Spatial and Temporal Diversity of Fishes at Wuzhizhou Island in the South China Sea Based on Environmental DNA

Abstract

Wuzhizhou Island (WZZ) is one of the most mature tourism islands on the coast of the South China Sea, and its surrounding waters are rich in tropical coral reef fishes. Tourism could affect marine biodiversity, especially the coral reef fish community. In order to understand the species diversity of fish surrounding WZZ, environmental DNA (eDNA) samples were collected from six sampling sites around the island, located in the core and non-core tourism areas, respectively, from 2022 to 2023. In total, 188 fish species, belonging to 124 genera, 63 families and 17 orders, were detected using eDNA method. The Perciformes contained the highest number of species (n = 130, 69.15% of total number). Compared to traditional fishing nets, eDNA could provide more information on fish species. The results indicated that species richness and Margalef’s index were higher at the sampling sites in the core area of tourism of WZZ, which maybe relevant to tourism-based provisioning. By contrast, the Shannon–Wiener index, Pielou’s index and dominant species of fish did not display significant differences between core and non-core tourism areas. On the basis of the results of the β-diversity index, the differences in fish communities had a closer relationship to geographical location. Furthermore, there were obvious differences in fish communities in different seasons, which may be attributed to the influence of ocean currents.

1. Introduction

Wuzhizhou Island (WZZ) is a small offshore island, located at Haitang Bay, belonging to Hainan Island, China. The area of WZZ is 1.48 km², and it is 2.7 km from the coastline of Hainan Island [1]. The South China Sea is an important distribution area of coral reefs in the world [2]. In the past decades, the coral reefs along coast of Hainan Island have been seriously destroyed due to climate change [3] and anthropogenic activities such as overfishing, reef rock digging, intensive dredging, land clearing and environmental pollution [4,5,6,7,8]. The tourist industry began to be developed at WWZ in 1997 and it helped to restore coral reef ecosystems to some extent by curbing unregulated fishing and environmental destruction [9], and WZZ was reported to have a very high live coral coverage (>70%) along the Sanya coastal in year 2007ؘ–2008 [9]. However, the coral coverage surrounding WZZ reduced to 21.5% in 2019 mainly due to over-exploitation [10]. The restoration of the coral reef ecosystem at WZZ was launched in 2010, the Sanya WZZ National Marine Ranch Demonstration Zone was established in 2019 and numerous artificial reefs have been planted in the coastal area of WZZ in order to restore the coral reef fishes and their ecosystems [1,11].

At present, WZZ is one of the most mature eco-tourism islands along the coast of the South China Sea. From the historical situation, the coral reef system in this region has been protected to a certain extent due to the development of tourism, since diving as the main activity of tourism mainly involves observing coral reefs and fish species in the water. Therefore, this has led to a greater local focus on the protection of coral reef ecosystems. Studying the fish community could help us to understand the status of coral reef ecosystems surrounding WZZ, especially evaluating the influence of tourist activities on the fish community. However, there has still been no research on this aspect until now, and most previous studies only focus on one aspect of the fish community or tourism development surrounding WZZ.

In this study, we detected fish composition based on eDNA, calculated α- and β-diversity indices and analyzed spatial and temporal characteristics of fish species diversity surrounding WZZ. Our study could help to understand the diversity status of the fish community in the sea areas around WZZ, demonstrate the effects of tourism on coral reef fish communities and provide scientific data for the protection of coral reef ecosystems. Environmental DNA (eDNA) is recognized as a non-invasive, efficient and easily standardized research approach compared to traditional fish investigation methods such as fish nets and underwater observation, and it could supply the relative complete fish diversity in a given area [12,13]. And eDNA metabarcoding has been applied in the fish species diversity of marine environments, such as the seasonal changes in the fish community along the Danish coast, the community structures of fish in Maizuru Bay, Sea of Japan, and fish assemblages from the West Antarctic Peninsula, which were separately revealed using eDNA [14,15,16]. Considering it is difficult to clearly investigate fish species composition through traditional fishing tools as well as underwater observation, the eDNA technique was used in this study.

