Assessment of fish diversity in the coastal waters off Nodaedo Island, Tongyeong, Korea using underwater visual census and environmental DNA metabarcoding

DOI: https://doi.org/10.21203/rs.3.rs-2032631/v1

Abstract

Environmental DNA (eDNA) analysis is a promising method to monitor marine biodiversity. Examining the capacity of eDNA to provide accurate biodiversity measures in species-rich ecosystems such as coastal waters is a prerequisite for their application in long-term monitoring. Therefore, both underwater visual census (UVC) and eDNA metabarcoding were conducted monthly from June 2018 to May 2019 to investigate fish diversity on the coast of Nodaedo Island in Tongyeong, South Korea. A total of 16,036 individuals were observed by UVC and classified into 69 species, with perciform fish being the most dominant (31 species). eDNA metabarcoding detected 68 operational taxonomy units (OTUs) and 738,217 reads, accounting for a total of 18 orders, 42 families, and 68 species. The Gobiidae family was the most dominant taxon, encompassing five species. The UVC method could detect species that were otherwise not identified through eDNA due to the small number of individuals or analytical limitations. eDNA metabarcoding was suitable for detecting smaller species, pelagic species, and those that inhabit microhabitats. Our findings demonstrated that these two methods can complement each other to gain insights into the structure of the communities of fish that inhabit various coastal environments. eDNA metabarcoding can be applied as a fast and efficient method to conduct surveys on fish species diversity in coastal waters. However, this approach must be further compared with traditional survey methods to validate its applicability as an effective complement to traditional measures.

Introduction

There are more than 30,000 marine fish species worldwide, making teleosts the largest and most diverse group of vertebrates. Therefore, fish diversity is also often used as an indicator to monitor marine ecosystems (Thomsen et al. 2012; Froese and Pauly 2022). The study of the diversity of marine fish is essential for monitoring climate change, as well as for the protection of fishery resources and environmental conservation (Roessig et al. 2004; Mora et al. 2008). Coastal areas contain a large amount of rocks and seaweeds and are rich in organic matter, making these sites an ideal breeding ground for larval and juvenile fish, as well as a hideout for various organisms including mollusks and crustaceans (Parrish 1989; Mateo and Tobías 2001). The southeastern waters of South Korea, where Tongyeong is located, are influenced by the Tsushima warm current, which is a tributary of the Kuroshio Current with high temperatures and high salinity. This region exhibits high fish productivity due to the development of continental shelves and reclaimed coastlines (KORDI 1998; Guo et al. 2006). Nodaedo is an island off the coast of Tongyeong, which is located in an area with higher water temperature and salinity than those observed in the coastal area. As expected, this island also exhibits high fish diversity (Choo and Kim 1998; Kim et al. 2016). Most of the methods that are currently used to survey fish diversity (e.g., tow nets and beam trawls) are time-consuming, destructive, and can only be used in some specific areas. Additionally, these approaches are not well suited to evaluate fish diversity in shallow coasts. The underwater visual census (UVC) is a well-known method to study fish diversity in shallow coastal waters. Unlike other conventional approaches that rely on fishing gear, this method is not only fast, non-disruptive, and highly effective, but can also be employed in various environments (Thresher and Gunn 1986; St. John et al. 1990; Lowry et al. 2012). However, due to variations in diving technology and fish observability caused by water clarity, it may become difficult to observe fish certain species, such as those inhabiting small habitats or the surface (Watson et al. 1995; Thompson and Mapstone 1997; Kulbicki 1998). eDNA is the DNA in the environment that derives form hair, cells, feces, and tissues separated from an organism. eDNA is commonly detected in a variety of environments including soil, air, and water (Taberlet et al. 2012; Mächler et al. 2016). Ficetola et al. (2008) were the first to apply eDNA technology to aquatic environments for the detection of the bullfrog (Rana catesbeiana) in a pond. Since then, various studies have relied on eDNA to survey aquatic organisms (Shaw et al. 2016; Grey et al. 2018; Kelly et al. 2018). Among the eDNA analysis methods, eDNA metabarcoding is a rapid method that allows for the quick analysis of large amounts of eDNA sequences to identify species living in a specific location. This molecular biology approach can provide highly accurate biological classifications (Yu et al. 2012; Wood et al. 2013). Recently, fish species diversity studies using both eDNA analysis and underwater survey methods such as UVC, snorkeling, and baited underwater video census have become common and there appears to be a complementary relationship between the two methods (Ulibarri et al. 2017; Stat et al. 2019; Valdivia-Carrillo et al. 2019). For example, eDNA metabarcoding is suitable for detecting migratory and rare fish species that are difficult to observe with UVC methods, whereas UVC is suitable for observing species that cannot be detected by eDNA analyses because of the small number of individuals. Therefore, more fish can be detected when these two methods are combined than if they were used alone. To the best of our knowledge, no previous studies have characterized the diversity of fish species in Korean coastal waters using both UVC and eDNA metabarcoding. Therefore, our study sought to apply these two methods to explore the fish diversity on the Nodaedo Island of Tongyeong, South Korea.

