Biodiversity of Nematodes from Coral Reef Sediments in the South China Sea Based on eDNA Metabarcoding

Abstract

Coral reef ecosystems in the South China Sea are one of the globally important marine biodiversity hotspots. However, there are few studies on nematode biodiversity in coral reef sediments. Here, we assessed nematode biodiversity in coral reef sediments in the South China Sea using eDNA metabarcoding. Eight sampling stations were set up in the region north of the South China Sea Basin (Xisha and Zhongsha Islands) and south of it (Nansha Islands), respectively. We also compared and analysed the nematode biodiversity and community structure in different regions, as well as the effects of environmental factors on the community structure. The results showed that a total of 503 operational taxonomic units (OTUs) were detected in 16 sediment samples. The nematodes identified belong to two classes, 10 orders, 37 families, 51 genera and 61 species, and all of them were Enoplea and Chromadorea, except for the unidentified taxa, which accounted for 97.26% of the total abundance. The analyses showed that the northern region had higher nematode abundance and diversity relative to the southern region. However, there was no significant difference in the nematode community structure between the two regions, which was less affected by geographic location. Similarly, environmental factors (total nitrogen, total phosphorus, copper, zinc, cadmium, lead and arsenic) have an effect on the abundance of specific nematode groups but not on the overall community structure. In summary, this study initially reveals the composition and diversity of nematode communities in coral reef sediments in the South China Sea and provides an important reference for further in-depth study of the South China Sea ecosystem.

1. Introduction

Coral reef ecosystems, often referred to as the tropical rainforests of the ocean due to their high biodiversity and ecosystem service value, continue to support thousands of marine species despite the pressures of global climate change and human activities [1]. Particularly in the South China Sea, the coral reefs not only boast rich biodiversity but also occupy a significant position in economic activities [2]. However, scientific research on the coral reef ecosystems in this region, especially studies on meiofauna communities, remains relatively undeveloped.

Nematodes, as a crucial part of the benthic meiofauna community in marine ecosystems, play a key role in coral reef ecosystems [3]. These minute organisms are not only vital components of the food web but also serve as indicator species for environmental health [4]. The diversity and community structure of nematodes reflects the health of ecosystems and biogeographical distributions [5]. However, despite the recognized importance of nematodes, studies on their diversity in the coral reef sediments of the South China Sea are still very limited.

Traditional biodiversity research methods primarily rely on morphological features for species identification and classification. While historically significant, this approach has clear limitations when dealing with large-scale samples or species that are difficult to distinguish [6]. In recent years, the development of molecular biology techniques, especially the application of environmental DNA (eDNA) metabarcoding, has provided new perspectives and tools for biodiversity research [7]. By analysing the DNA in environmental samples, this technique allows for rapid and efficient monitoring and assessment of biological communities, making it particularly suited for studying minute or cryptic species that are challenging to identify through traditional methods [8].

Thus, using eDNA metabarcoding technology, this study conducted a comprehensive analysis of the nematode community’s biodiversity and structure in the coral reef sediments of the South China Sea. Through this method, we were able to monitor the dynamics of coral reef nematode communities non-invasively and assess the health of ecosystems on a larger geographical scale, promptly identifying ecological risks and potential crises and providing a basis for scientific management and conservation measures.

2. Materials and Methods

2.1. Sample Collection

The samples for this study were collected in April–May 2023 in the coral reef region of the Nansha Islands (three stations on Beiwai shoal (BW1, BW2 and BW3); two stations on Meiji Reef (MJ2 and MJ4); one station on Chigua Reef (CG1); one station on Dongmen Reef (DM1); and one station on Zhangxi Reef (ZX1)), the Zhongsha Islands (one station on Walker Shoal: (MB)) and the Xisha Islands (three stations on North Reef (BJ1, BJ3 and BJ4); one station on Ganquan Reef (GQ); one station on Shi island (SY); two stations on Qilian island (QLY1, QLY2)), with a total of 16 sampling stations. As the sampling stations are separated by the South China Sea basin, the sampling stations in the Xisha Islands and the Zhongsha Islands were divided into the northern region, and those in the Nansha Islands were divided into the southern region (Figure 1). Surface sediments were collected on-site by divers using sterile sampling bags in water depths of 10–20 m and subsequently placed in an −80 °C refrigerator for preservation. Sampling stations were mapped by the software Ocean Data View (ODV, 4.7.10).