2. Materials and Methods

2.1. Study Sites and eDNA Sampling

In total, six sampling sites were set up along coastal areas surrounding WWZ (Figure 1), and samplings were conducted in June, 2022, and January and September, 2023, respectively. The study area chosen is located at the northern side of WZZ, which is the core area of tourism, and we set up three sampling sites in order to fully analyze the effects of tourist activities on fish communities. And the northeastern and southern sides of WZZ, where there is almost no tourism in the water, were also chosen, and three sampling sites were set up as a contrast. Among them, sites WZZ-4, WZZ-5 and WZZ-6 are located in the core area of tourism, and sites WZZ-1, WZZ-2 and WZZ-3 are located in the non-core tourism area. According to sampling protocols recommended [17], the major protocols were followed. At each site, 2 L × 3 replicates water samples were collected, and sterile masks and latex gloves were always worn during sampling, as well as changed between different samples to prevent cross-contamination. The eDNA collection was performed using the filtration method with 0.45 µm MCE membranes (Pall Whatman, Portsmouth, UK) when back indoors. Each filtration membrane was separately preserved in a 2 mL centrifuge tube and stored at −10 °C. The negative control was made through filtering 300 mL pure water. All the sampling devices and filtration tools were cleaned with 10% bleach containing sodium hypochlorite to prevent external DNA contamination.

2.2. Laboratory Work and Sequencing

According to laboratory protocols recommended [17], the E.Z.N.A. Water DNA Kit (Omega Bio-tek, Norcross, Georgia) was used for DNA extraction and one negative control was made at each batch. The MiFish 12s rRNA universal primer (F: 5′-GTCGGTAAAACTCGTGCCAGC; R: 5′-CATAGTGGGGTATCTAATCCCAGTTTG) was used for PCR. The PCR system had a total 25 µL volume, containing 4 µL of 5× FastPfu Buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL forward primer (5 μM), 0.8 µL reverse primer (5 μM), 0.4 µL FastPfu Polymerase, 10 ng of template DNA and 12 µL ddH₂O. The PCR reaction conditions were the following: pre-denaturation for 5 min at 95 °C, followed by 32 cycles of denaturation (30 s at 95 °C), annealing (30 s at 55 °C) and elongation (45 s at 72 °C) and final elongation for 10 min at 72 °C. And two negative controls (PCR blank) were set up in each PCR reaction, in order to monitor cross-contamination. The PCR products at each sampling site were verified through 2% agarose electrophoresis. Libraries were built and then high-throughput sequencing was conducted on an Illumina Miseq 150 PE platform by Biozeron, Shanghai, China.

2.3. Bioinformatics Analysis and Species Annotation

Quality control of aligned reads was performed using Qiime 2 [18] to ensure a Phred quality score >30. Filtered reads were clustered using Qiime 2 [18] into Molecular Operational Taxonomic Units (MOTUs) with a 99% similarity cutoff. Chimeras were identified and removed from the dataset using the Vsearch uchime-denovo command of Qiime 2 [18]. MOTUs with a number less than 10 were also removed. The most abundant sequences in each MOTU were chosen and assigned with the NCBI database https://www.ncbi.nlm.nih.gov/ (accessed on 25/01/2024), using NCBI-Blast to obtain the annotation taxon information of each MOTU. NCBI-Blast settings included an e-value ≤ 1 × 10⁻⁵, percent identity ≥ 97% and min_raw_gapped_score ≥ 100. The Supplementary Material 2 in a related reference [19] for MOTU information was used in this study.

There are three principles that we followed when we conducted species annotation: (a) if the query sequence matched one single locally occurring species in the database with ≥97% identity, this species was assigned; (b) if the query sequence matched more than one locally occurring species in the database with ≥97% identity, the lowest taxonomic level (i.e., genus or family) that included all of these species was assigned; and (c) if the query sequence matched one non-local species in the database with ≥97% identity and this species belonged to the same genus as the known local species, this genus was assigned. The sequences with an assigned taxon of ‘NA’ or assigned to human, bird, mammal or amphibian were removed.

2.4. Statistics Analysis

In eDNA metabarcoding studies, α-diversity (i.e., the number of species within a defined location) and β-diversity (i.e., the species turnover between locations) are often used to quantify the biodiversity of fish species [20]. The Shannon–Wiener diversity index (Shannon index), the Pielou’s evenness index (Pielou’s index) and Margalef’s richness index (Margalef’s index) are common indices to assess α-diversity [21,22,23]. The Shannon index is the most commonly used and able to present a mix between the diversity and heterogeneity of species in a certain region, Pielou’s index reflects the evenness of biodiversity and Margalef’s index reflects the number of species in a community or specific environment [21,22,24]. The abundance of each species at each sampling site was indicated by eDNA sequence counts, since previous studies have proved that eDNA can be used for quantitative analysis when studying the abundance of a fish community [25]. Ecologists routinely use dissimilarity measures between pairs of plots (species assemblages or communities) to explore community assembly processes, and this is the ecological meaning of β-diversity [26]. The Bray–Curtis (β_bray) dissimilarity coefficient is a common β-diversity index to reflect the proportion of the total species abundances in which the two plots differ [26].

