis. Zenodo. doi: 10.5281/zenodo.2540861
68
PembletonL. W.CoganN. O.ForsterJ. W. (2013). St AMPP: An R package for calculation of genetic differentiation and structure of mixed‐ploidy level populations. Mol. Ecol. Resour.13, 946–952. doi: 10.1111/1755-0998.12129
69
PerryC. T.LarcombeP. (2003). Marginal and non-reef-building coral environments. Coral Reefs22, 427–432. doi: 10.1007/s00338-003-0330-5
70
PrimovK. D.BurdickD. R.LemerS.ForsmanZ. H.ComboschD. J. (2024). Genomic data reveals habitat partitioning in massive Porites on Guam, Micronesia. Sci. Rep.14, 17107. doi: 10.1038/s41598-024-67992-w
71
PurcellS.NealeB.Todd-BrownK.ThomasL.FerreiraM. A.BenderD.et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Gen. 81(3), 559–575. doi: 10.1086/519795
72
RieglB.BranchG. M. (1995). Effects of sediment on the energy budgets of four scleractinian (Bourne 1900) and five alcyonacean (Lamouroux 1816) corals. J. Exp. Mar. Biol. Ecol. 186(2), 259–275. doi: 10.1016/0022-0981(94)00164-9
73
RöthigT.Trevathan-TackettS. M.VoolstraC. R.RossC.ChaffronS.DurackP. J.et al. (2023). Human-induced salinity changes impact marine organisms and ecosystems. Global Change Biol.29, 4731–4749. doi: 10.1111/gcb.16859
74
SagarinR. D.GainesS. D.GaylordB. (2006). Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol. Evol.21, 524–530. doi: 10.1016/j.tree.2006.06.008
75
SchleyerM. H.FlorosC.LaingS. C.MacdonaldA. H.Montoya-MayaP. H.MorrisT.et al. (2018). What can South African reefs tell us about the future of high-latitude coral systems? Mar. pollut. Bull.136, 491–507. doi: 10.1016/j.marpolbul.2018.09.014
76
ShinzatoC.MungpakdeeS.ArakakiN.SatohN. (2015). Genome-wide SNP analysis explains coral diversity and recovery in the Ryukyu Archipelago. Sci. Rep. 5(1), 18211. doi: 10.1038/srep18211
77
StathamP. J. (2012). Nutrients in estuaries—An overview and the potential impacts of climate change. Sci. Total Environ.434, 213–227. doi: 10.1016/j.scitotenv.2011.09.088
78
SteinbergR. K.DaffornK. A.AinsworthT.JohnstonE. L. (2020). Know thy anemone: a review of threats to octocorals and anemones and opportunities for their restoration. Front. Mar. Sci. 7, 590. doi: 10.3389/fmars.2020.00590
79
ThomsonJ. A.MackinnonD. L. (1910). Alcyonarians collected on the Percy Sladen Trust Expedition by Mr. J. Stanley Gardiner. Part 2, the Stolonifera, Alcyonacea, Pseudaxonia, and Stelechotokea. Trans. Linn. Soc. Lond. 13(8), 165–211.
80
TsengL. C.DahmsH. U.HsuN. J.HwangJ. S. (2011). Effects of sedimentation on the gorgonian Subergorgia suberosa (Pallas 1766). Mar. Biol.158, 1301–1310. doi: 10.1007/s00227-011-1649-z
81
TuttleL. J.DonahueM. J. (2022). Effects of sediment exposure on corals: a systematic review of experimental studies. Environ. Evidence11, 4. doi: 10.1186/s13750-022-00256-0
82
VerrillA. H. (1864). List of the polyps and corals sent by the Museum of Comparative Zoology to other institutions in exchange, with annotations. Bull. Mus. Comp. Zool. 1, 29–60.
83
van OppenM. J. H.GatesR. D. (2006). Conservation genetics and the resilience of reef-building corals. Mol. Ecol.15, 3863–3883. doi: 10.1111/j.1365-294X.2006.03026.x
84
VucetichJ. A.WaiteT. A. (2003). Spatial patterns of demography and genetic processes across the species' range: null hypotheses for landscape conservation genetics. Conserv. Genet.4, 639–645. doi: 10.1023/A:1025671831349
85
WangI. J.BradburdG. S. (2014). Isolation by environment. Mol. Ecol.23, 5649–5662. doi: 10.1111/mec.12938
86
WeberM.De BeerD.LottC.PolereckyL.KohlsK.AbedR. M.et al. (2012). Mechanisms of damage to corals exposed to sedimentation. P. Natl. A. Sci. 109(24), E1558–E1567. doi: 10.1073/pnas.1100715109
87
WilliamsonJ. E.GillingsM. R.NevatteR. J.HarastiD.RaoultV.GhalyT. M.et al. (2022). Genetic differentiation in the threatened soft coral Dendronephthya australis in temperate eastern Australia. Austral Ecol. 47(4), 804–817. doi: 10.1111/aec.13160
88
WinterD. J. (2012). MMOD: an R library for the calculation of population differentiation statistics. Mol. Ecol. Resour.12, 1158–1160. doi: 10.1111/j.1755-0998.2012.03174.x
89
XieJ. Y.LauD. C.KeiK.YuV. P.ChowW. K.QiuJ. W. (2017). The 2014 summer coral bleaching event in subtropical Hong Kong. Mar. pollut. Bull.124, 653–659. doi: 10.1016/j.marpolbul.2017.03.061
90
YeungC. W.CheangC. C.LeeM. W.FungH. L.ChowW. K.AngP. (2014). Environmental variabilities and the distribution of octocorals and black corals in Hong Kong. Mar. pollut. Bull.85, 774–782. doi: 10.1016/j.marpolbul.2013.12.043
91
YeungY. H.XieJ. Y.KwokC. K.KeiK.AngP.ChanL. L.et al. (2021). Hong Kong's subtropical scleractinian coral communities: Baseline, environmental drivers and management implications. Mar. pollut. Bull.167, 112289. doi: 10.1016/j.marpolbul.2021.112289
92
ZayasuY.TakeuchiT.NagataT.KanaiM.FujieM.KawamitsuM.et al. (2021). Genome-wide SNP genotyping reveals hidden population structure of an acroporid species at a subtropical coral island: implications for coral restoration. Aquat. Conservation: Mar. Freshw. Ecosyst.31, 2429–2439. doi: 10.1002/aqc.3626
93
ZweiflerA.O’learyM.MorganK.BrowneN. K. (2021). Turbid coral reefs: past, present and future—a review. Diversity13, 251. doi: 10.3390/d13060251
Summary
Keywords
Dendronephthya, genetic diversity, marginal environment, Octocorallia, South China Sea
Citation
Jong TC and Tsang LM (2026) Population genomics on octocorals in marginal environments: resilient but vulnerable refugia for corals under the Anthropocene. Front. Mar. Sci. 13:1847700. doi: 10.3389/fmars.2026.1847700
Received
04 April 2026
Revised
16 June 2026
Accepted
30 June 2026
Published
24 July 2026
Corrected
28 July 2026
Volume
13 - 2026
Edited by
Wei Jiang, Guangxi University, China
Reviewed by
Pedro A. S. Longo, Federal University of São Paulo, Brazil
Bharath Subramanyam Ammanabrolu, Council of Scientific and Industrial Research (CSIR), India
Cristiana Manullang, Shantou University, China
Updates
Copyright
© 2026 Jong and Tsang.
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: Ling Ming Tsang, lmtsang@cuhk.edu.hk
Disclaimer
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