2.2. Environmental Factor Parameter Acquisition

Thirty grams of sediment samples were placed in a freeze-dryer and lyophilised for 24 h. The dried samples were then ground into powder using a mortar and pestle and transferred into 50 mL sterile centrifuge tubes. These samples were sent to the Institute of Soil Science, Chinese Academy of Sciences in Nanjing for the determination of environmental factors, including nutrients (total nitrogen (TN), total phosphorus (TP)) and heavy metal elements (copper (Cu), zinc (Zn), cadmium (Cd), lead (Pb) and arsenic (As)).

2.3. High-Throughput Sequencing

In this study, genomic DNA was extracted from sediment samples using the DNeasy PowerSoil Kit (QIAGEN, Hilden, Germany) according to the instructions, and integrity was examined using 1% agarose gel electrophoresis. Marine nematode-specific primers were selected for PCR amplification of 18S rRNA gene [9], and the primer sequences were A-NF1: 5′-GCCTCCCTCGCGCCATCAGGGGTGGTGCATGGCCGTTCTTAGTT-3′ and B-18Sr2b: 5′-GCCTTGCCAGCCCGCTCAGTACAAAGGGCAGGGACGTAAT-3′. PCR was performed using TransStart Fastpfu DNA Polymerase (TransGen Biotech) with a 20 μL reaction system in the following amplification procedure: pre-denaturation at 95 °C for 5 min, followed by 35 cycles (denaturation at 95 °C for 30 s, annealing at 58 °C for 30 s, and extension at 72 °C for 45 s), and finally extension at 72 °C for 10 min. Three replicates were set up for all the samples, and the PCR products of the same samples were mixed and checked by electrophoresis on 2% agarose gel. Qualified PCR products were subjected to library preparation and high-throughput sequencing on the Illumina MiSeq PE250 sequencing platform. The results were stored in FASTQ file format and uploaded to NCBI, BioProject: PRJNA1101407.

2.4. Data Quality Control and Processing

Firstly, Raw Reads were filtered using Trimmomatic [10], and adaptor sequence and primer sequences were identified and removed by using cutadapt [11] to obtain Clean Reads. Double-ended reads were spliced using USEARCH (8.1) [12]. We removed chimeras using UCHIME [13] to obtain high-quality sequences for subsequent analyses. Those Sequences were clustered at a 97% similarity level using USEARCH [12], and OTUs were filtered by default parameters with a threshold of 0.005% of total number of all sequences sequenced. OTUs are clusters of similar sequences that are used to classify groups of closely related individuals. In microbial ecology, OTUs are often used as a proxy for species or other taxonomic units, especially when exact taxonomic identification is challenging. By clustering sequences based on similarity, OTUs provide a practical way to analyze and compare community composition and diversity. Taxonomical annotation was performed using the Naive Bayes Classifier with a reference database (SILVA 138). All optimised sequences were mapped to OTU representative sequences to generate OTU tables (Appendix A).

2.5. Statistical Analysis

The region of this study is located in the Nansha Islands, Zhongsha Islands and Xisha Islands in the South China Sea; due to the separation of the South China Sea Basin, the above region is divided into the northern region (Xisha Islands and Zhongsha Islands) and the southern region (Nansha Islands). The nematode biodiversity and community structure in different regions and the effects of environmental factors on the community structure were analysed. The Alpha diversity index of the samples was assessed using QIIME2 [14]; Beta diversity analysis was performed using QIIME [15] to evaluate nematode biodiversity in different samples. Linear Discriminant Analysis (LDA) was performed on the samples according to different grouping conditions based on taxonomic composition using LEfSE to identify communities or species that significantly differed in their impact on sample partitioning [16]. Redundancy Analysis (RDA) was performed using CANOCO5 [17] to analyse the effect of environmental factors on community structure. Correlation mapping was performed based on R language (R4.2.2), Origin (9.9) and EXCEL (2021).

3. Results

3.1. Valid Sequences and OTUs

From 16 sediment samples collected from coral reefs in the South China Sea, the 18S rRNA gene of marine nematodes was amplified and subjected to high-throughput sequencing. A total of 1,769,042 sequences were obtained, of which 1,659,263 valid sequences remained after data purification for subsequent analysis (

Table 1. Statistics of sequences among two regions of the South China Sea.