When α-diversity was analyzed, we calculated species richness using the number of fish MOTUs based on previous studies [27,28,29]. The Shannon index was calculated as per the formula H = −∑(Pi)(lnPi), and Pi was calculated by the proportion of eDNA reads of a detected fish species in the total reads of each sample. Pielou’s index was calculated by the formula E = H/Hmax, and H refers to the Shannon index of each sample and Hmax was calculated by Hmax = LnS (S refers to total number of fish MOTUs detected). Margalef’s index was calculated by the formula D = (S − 1)/LnN, and S refers to total number of fish MOTUs detected. α-diversity was analyzed using the “vegan” package of RStudio v. 1.4. For β-diversity, the β_bray dissimilarity coefficient was adopted, and it was calculated using the “betapart” package of RStudio v. 1.4. All statistics analyses in this study were conducted using RStudio v. 1.4. The Principal Co-ordinates Analysis (PCoA) and Permutational Multivariate Analysis of Variance (PERMANOVAA) were analyzed by the “vegan” and “ggplot2” packages of RStudio v. 1.4. The eDNA reads of each species at each sampling sites are presented in Table S1.

3. Results

3.1. Fish Species Composition of WZZ

In total, 188 fish species were detected using eDNA, belonging to 124 genera, 63 families and 17 orders (for the species list, see Supplementary Material 1 in the related reference [19]). At the order level, the Perciformes contained the largest number of species (n = 130, 69.15% of total number), and it was absolutely dominant in species richness (Figure 2). The Clupeiformes (n = 14, 7.45%) and Anguilliformes (n = 11, 5.85%) both included more than 10 species, and other orders all had fewer than 10 species (Figure 2). At the family level, five families contained more than 10 species, which were Pomacentridae (n = 14, 7.45%), Blenniidae (n = 12, 6.38%), Carangidae (n = 11, 5.85%), Muraenidae (n = 10, 5.32%) and Labridae (n = 10, 5.32%), respectively (Figure 3). At the genus level, the Gymnothorax (n = 6, 3.19%) included the most species, four genera, Lethrinus, Abudefduf, Lutjanus and Siganus, all had 5 species (respectively, 2.66%) and more than 70% genera included only 1 species (Figure 4).

3.2. Spatial Fish Species Diversity of WZZ

The species richness of fish was compared among the six sampling sites (Figure 5), and the related results indicated that the species richness of sites WZZ-4, WZZ-5 and WZZ-6 was higher than that of sites WZZ-1, WZZ-2 and WZZ-3, and the species richness of fish was the highest at WZZ-4 and the lowest at WZZ-3. The dominant fish species of each sampling site were analyzed using relative eDNA abundance (Figure 6). According to eDNA results (Figure 6), the dominant fish species were Abudefduf sordidus, with a proportion of relative eDNA abundance of 20%–30% at four sampling sites (WZZ-1, WWZ-2, WZZ-3 and WZZ-6), and Siganus fuscescens, with a proportion of relative eDNA abundance exceeding 40% at two sampling sites (WZZ-4 and WZZ-5). In addition, other several fish species also occupied high relative proportions at each sampling site, for example, Helcogramma fuscipectoris, Herklotsichthys quadrimaculatus and Kyphosus vaigiensis occupied, respectively, ~10% at WZZ-1, Siganus fuscescens and Oedalechilus labiosus accounted for, respectively, ~10–20% at WZZ-2, Siganus fuscescens, Abudefduf vaigiensis and Oedalechilus labiosus occupied, respectively, ~10% at WZZ-3, Abudefduf sordidus accounted for >10% at WZZ-4, Chelon macrolepis occupied ~20% at WZZ-5, and Siganus fuscescens accounted for ~20% at WZZ-6.