Materials And Methods

1) Study areas and sample collection

The survey was conducted in the waters off the Nodaedo Island, approximately 25 km from Tongyeong, South Korea (Fig. 1). The Nodaedo Island features an open bay with a 0–10 m depth (average depth of 5 m). At a 0–4 m depth, the coastline is mainly composed of rocks and seaweed; below 6–7 m, it is composed of sand and mud. The survey, including UVC and eDNA water sample collection, was conducted once a month from June 2018 to May 2019. Additionally, surveys were conducted two days after neap tide to ensure visibility during UVC. 

2) Underwater visual censuses

UVC was conducted using the line-transect method to observe species within a certain range while SCUBA diving in groups of two. The line transect was set to an observation area comprising a length of approximately 200 m and an area of 1,200 m2. Underwater photos and videos were taken using DSC-RX100 (Sony, Japan) and a GoPro Hero 4 Black camera (GoPro, USA) for approximately 40 min during one UVC. The identification of species was performed in accordance with Kim et al. (2005). The images and photos were compared at the species level in the laboratory. FishBase (Froese and Pauly 2022) was used to determine the classification system and scientific names. The number of fishes on the photos and videos were counted using the Image J program (NIH, USA). Water temperature was measured by ZOOP Novo (Suunto, Finland). Salinity data (psu) were obtained from the KHOA (Korean Hydrographic and Oceanographic Agency). 

3) eDNA sample collection, filtration, and preservation

Seawater samples (1 L) for eDNA analyses were collected five times (200 mL per sample) from approximately 1.5 m above the sea bottom using a stand up plastic pouch at 50-m intervals along 200-m transect lines. The collected water was placed in a container treated with 10% commercial bleach and passed through Sterivex-HV filters (0.45 µm) using a 250-mL syringe. After filtering the seawater, the Sterivex-HV filters were dried using a 250-mL syringe. The samples were then capped and stored in a cooler with dry ice and transported to the laboratory within an hour prior to DNA extraction. Additionally, we processed three field blanks comprising 1 L of distilled water as a negative control following the above-described method. To minimize contamination, latex gloves were worn during filtration and all tools were disinfected with chlorine.  

4) DNA extraction and metabarcoding

The DNeasy Blood and Tissue Kit (Qiagen, Germany) was used for DNA extraction as described by Miya et al. (2016). To determine contamination during DNA extraction, 1 L of double distilled water (DDW) was passed through a Sterivex-HV filter as a negative control. To construct an amplicon library for 12S rRNA, a two-step tailed PCR method was used and PCR amplification was performed in duplicate for each DNA extract using the MiFish-F (5’-GTC GGT AAA ACT CGT GCC AGC-3’) and MiFish-R (5’-CAT AGT GGG GTA TCT AAT CCC AGT TTG-3’) primers (Miya et al. 2015). These primers target the 12S rRNA gene of the mitochondrial genome and were used to identify the fish species. An amplicon library was produced using the method proposed by Miya et al. (2015). Here, 10 μL of reaction solution was used for the first PCR amplification as follows: 10 × Ex buffer, 1.0 μL; dNTPs (2.5 mM of each), 0.8 μL; 10 μM forward primer, 0.5 μL; 10 μM reverse primer, 0.5 μL; template DNA (max 2 ng/μL), 2.0 μL; ExTaq HS [TaKaRa] (5 U/μL), 0.1 μL; and DDW, 5.1 μL. The PCR conditions were as follows: 95 ℃, 3 min; 98 ℃, 20 s; 35 cycles; 65 ℃, 15 s; 72 ℃, 20 s; and 72 ℃, 5 min. The total reaction volume of the second PCR cycle was also 10 μL: 10 × Ex buffer, 1.0 μL; dNTPs (2.5 mM of each), 0.8 μL; 10 μM forward primer, 0.5 μL; 10 μM reverse primer, 0.5 μL; PCR product (max 2 ng/μL), 2.0 μL; ExTaq HS [TaKaRa] (5 U/μL), 0.1 μL; and DDW, 5.1 μL. The PCR conditions were the following: 94 ℃, 2 min; 94 ℃, 30 s; 12 cycles; 60 ℃, 30 s; 72 ℃, 30 s; and 72 ℃, 5 min. The PCR products were purified at each step using Agencourt AMPure XP purification beads (Beckman Coulter, Brea, CA, USA). Both negative and extraction controls did not show any evidence of amplification.