Group	Station	Input	Merged	Filtered
Northern	SY	130,251	124,931	121,859
	GQ	114,936	111,734	108,498
	QLY1	155,331	146,627	140,371
	QLY2	73,950	72,626	70,182
	BJ1	138,678	135,313	131,186
	BJ3	112,546	110,911	107,886
	BJ4	123,672	121,653	117,627
	MB	106,069	103,946	100,938
Southern	BW1	145,553	140,193	136,322
	BW2	134,999	132,028	127,920
	BW3	79,462	73,205	70,437
	MJ4	111,306	109,822	106,471
	MJ2	69,361	64,965	63,315
	DM1	101,336	95,673	93,962
	ZX1	112,938	110,658	106,448
	CG1	58,654	57,591	55,841

). Approximately 99.26% of these sequences ranged from 310 to 330 bp in length. Annotation and screening were conducted using the SILVA138 database. Among the annotated sequences, 503 OTUs were identified as marine nematodes. There were 52 OTUs common to both the northern and southern regions, with 257 unique OTUs in the northern region and 194 unique OTUs in the southern region.

Table 2. Species annotations of shared OTUs are in high abundance in two regions of the South China Sea.

OTUID	Class	Order	Family	Genus	Species
OTU1	Enoplea	Mermithida	Mermithidae	Hexamermis	Hexamermis albicans
OTU2	Chromadorea	Rhabditida	Cephalobidae	Acrobeles	Acrobeles sp. MA-2012
OTU3	Chromadorea	Rhabditida	Criconematidae	Criconemoides	Criconemoides myungsugae
OTU4	Chromadorea	Rhabditida	Anguinidae	Subanguina	Subanguina radicicola
OTU5	Chromadorea	Plectida	Ceramonematidae	Ceramonema	Ceramonema altogolfi
OTU6	Enoplea	Enoplida	Ironidae	Syringolaimus	Syringolaimus sp. 1060
OTU7	Enoplea	Enoplida	Alaimidae	Alaimus	Alaimus sp. 1247
OTU8	Chromadorea	Rhabditida	Cephalobidae	Acrobeles	Acrobeles sp. MA-2012
OTU9	Enoplea	Dorylaimida	Aporcelaimidae	Aporcelaimellus	Aporcelaimellus sp. JH-2004
OTU10	Chromadorea	Rhabditida	Cephalobidae	Acrobeles	Acrobeles sp. MA-2012
OTU16	Enoplea	Dorylaimida	Aporcelaimidae	Aporcelaimellus	Aporcelaimellus sp. JH-2004
OTU18	Chromadorea	Rhabditida	Cephalobidae	Acrobeles	Acrobeles sp. MA-2012
OTU19	Chromadorea	Rhabditida	Cephalobidae	Acrobeloides	Acrobeloides sp. MCt34
OTU21	Chromadorea	Rhabditida	Cephalobidae	Acrobeles	Acrobeles sp. MA-2012
OTU25	Chromadorea	Rhabditida	Cephalobidae	Acrobeles	Acrobeles sp. MA-2012
OTU27	Enoplea	Dorylaimida	Aporcelaimidae	Aporcelaimellus	Aporcelaimellus sp. JH-2004

presents species annotations of shared OTUs with high abundance in both regions. At the class level, Enoplea and Chromadorea were the dominant groups. At the order level, the primary groups included Mermithida, Rhabditida, Plectida, Enoplida, Dorylaimida. At the family level, the major families observed were Mermithidae, Cephalobidae, Criconematidae, Anguinidae, Ceramonematidae, Ironidae, Alaimidae, Aporcelaimidae. At the genus level, Hexamermis, Acrobeles, Criconemoides, Subanguina, Ceramonema, Syringolaimus, Alaimus, Aporcelaimellus were predominant.

3.2. Alpha Diversity

The alpha diversity indices based on the OTU level for each sample (

Table 3. Alpha diversity index among two regions in the South China Sea.