The Shannon index, Pielou’s index and Margalef’s index were calculated to present the level of α-diversity of fish species (Table 1). Based on the results of the Shannon index at all sampling sites of WZZ, the range of the index was 2.350–2.899, and the average was 2.693 ± 0.23. According to the results of each sampling site, the index of WZZ-2 was the highest (2.899), that of WZZ-6 was the second highest (2.898) and the index of WZZ-5 was the lowest (2.350). According to results of Pielou’s index, the range of the index was 0.4702–0.5907, and the average was 0.545 ± 0.05. With regard to the results of each sampling site, the index of WZZ-3 was the highest (0.5907) and that of WZZ-5 was the lowest (0.4702). On the basis of the results of Margalef’s index, the range of the index was 10.77–12.84, and the average was 11.76 ± 0.75. Reviewing the results of each sampling site, the index of WZZ-4 was the highest (12.84) and that of WZZ-3 was the lowest (10.77).

The β_bray dissimilarity coefficients were calculated to reveal the discrepancy in fish communities between pairwise sampling sites (Table 2). According to the results of Table 2, the β_bray dissimilarity coefficient between WZZ-5 and WZZ-6 was the lowest (0.11498) and the fish communities of these two sites were the most similar. Additionally, the β_bray dissimilarity coefficient between WZZ-4 and WZZ-5 (0.12957), WZZ-1 and WZZ-2 (0.13971), as well as WZZ-2 and WZZ-3 (0.13534) were all lower than the values between the other sampling sites. By contrast, the β_bray dissimilarity coefficient between WZZ-3 and WZZ-6 was the highest (0.26119), and the fish communities between these two sites had the biggest difference. And the β_bray dissimilarity coefficients between WZZ-2 and WZZ-6 (0.24638) and between WZZ-1 and WZZ-6 (0.23358) were also higher than the value between the other two sampling sites.

3.3. Temporal Fish Species Diversity of WZZ

The β_bray dissimilarity coefficients between different seasons were calculated to display seasonal differences (Table 3). According to Table 3, the difference in fish community composition was bigger between summer and autumn compared to that between winter and summer as well as winter and autumn. The discrepancy in fish communities among different seasons was analyzed using PCoA (Figure 7). In accordance with the result of PCoA, the sampling sites at summer, winter and autumn were significantly separated at the x-coordinate. According to the result of PERMANOVAA, p = 0.001. And the above results indicated that fish communities among different seasons displayed a significant difference.

4. Discussion

4.1. Comparative Analysis of Fish Diversity by eDNA and Traditional Methods

In our study, we detected in total 17 orders, 63 families, 124 genera and 188 species of fish surrounding WZZ using eDNA. Nearly 70% of all species detected belonged to Perciformes, which was overwhelmingly dominant in this region, and this is similar to the dominant order in the fishing net investigation performed in WZZ [1]. According to investigation data of year 2005–2008 using underwater photography, the Pomacentridae, Labridae, Apogonidae and Chaetodontidae were the main families around WZZ [9]. Our research results also indicated that the number of Pomacentridae and Labridae was significantly higher than that of other families and these two families were dominant, which was the same as the results of previous studies [9]. Although the Apogonidae and Chaetodontidae were not dominant families, the number of species of both families was also in the top 20 (the first third) of all the families detected. Therefore, it was manifested that the fish composition at the levels of order and family were consistent between results from eDNA and traditional methods.

On the basis of a recent summer survey (July, 2020) on the fish around WZZ using angling, a total of 30 fish species were caught [30]. According to our eDNA results obtained in summer (June, 2022), a total of 53 fish species were detected in the same sampling area. Obviously, eDNA could provide more species information for us compared to fishing gear, especially angling. Certainly, there were differences in species composition between eDNA and traditional fishing nets, which may be seasonal changes in the marine fish community caused by the fact that the survey seasons of the two methods were not exactly the same.

4.2. Spatial Characteristics of Fish Species Diversity of WZZ

Through the comparative analysis results of species richness, the Shannon index, Pielou’s index and Margalef’s index of different sampling sites (Figure 5 and Table 1), the fish species diversity of WZZ appeared to have significant spatial differences. According to Figure 5, the species richness of sampling sites WZZ-4, WZZ-5 and WZZ-6 (range from 139 to 153) was obviously higher than that of sampling sites WZZ-1, WZZ-2 and WZZ-3 (range from 129 to 137). Among them, the species richness of WZZ-4 was the highest, and that of WZZ-3 was the lowest. Combined with the distribution of tourism activities at the coast of WZZ, we found that the quantity of fish species was higher in the core area of tourism, including WZZ-4, WZZ-5 and WZZ-6, especially in the diving areas like site WZZ-4. According to Table 1, Margalef’s index showed similar results to species richness, also displaying that the sampling sites WZZ-4, WZZ-5 and WZZ-6 (range from 11.45 to 12.84) were higher than sites WZZ-1, WZZ-2 and WZZ-3 (range from 10.77 to 11.85). It was interesting that many previous studies have come to similar conclusions, and a higher number of fish species was found in tourism areas, especially diving areas in the sea waters [31,32,33]. And these studies have proved that this phenomenon is related to tourism-based provisioning [30,31,32,33], which indicates that fish feeding, which happens during tourist activities, could directly influence the fish assemblages [27,28,29], and feeding of the fish community also exists in WZZ as far as we know.