To quantitatively measure the amplicon library, the concentration was determined using the Synergy H1 and QuantiFlour dsDNA system (Promega, Madison, WI, USA). The library quality was assessed by using a fragment analyzer (Advanced Analytical Technologies, Ankeny, IA, USA) with a dsDNA 915 Reagent Kit (Agilent, Santa Clara, CA, USA) following the manufacturer’s instructions. Paired-end sequencing (2 × 300 bp) was conducted on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) with the MiSeq Reagent Kit v3. 

5) Data analysis for eDNA and UVCs

Reads beginning with a sequence that completely matched the primer used were extracted using the fastx_barcode_splitter tool within the FASTX-Toolkit (v.0.0.14), after which the primer sequence was trimmed. The primer array and 70 bases in the second half of the read were removed from the extracted array. Afterward, sequences with quality scores below 20 were removed using the Sickle tools (Joshi and Fass 2011) and ≥40 bp sequences were discarded. The reads were merged using 180 bases with the FLASH (v.1.2.11) paired-end merge script (Magoč and Salzberg 2011). The sequences were then clustered into operational taxonomic units (OTUs) using USEARCH (Version 7.0.1090) (Rognes et al. 2016) with a 97% similarity threshold. Chimeras were filtered out using UCHIME (v4.2.40) (Rognes et al. 2016). Next, a systematic estimation was performed by comparing MitoFish (Mitochondrial Genome Database of Fish) and MiFish references using the BLAST algorithm. The read and eDNA metabarcoding data were converted into presence/absence data to enable their comparison with the UVC data. Bray–Curtis similarity analyses were conducted to assess the similarities between fish communities depending on the survey method. ANOSIM (one-way analysis of similarity) was used to analyze the differences in fish communities depending on the survey method. Ordination by Non-metric multidimensional scaling (NMDS) based on Bray-Curtis similarity was used to visualize dissimilarity in community structure among methods, and SIMPER (similarity percentages) analysis was conducted to find the species responsible for these differences. All of the above analyses were performed using the PRIMER v6 statistical package (Plymouth Routines in Multivariate Ecological Research, v6.1.6; PRIMER-E Ltd., Lutton, Ivybridge, UK) and the “vegan” package (Oksanen 2013) in R version 4.1.2 (R Development Core Team 2013), and we made graphs with ggplot2 in R.

Results

1) Temperature and Salinity

The highest water temperature of 25 °C was recorded in August 2018 and the lowest of 11 °C was observed in January 2019. The average salinity was 33 psu (Fig. 2). 

2) Fish species observed by UVCs 

A total of 16,036 fish were observed through UVC and were classified into 14 orders, 33 families, and 69 species (Table 1). Gobiidae was the largest family with 9 species, followed by Labridae with 8 species, Sebastidae with 5 species, Cottidae with 4 species, and Pomacentridae and Monacanthidae, each with 3 species. All other families contained only 1–2 species. Chromis notata was the dominant species, with 7,870 individuals, followed by Sebastes inermis with 4,569 individuals and Parajulis poecilepterus with 788 individuals (Table 1, 7). Of the 12 surveys, 18 species appeared more than six times, including S. inermis and C. notata 12 times, Pseudolabrus sieboldi, Hexagrammos agrammus, Paracentropogon rubripinnis, Semicossyphus reticulatus, and Thamnaconus modestus 10 times, Pterogobius zonoleucus 9 times, Halichoeres tenuispinis, Istigobius campbelli, H. otakii, and Rudarius ercodes 8 times, P. poecilepterus, Sagamia geneionema, Enneapterygius etheostoma, Pseudoblennius cottoides, and Neoditrema ransonnetii 7 times, and Petroscirtes breviceps 6 times.

The highest number of monthly appearances by species and individuals were recorded in September 2018 (39 species) and May 2019 (2,407 individuals), respectively. In contrast, the lowest number of species and individuals were recorded in January 2019, with 6 species and 56 individuals, respectively.