Group	Station	Richness Index	Shannon Index	Simpson Index	Pielou Index
Northern	SY	50.1	2.8224	0.8392	0.4999
	GQ	159.1	5.3288	0.9663	0.7286
	QLY1	12.8	0.0080	0.0010	0.0022
	QLY2	81.1	1.9746	0.7051	0.3114
	BJ1	14.6	0.0195	0.0031	0.0051
	BJ3	19.9	1.5835	0.6572	0.3680
	BJ4	28.1	0.0323	0.0046	0.0067
	MB	37.9	0.9829	0.4703	0.1877
Southern	BW1	82.8	2.1153	0.7329	0.3320
	BW2	21.4	0.9608	0.4638	0.2179
	BW3	14.6	0.7314	0.3131	0.1897
	MJ4	23.4	0.1444	0.0338	0.0318
	MJ2	103.5	2.2335	0.7103	0.3337
	DM1	12	0.6860	0.2506	0.1916
	ZX1	11.1	0.0085	0.0011	0.0024
	CG1	35.2	0.0386	0.0054	0.0075

) revealed that the northern region exhibited higher alpha diversity indices compared to the southern region. To visualise the differences in nematode biodiversity between regions, box plots of the four alpha diversity indices were generated based on the OTU level (Figure 2). The medians of the alpha diversity indices were higher in the northern region than in the southern region, and the lengths of the upper and lower quartile ranges of the alpha diversity indices were also greater, indicating higher nematode abundance and evenness in the northern region. Additionally, one station in the northern region (GQ) and one station in the southern region (MJ2) exhibited significantly higher nematode biomass compared to other stations within their respective regions.

3.3. Species Composition and Relative Abundance

The results of the OTU sequence comparison revealed that the nematodes identified in the coral reef area of the South China Sea belong to two classes, 10 orders, 37 families, 51 genera and 61 species. At the class level, excluding unidentified taxa, the nematodes are predominantly Chromadorea (56.53%) and Enoplea (41.87%). At the order level, the dominant groups are Rhabditida (49.65%), Enoplida (15.21%), Dorylaimida (13.70%), Mermithida (12.74%). At the family level, the major observed families are Cephalobidae (26.89%), Criconematidae (9.93%), Aporcelaimidae (13.61%), Mermithidae (12.74%), Alaimidae (8.33%). At the genus level, the primary genera include Acrobeles (24.56%), Aporcelaimellus (13.61%), Hexamermis (12.74%), Criconemoides (9.93%), Alaimus (8.33%). At the species level, the dominant species are Acrobeles sp. MA-2012 (24.56%), Aporcelaimellus sp. JH-2004 (13.61%), Hexamermis albicans (12.74%), Criconemoides myungsugae (9.93%), Alaimus sp. 1247 (8.32%) (Figure 3).

In the northern and southern regions of the study area, the dominant classes are Enoplea and Chromadorea. In the northern region, the proportions of these two classes are approximately equal, whereas in the southern region, Chromadorea is more prevalent. The dominant orders are Rhabditida, Enoplida, Dorylaimida, Mermithida. The dominant families include Cephalobidae, Aporcelaimidae, Mermithidae. The dominant genera are Acrobeles, Aporcelaimellus, Hexamermis. Figure 4 illustrates the relative abundance of different taxonomic levels (classes, orders, families, genera) in the northern and southern regions. Overall, the differences between the dominant groups in the northern and southern regions are minimal, with the southern region exhibiting a greater number of dominant groups.

3.4. Community Structure and Differential Groups

To explore the similarities and differences in nematode communities between different regions, Beta diversity was assessed using various similarity matrices based on the abundance–Jaccard algorithm. Principal Component Analysis (PCA) revealed (Figure 5A) that the stations were not significantly dispersed, indicating that the nematode community structure was relatively consistent across stations. Notably, MB and GQ, as well as MJ2 and QLY2, were closer together, suggesting a higher degree of similarity in nematode community structure between these pairs of stations. Additionally, some overlapping areas in the elliptical confidence zones of the two regions indicate that the nematode community structures of certain stations in the northern and southern regions share similarities. Furthermore, the UPGMA clustering tree and abundance histogram at the family level (Figure 5B) showed no distinct clustering between the two regions, reinforcing the observation that there are no significant differences in nematode community structure between the northern and southern regions.