However, the Shannon index and Pielou’s index displayed different trends. The Shannon index of sampling sites WZZ-2, WZZ-3 and WZZ-6 (from 2.871 to 2.899) was significantly higher than for sampling sites WZZ-1, WZZ-4 and WZZ-5 (from 2.350 to 2.589), and Pielou’s index of sampling sites WZZ-1, WZZ-2, WZZ-3 and WZZ-6 (from 0.5276 to 0.5907) was distinctly higher than that of sampling sites WZZ-4 and WZZ-5 (from 0.4702 to 0.5070). It could be seen that the fish species diversity of the core area of tourism was not higher than that of the non-core region, although the former had higher species richness. Furthermore, Abudefduf sordidus was the dominant species at sampling sites WZZ-1, WZZ-2, WZZ-3 and WZZ-6, and Siganus fuscescens was the dominant species at sampling sites WZZ-4 and WZZ-5, according to Figure 6. We also compared the water layer inhabited and feeding habits of the dominant species at each sampling site (Table 4), and we found that there was no clear distinction between core and non-core regions of tourism. Therefore, tourism did not bring significant negative effects on fish abundance and evenness indices, nor dominant species composition.

Based on the results of β_bray dissimilarity coefficients of fish communities between pairwise sampling sites (Table 2), it was proved that the closer the geographical location of the sampling sites, the more similar composition of the fish community, regardless of whether it was the core area of tourism.

4.3. Seasonal Characteristics of Fish Species Diversity of WZZ

According to our results on temporal differences in the fish species diversity of WZZ (Table 3 and Figure 7), the fish diversity of WZZ presented significant seasonal differences. WZZ is near to the region of the Qiongdong upwelling, which is one of the two major upwelling areas in the northern shelf area of the South China Sea, and it is affected by both coastal currents and the Qiongdong upwelling [30,34]. We hypothesized that the ocean current and seasonal migration of fish were the most important factors affecting the seasonal composition of the fish community. A similar conclusion had also been came to when seasonal changes in fishery resources in tropical marine pastures on WZZ were studied [11]. According to previous studies, the Qiongdong upwelling increased from April to September and was the strongest from June to July [30,35], and it was speculated that the fish composition around WWZ in summer and autumn may be affected by it. The northeast monsoon prevails in the South China Sea in winter [36], which formed a relatively stable fish community different from other seasons [34].

5. Conclusions

In summary, this study obtained 188 fish species belonging to 124 genera, 63 families and 17 orders around WZZ using eDNA, and Perciformes showed absolute dominance. The spatial and temporal α-diversity of fish species were analyzed, and the results indicated that species richness and Margalef’s index were higher at sampling sites in the core tourism area of WZZ, and we considered this had a close relationship with tourism-based provisioning, and previous studies also obtained similar results in the tropical marine. However, this difference between core and non-core regions of tourism was not reflected in the Shannon index, Pielou’s index and dominant fish species. In addition, spatial differences in fish communities were related to geographical location rather than tourist activities. Furthermore, the fish diversity and communities of WZZ also presented a significant seasonal discrepancy, which may be affected by the ocean current and seasonal migration of fish. Considering that WZZ, as well as being a popular tropical tourism island, is also an important distribution area of coral reef ecosystems in the South China Sea, it is suggested that long-term monitoring of fish communities in this region and the effects of tourism on fish communities should be carried out, and eDNA is suitable as a method of long-term monitoring.

Author Contributions

T.J. wrote the original draft and performed data analyses. Y.X. conceived and guided this study. K.C., X.W. and C.W. conducted field work. R.X. and R.Y. performed the experiments and eDNA sequence analyses. Z.M. and W.Y. reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in this published paper.

Acknowledgments

We appreciated help from the Hainan Wuzhizhou Tourism Development Co., Ltd. during water sampling.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. (a) Locality of sampling sites around WZZ; (b) habitat photographs of each sampling site.