The following subtropical or tropical fish species were observed in this study: Springerichthys bapturus, Abudefduf vaigiensis, Pomacentrus coelestis, Chaetodontoplus septentrionalis, Parupeneus indicus, Ostracion immaculatum, Vellitor centropomus, Stethojulis interrupta, and Thalassoma lunare. An unrecorded species, Parupeneus biaculeatus, also appeared. 

3) Fish species detected by eDNA metabarcoding 

A total of 738,217 reads were analyzed through eDNA metabarcoding, resulting in 18 orders, 42 families, and 68 species (Table 2). Among them, five species each were identified as members of the Gobiidae, Clupeidae, Labridae, and Stichaeidae families; four species each were classified as members of the Scombridae, Carangidae, Monacanthidae, and Scorpaenidae families; and three of were members of the Engraulidae family. In addition, 1 or 2 species belonging to the remaining 31 families were identified. Some sequences had very high similarities, including Sebastes spp., Hexagrammos spp., Ditrema spp., Takifugu spp., Scomber spp., Platycephalus spp., Gadus spp., Thunnus spp., and Pseudopleuronectes spp. Therefore, they were not classified at the species level through OTU analysis (Table 2, 7).

Among the fish classified at the species level, Engraulis japonicus was identified nine times; Pagrus major and Trachurus japonicus eight times, Ammodytes personatus, Clupea pallasii, and Neoditrema ransonnetii seven times; and P. poecilepterus, Lateolabrax japonicus, P. rubripinnis, Stephanolepis cirrhifer six times. The highest number of monthly appearances (26 species) occurred in January 2019, when the water temperature was the lowest, followed by 25 species in November 2018. In contrast, only five species were detected in April 2019 and nine in February. These results contradict the results obtained through UVC. 

4) Comparison with previous research

In this study, 69 species of fish were identified, whereas 59 species were reported in two previous studies (Gwak et al. 2016; Lee et al. 2018) conducted using the same UVCs, and these numbers were large at both the species and individual levels (Fig. 3). eDNA metabarcoding analysis helped identify 68 species, which indicates that a higher species diversity was observed in our study than in previous studies based on UVC (Table 3). In this study, fish species that had not been previously collected or observed off the coast of the South Sea, Korea, were also observed (Table 4). For example, S. bapturus, a fish species mainly observed in Ulleungdo and Dokdo Island in the East Sea, South Korea, was first discovered in Tongyeong attached to the bottom of the bedrock. Parupeneus indicus was also observed and classified as an endangered species (the IUCN red list) (Smith-Vaniz and Williams 2016). These are subtropical and tropical fish species and generally appear between September and November. 

5) Results from UVC and eDNA metabarcoding

ANOSIM analysis identified a significant difference between the eDNA metabarcoding and UVC (ANOSIM, global R=0.695, P<0.001) (Table 5). Species contribution (%) from SIMPER between eDNA metabarcoding and UVC indicated that the average dissimilarity was 75.94% (Table 6). Among them, Scomber spp. and E. japonicus were not observed by UVC. Moreover, C. notata, P. zonoleucus, P. sieboldi were detected over the 9 times month in UVC but only once or twice detected in eDNA metabarcoding, which demonstrated differences between the survey methods. Additionally, as a result of nMDS analysis from two methods (UVC, eDNA metabarcoding) using monthly presence/absence data were divided into two groups (Fig. 4).

A total of 22 orders, 54 families, and 105 species were observed through UVC and eDNA metabarcoding. A total of 32 species were observed when both methods were used, 37 species were observed only with UVC, and 36 species were detected only with eDNA metabarcoding (Fig. 3, Table 5). Very few individuals were identified exclusively through UVC. These were generally slow swimmers and most of them settled on the rock or sand. However, there were some notable exceptions such as Seriola dumerili, which was observed despite being an agile swimmer, and Ostorhinchus semilineatus, for which a large number of individuals were observed exclusively through UVC. The fish detected only through eDNA metabarcoding were generally active swimmers with a strong migratory tendency. These fish, which included members of the Clupeidae and Blenniidae families, also tend to hide in microhabitats that are difficult to observe by UVC. Species that were commonly observed through both methods generally had a strong settlement and showed less evacuation when the divers appeared. However, active swimming fish species such as A. personatus and T. japonicus were exceptions. Moreover, both of them tended to appear together. The largest difference between the monthly number of species observed through the two methods was observed in September and November 2018 (Fig. 3).