The LEfSe-based analysis (Figure 6) revealed more categories of significantly different impacts in the northern region compared to the southern region. In the northern region, the significant taxa include Diplogasteridae, Oxystominidae and Haliplectidae at the family level and Pristionchus, Pseudodiplogasteroides and Haliplectus at the genus level. In the southern region, the significant taxa are Mermithida at the order level, Mermithidae at the family level and Hexamermis at the genus level.

3.5. Effects of Environmental Factors on Community Structure

Detrended Correspondence Analysis (DCA) was performed on the nematode abundance matrix of South China Sea coral reefs. The results showed that the axis lengths of the gradient were less than 3.0, prompting the selection of Redundancy Analysis (RDA) to explore the relationship between environmental factors and nematode community structure. The environmental factors considered included conventional indices (latitude (LAT), total nitrogen (TN), total phosphorus (TP)) and heavy metal indicators (copper (Cu), zinc (Zn), cadmium (Cd), lead (Pb), arsenic (As)). Nematodes were analysed at the genus level. The RDA results (Figure 7) indicated that the first two ordination axes contributed 31.48% and 24.3% of the explanatory power for the nematode community structure. There was no obvious clustering of stations in the northern and southern regions, suggesting that environmental factors did not significantly affect the nematode communities in these regions. Specific influences of environmental factors were observed as follows: Latitude has a greater effect on BJ3 at the station; TN has a greater effect on MB at the station and on Aporcelaimellus, Pristionchus, Halicephalobus at the community structure; TP has a greater effect on BJ1 and CG1 at the station and on Acrobeles at the community structure; Cu and Pb have a greater effect on GQ and MJ4 at the station; Cd has a greater effect on Criconemoides at the community structure; and As has a greater effect on BW2 and DM1 at the station and on Ceramonema at the community structure.

4. Discussion

4.1. Nematode Biodiversity in the South China Sea Coral Reefs

In this study, we monitored the nematode biodiversity of coral reef sediments in the South China Sea using eDNA metabarcoding sequencing. A total of 503 OTUs were detected in 16 sediment samples, with the dominant groups being Enoplea and Chromadorea, accounting for 97.26% of the total abundance. Similar findings have been reported in other coral reef ecosystems. For instance, a study on the morphological identification of 2010 nematodes from the Maldives coral reefs revealed that all 173 identified genera belonged to Enoplea and Chromadorea [18]. In comparison, this study identified more than 30 families across the two coral reef regions, with seven families being common to both regions. However, only 61 genera were identified at the genus level, indicating a significant gap compared to the Maldives coral reef region. This discrepancy may be attributed to the lower resolution at the genus level, resulting in some unidentified genera. Nematodes also dominate meiofauna in extreme marine environments such as cold seeps [19], hydrothermal vents [20] and hypersaline environments [21]. Additionally, some unidentified taxa were observed in this study, potentially due to the presence of new species, an incomplete reference database, degradation of DNA fragments, or varying amplification efficiencies of primers. With continuous improvements in reference databases and advancements in science and technology, it is expected that these unidentified OTUs will be more accurately identified and classified in the future.

4.2. Community Variation and LEfSe Analyses

In this study, the investigation region was divided into northern and southern sections based on spatial latitude. The nematode community structure in different coral reef regions of the South China Sea was explored using eDNA metabarcoding sequencing technology. LEfSe analysis is known for revealing significant differences among multiple groups [22]. A previous study utilising LEfSe showed that nematodes were more readily detected in sediment eDNA, whereas echinoderms were more readily detected in water eDNA [23]. Although no significant difference in the overall nematode community structure between the northern and southern regions was observed in this study, LEfSe analysis still highlighted some taxa with specific distributions. In the northern region, the families Diplogasteridae, Oxystominidae and Haliplectidae were detected, with percentages of 0.41%, 0.3% and 0.3%, respectively. These families were not detected in the southern region, indicating a subtle but noteworthy difference that underscores the sensitivity of LEfSe analysis. In the southern region, Mermithida was identified as a significant group, comprising 12.98% of the total nematode population. In the northern region, Mermithida accounted for approximately 12.5%, a similar percentage. However, this similarity may be attributed to the uneven distribution of nematodes, primarily observed at station QLY1, which represented over 99.9% of the Mermithida detected in the northern region.