Figure 2. The order composition of fish at WZZ.

Figure 3. The family composition of fish at WZZ.

Figure 4. The genus composition of fish at WZZ.

Figure 5. Comparison of fish species richness at each sampling site of WZZ.

Figure 6. Analysis of relative eDNA abundance of fish species at each sampling site of WZZ.

Figure 7. PCoA of fish communities among different seasons (A: summer; B: winter; C: autumn).

Table 1

The α-diversity indices of each sampling site at WZZ.

	Shannon Index	Pielou’s Index	Margalef’s Index
WZZ-1	2.589	0.5276	11.30
WZZ-2	2.899	0.5890	11.85
WZZ-3	2.871	0.5907	10.77
WZZ-4	2.551	0.5070	12.84
WZZ-5	2.350	0.4702	12.36
WZZ-6	2.898	0.5872	11.45

Table 2

β_bray dissimilarity coefficient between pairwise sampling sites at WZZ.

	WZZ-1	WZZ-2	WZZ-3	WZZ-4	WZZ-5	WZZ-6
WZZ-1	0
WZZ-2	0.13971	0
WZZ-3	0.16667	0.13534	0
WZZ-4	0.18056	0.17241	0.14894	0
WZZ-5	0.22968	0.22105	0.22744	0.12957	0
WZZ-6	0.23358	0.24638	0.26119	0.15753	0.11498	0

Table 3

β_bray dissimilarity coefficients between different seasons at WZZ.

	Summer	Winter	Autumn
Summer	0
Winter	0.13971	0
Autumn	0.16667	0.13534	0

Table 4

Comparison of water layer inhabited and feeding habits of the dominant species at each sampling site.

Dominant Fish Species	Water layer Inhabited *	Feeding Habits *	Sampling Sites
Abudefduf sordidus	pelagic (0–3 m)	omnivorous	WZZ-1, WWZ-2, WZZ-3, WZZ-6
Siganus fuscescens	benthopelagic (1–50 m)	herbivorous	WZZ-4, WZZ-5
Helcogramma fuscipectoris	pelagic (0–8 m)	carnivorous	WZZ-1
Herklotsichthys quadrimaculatus	pelagic (0–13 m)	carnivorous	WZZ-1
Kyphosus vaigiensis	benthopelagic (0–40 m)	carnivorous	WZZ-1
Oedalechilus labiosus	pelagic (0–3 m)	omnivorous	WWZ-2
Abudefduf vaigiensis	pelagic (0–3 m)	omnivorous	WZZ-3
Chelon macrolepis	benthopelagic (10–? m)	omnivorous	WZZ-5

* Data referenced from Froese and Pauly, www.fishbase.org (accessed on 01 June 2024).

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/d16120712/s1: Table S1: Fish species list around WZZ detected using eDNA and abundance of eDNA reads at each sampling site.

References

1. Luo, H.; Wang, J.; Xie, Z.; Li, J.; Zhang, X. Fish biodiversity and community structure characteristics of Wuzhizhou Island, Sanya. J. Mar. Sci.; 2023; 47, pp. 74-86.

2. Huang, H.; Li, X. Coral biology and reef ecology in the South China Sea. Chin. Sci. Bull.; 2013; 58, 1573. [DOI: https://dx.doi.org/10.1360/csb2013-58-17-1573]

3. Hughes, T.P.; Huang, H.; Young, M.A. The wicked problem of China’s disappearing coral reefs. Conserv. Biol.; 2013; 27, pp. 261-269. [DOI: https://dx.doi.org/10.1111/j.1523-1739.2012.01957.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23140101]

4. Zhang, Q.; Shi, Q.; Chen, G.; Fong, T.; Wong, D.; Huang, H.; Wang, H.; Zhao, M. Status monitoring and health assessment of Luhuitou fringing reef of Sanya, Hainan, China. Chin. Sci. Bull.; 2006; 51, pp. 81-88. [DOI: https://dx.doi.org/10.1007/s11434-006-9081-3]

5. Zhao, M.; Yu, K.; Zhang, Q.; Shi, Q.; Price, G.J. Long-term decline of a fringing coral reef in the northern South China Sea. J. Coast. Res.; 2012; 28, pp. 1088-1099.

6. Li, X.; Huang, H.; Lian, J.; Liu, S.; Huang, L.; Yang, J. Spatial and temporal variations in sediment accumulation and their impacts on coral communities in the Sanya Coral Reef Reserve, Hainan, China. Deep. Sea Res. Part II Top. Stud. Oceanogr.; 2013; 96, pp. 88-96. [DOI: https://dx.doi.org/10.1016/j.dsr2.2013.04.015]

7. Li, X.; Huang, H.; Lian, J.; Yang, J.; Ye, C.; Chen, Y.; Huang, L. Coral community changes in response to a high sedimentation event: A case study in southern Hainan Island. Chin. Sci. Bull.; 2013; 58, pp. 1028-1037. [DOI: https://dx.doi.org/10.1007/s11434-012-5601-5]

8. Roder, C.; Wu, Z.; Richter, C.; Zhang, J. Coral reef degradation and metabolic performance of the scleractinian coral Porites lutea under anthropogenic impact along the NE coast of Hainan Island, South China Sea. Cont. Shelf Res.; 2013; 57, pp. 123-131. [DOI: https://dx.doi.org/10.1016/j.csr.2012.11.017]

9. Wu, Z.; Chen, C.; Ding, X. The effect of reef sightseeing tourism activities on corals in Wuzhizhou Island (Sanya). Ocean Dev. Manag.; 2011; 28, pp. 57-60.

10. Zheng, X.; Li, Y.; Liang, J.; Lin, R.; Wang, D. Performance of ecological restoration in an impaired coral reef in the Wuzhizhou Island, Sanya, China. Chin. J. Oceanol. Limnol.; 2021; 39, pp. 135-147. [DOI: https://dx.doi.org/10.1007/s00343-020-9253-z]

11. Wang, X.; Li, J.; Zhang, X.; Chen, M.; Wu, P.; Ma, M.; Peng, S.; Zhang, J.; Xie, J.; Gao, F. et al. Current situation and seasonal variation of fishery resources in tropical marine ranch in Wuzhizhou Island, Sanya, Hainan. Oceanol. Limnol. Sin.; 2021; 52, pp. 1557-1566.

12. Thomsen, P.F.; Willerslev, E. Environmental DNA–An emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv.; 2015; 183, pp. 4-18.

13. Barnes, M.A.; Turner, C.R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet.; 2016; 17, pp. 1-17.

14. Sigsgaard, E.E.; Nielsen, I.B.; Carl, H.; Krag, M.A.; Knudsen, S.W.; Xing, Y.; Holm-Hansen, T.H.; Møller, P.R.; Thomsen, P.F. Seawater environmental DNA reflects seasonality of a coastal fish community. Mar. Bio.; 2017; 164, 128. [DOI: https://dx.doi.org/10.1007/s00227-017-3147-4]

15. Cowart, D.A.; Murphy, K.R.; Cheng, C.-H.C. Metagenomic sequencing of environmental DNA reveals marine faunal assemblages from the West Antarctic Peninsula. Mar. Geonomics; 2018; 37, pp. 148-160.

16. Yamamoto, S.; Masuda, R.; Sato, Y.; Sado, T.; Araki, H.; Kondoh, M.; Minamoto, T.; Miya, M. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep.; 2017; 7, 40368. [DOI: https://dx.doi.org/10.1038/srep40368]

17. Xing, Y.; Gao, W.; Shen, Z.; Zhang, Y.; Bai, J.; Cai, X.; Ouyang, J.; Zhao, Y. A review of environmental DNA field and laboratory protocols applied in fish ecology and environmental health. Front. Environ. Sci.; 2022; 10, 725360.

18. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods; 2010; 7, pp. 335-336. [DOI: https://dx.doi.org/10.1038/nmeth.f.303]

19. Wang, C.; Ma, Z.; Cao, K.; Wang, X.; Xi, R.; Jiang, T.; Yang, R.; Xing, Y. Species diversity of fish at the Wuzhizhou Island, South China Sea based on environmental DNA. Biodivers. Data J.; 2024; 12, e127120. [DOI: https://dx.doi.org/10.3897/BDJ.12.e127120]

20. Li, Y.; Evans, N.; Renshaw, M.; Jerde, C.; Olds, B.; Shogren, A.; Deiner, K.; Lodge, D.; Lamberti, G.; Pfrender, M. Estimating fish Alpha-and Beta-diversity along a small stream with environmental DNA metabarcoding. Metabarcoding Metagenom.; 2018; 2, e24262. [DOI: https://dx.doi.org/10.3897/mbmg.2.24262]