4.3. Environmental Factors and Community Structure

The nematode community structure in different regions may be influenced by the concentration of various elements, such as nitrogen and phosphorus, which are major nutrient elements in the water. Appropriate amounts of nitrogen and phosphorus can provide the nutrients required by meiofauna to promote their growth and reproduction. However, when the concentrations of these elements are too high, they may produce negative effects, such as eutrophication and hypoxia [24]. Additionally, high concentrations of certain metal elements can have toxic effects on meiofauna, affecting their survival and reproduction and potentially leading to death [25]. The present study showed that nutrient elements and heavy metal elements did not have a significant effect on the overall community structure of nematodes. Specific environmental factors impacted individual nematode types but did not significantly affect other species, thus masking the effect on the overall community structure. Although the two regions are geographically distant from each other, latitude did not have a significant impact on the nematode community structure. This lack of effect may be attributed to the shallow sampling depth and the strong ecological adaptability of nematodes in the tropical region, allowing them to maintain a relatively stable community structure. In addition to the aforementioned environmental factors, other factors such as temperature, salinity and pH may also affect nematode community structure, though these were beyond the scope of this research.

4.4. Nematode Biodiversity Assessment Methods

Nematodes are the most abundant taxa of benthic meiofauna and can be found in various ecosystems, ranging from shallow waters to the deep sea. The species composition of nematodes not only offers valuable insights into the ecosystems they inhabit but also serves as a widely recognised bioindicator [26]. Despite two centuries of research, the number of marine nematode species identified through morphological methods is about 6900, representing only 19% of the estimated total number of species [27]. Nematode identification primarily relies on morphological features, but this approach is often biased due to the time-consuming nature of the process, the high phenotypic plasticity of nematodes and the scarcity of distinct taxonomic features [28,29]. In contrast, combining morphological observation with molecular biology techniques provides a new approach to assessing nematode biodiversity [30]. Molecular biology techniques can be effectively used for large-scale assessment of nematode biodiversity across various ecosystems. This not only improves the efficiency of identification but also enables the detection of species that are difficult to observe [31]. Additionally, it helps resolve the problem of identifying cryptic species [26]. In this context, eDNA metabarcoding sequencing technology emerges as a promising approach. The use of eDNA technology in environmental monitoring is rapidly expanding, with applications in fisheries, coral reefs, harmful algal blooms, invasive and endangered species, and biodiversity monitoring [32]. By detecting species across space and time, eDNA fulfils the basic needs of environmental surveys. The accuracy of eDNA is largely determined by the selected marker genes, the suitability of primers, amplification conditions and reference databases [33]. For free-living marine nematodes, COI genes have proven relatively difficult to amplify due to rampant gene rearrangements and frequent recombination in the mitochondrial genome [26]. Therefore, the 18S rRNA gene, which has a higher success rate, was selected as the marker gene to assess the diversity of coral reef nematodes in the South China Sea, focusing primarily on elucidating nematode biodiversity from different habitats. Notably, although molecular tools have been applied to the identification and description of nematode species in recent years, the rate of new species descriptions has not increased exponentially as initially expected. Indeed, the benefits of molecular taxonomy are often incomplete, lacking the final step of formal species description, which requires traditional morphological methods and the expertise of experienced nematologists. Therefore, eDNA metabarcoding sequencing technology needs to be combined with traditional morphological methods to build a comprehensive biomonitoring system.

5. Conclusions

In this study, the nematode biodiversity and community structure of coral reef sediments in the South China Sea were analysed by eDNA metabarcoding technology, which not only realised the non-invasive monitoring of coral reef nematode communities but also better assessed the health of coral reef ecosystems and identified and prevented possible ecological risks and ecological crises in a timely manner. In summary, the dominant groups of nematodes in coral reef sediments in the South China Sea are Enoplea and Chromadorea. Alpha diversity analysis shows that the northern region has higher nematode abundance and homogeneity than the southern area; Beta diversity analysis shows that there is no significant difference in the structure of the nematode communities in the northern region and the southern region and that the dominant groups are more or less the same. The analysis of variance showed that the significant taxa in the northern region were Diplogasteridae, Oxystominidae, Haliplectidae, Pristionchus, Pseudodiplogasteroides and Haliplectus, and in the southern region, they were Mermithida, Mermithidae and Hexamermis. Correlation analysis showed that environmental factors had little effect on the overall nematode community structure and only affected individual stations or single nematode species.