21. Margalef, R. Fitoplancton de las costas de Puerto Rico. Investig. Pesq.; 1957; 6, pp. 39-52.

22. Sheldon, A.L. Equitability indices: Dependence on the species count. Ecology; 1969; 50, pp. 466-467. [DOI: https://dx.doi.org/10.2307/1933900]

23. Shannon, C.E. A mathematical theory of communication. Bell Syst. Technol. J.; 1948; 27, pp. 379-423. [DOI: https://dx.doi.org/10.1002/j.1538-7305.1948.tb01338.x]

24. Magurran, A.E. Measuring biological diversity. Curr. Biol.; 2021; 31, pp. R1174-R1177. [DOI: https://dx.doi.org/10.1016/j.cub.2021.07.049]

25. Hänfling, B.; Lawson Handley, L.; Read, D.S.; Hahn, C.; Li, J.; Nichols, P.; Blackman, R.C.; Oliver, A.; Winfield, I.J. Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol. Ecol.; 2016; 25, pp. 3101-3119. [DOI: https://dx.doi.org/10.1111/mec.13660]

26. Ricotta, C.; Podani, J. On some properties of the Bray-Curtis dissimilarity and their ecological meaning. Ecol. Complex.; 2017; 31, pp. 201-205. [DOI: https://dx.doi.org/10.1016/j.ecocom.2017.07.003]

27. Evans, N.T.; Olds, B.P.; Renshaw, M.A.; Turner, C.R.; Li, Y.; Jerde, C.L.; Mahon, A.R.; Pfrender, M.E.; Lamberti, G.A.; Lodge, D.M. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour.; 2016; 16, pp. 29-41. [DOI: https://dx.doi.org/10.1111/1755-0998.12433]

28. Lacoursière-Roussel, A.; Rosabal, M.; Bernatchez, L. Estimating fish abundance and biomass from eDNA concentrations: Variability among capture methods and environmental conditions. Mol. Ecol. Resour.; 2016; 16, pp. 1401-1414. [DOI: https://dx.doi.org/10.1111/1755-0998.12522]

29. Doi, H.; Uchii, K.; Matsuhashi, S.; Takahara, T.; Yamanaka, H.; Minamoto, T. Isopropanol precipitation method for collecting fish environmental DNA. Limnol. Oceanogr. Methods; 2017; 15, pp. 212-218. [DOI: https://dx.doi.org/10.1002/lom3.10161]

30. Yin, H.; Zhu, W.; Ma, W.; Zhang, X.; Xia, J.; Xu, Q.; Li, J.; He, F.; Li, X. The summer food web in the marine ranch area of Wuzhizhou Island in Sanya, Hainan. Acta Ecol. Sin.; 2022; 42, pp. 3241-3253.

31. Milazzo, M.; Badalamenti, F.; Vega Fernández, T.; Chemello, R. Effects of fish feeding by snorkellers on the density and size distribution of fishes in a Mediterranean marine protected area. Mar. Biol.; 2005; 146, pp. 1213-1222. [DOI: https://dx.doi.org/10.1007/s00227-004-1527-z]

32. Ilarri, M.D.I.; Souza, A.T.d.; Medeiros, P.R.d.; Grempel, R.G.; Rosa, I.M.d.L. Effects of tourist visitation and supplementary feeding on fish assemblage composition on a tropical reef in the Southwestern Atlantic. Neotrop. Ichthyol.; 2008; 6, pp. 651-656. [DOI: https://dx.doi.org/10.1590/S1679-62252008000400014]

33. Chung, M.G.; Dietz, T.; Liu, J. Global relationships between biodiversity and nature-based tourism in protected areas. Ecosyst. Serv.; 2018; 34, pp. 11-23. [DOI: https://dx.doi.org/10.1016/j.ecoser.2018.09.004]

34. Wang, X.; Lin, Z.; Du, F.; Qiu, Y.; Sun, D.; Wang, Y.; Liang, X.; Huang, S. Fish species composition and community pattern in the continental shelf of northwestern South China Sea. Acta Ecol. Sin.; 2013; 33, pp. 2225-2235. [DOI: https://dx.doi.org/10.5846/stxb201112261975]

35. Xie, L.; Zhang, S.; Zhao, H. Overview of studies on Qiongdong upwelling. J. Trop. Oceanogr.; 2012; 31, pp. 35-41.

36. Yang, H.; Liu, Q. A summary on ocean circulation study of the South China Sea. Adv. Earth Sci.; 1998; 13, 364.