Discussion

1) UVCs 

A total of 69 species were observed by UVC on the coastal waters off Nodaedo Island. In another study, the composition of fish species in the bay of Tongyeong was also investigated using a method similar to the one described in the present study. An average of 44 species were identified annually, thus highlighting the substantial difference in the number of species identified by the aforementioned study and ours (Gwak et al. 2016; Lee et al. 2018). Additionally, the number of species that appeared in common with this study varied depending on the geographical characteristics of the survey area. In other words, there was a significant difference in the appearance of fish in the closed and open seas of Tongyeong. Moreover, the coast of the Nodaedo Island, located in the outer sea, had higher diversity of fish species than that observed on the bay of Tongyeong.

Among the fish species observed on the coast of the Nodaedo Island, Chromis notata was the dominant species, appearing 12 times with 7,870 individuals. Huh et al. (2013) interpreted the appearance of C. notata in the central and southeastern part of the East Sea of Korea as a result of the influx of large currents and climate change. Shin et al. (2014) reported genetic similarities between the C. notata populations of Jeju Island and the East Sea. Therefore, these observations support the hypothesis that a C. notata population that inhabited the coast of Jeju Island may have moved northwardly due to the influence of water currents. However, C. notata specimens were observed throughout the entire year. Therefore, these findings suggest that C. notata has already adapted to seasonal changes and settled on the coast of the South Sea. Lee et al. (2018) investigated the fish diversity in the bay of Tongyeong and reported different results from those of our study. The authors reported that approximately 50 individuals appeared five times a year. Although the bays of Tongyeong (Lee et al. 2018) and Nodaedo Islands are 25 km apart, they have similar water temperature and salinity. Therefore, the difference in the number of fish and their appearances is likely influenced by geographic characteristics rather than physical factors. In other words, compared to bay of Tongyeong, C. notata adapted more readily to Nodaedo Island, which is directly affected by the warm Tsushima current. These observations suggest that C. notata requires more time to settle in the closed bay of Tongyeong. Sebastes inermis, the subdominant species, is known to adapt easily to changes in water temperature and this species has already settled in the coast (Oh and Noh 2006). Many studies conducted on the south coast of South Korea have reported the occurrence of a large number of individuals belonging to these species. UVC surveys demonstrated that both S. inermis and C. notata tended to occur in groups, which is thought to be because the former is carnivorous and the latter is planktivorous, and therefore these species do not compete with each other for food (Ochi 1986; Huh and Kwak 1998).

The seasonal appearances of certain fish species are at least partially related to water temperature. However, we cannot conclusively state that species richness increases with water temperature because our findings, as well as those of previous studies (Gwak et al. 2016; Han et al. 2017; Lee et al. 2018), demonstrated the occurrence of many species between September and November, after the highest water temperature was recorded in August. This was likely because the current flows from the Jeju Island to the northeast during the summer and autumn seasons and transports fish with it (Kim and Bae 2011).

In this study, the number of species observed in August was smaller than that in July and September due to the lack of visibility during the diving survey. The UVC method is a nondestructive method that is commonly used in rocky areas. However, underwater visibility can severely affect the accuracy of this approach, which in turn depends on the weather and/or environmental conditions.

A significant difference was observed in the number of species between our study and previous studies involving UVC in the Tongyeong Island (Gwak et al. 2016; Lee et al. 2018). This is probably because water temperature fluctuates less in Nodaedo Island than at the bay of Tongyeong. A previous study reported a water temperature of <10 ℃ in January and February and a summer temperature of ≥25 ℃. In contrast, we reported a lower water temperature in the summer than that reported in the previous study. In the winter, the water temperature decreased to >10℃, confirming that water temperature fluctuates less in Nodaedo Island than at the bay of Tongyeong (Kim et al. 2016). The eastern waters of the South Sea of South Korea contain a mix of Tsushima warm current waters, cold water from the East Sea, and the coastal waters of the South Sea, resulting in complex marine characteristics. Particularly, a water temperature front is formed between the waters at Nodaedo Island and the bay of Tongyeong. Therefore, the study area provides a more stable environment than the bay of Tongyeong.

Most of the species observed only by UVC were represented by only a small number of individuals with a low frequency of occurrence. Takahara et al. (2012) reported a significant relationship between the biomass and eDNA concentration in living organisms. Therefore, the number of individuals for a given species can be so small that the eDNA concentration becomes undetectable. However, although the number of individuals of Ostorhinchus semilineatus increased considerably from September to November compared to other species, this increase was only detected through the UVC method. This was likely because the juvenile fish were schooling in a limited area at the time of underwater observation, resulting in a low concentration of eDNA. 

Most of the subtropical and tropical fish species that were observed in the coast of Nodaedo Island appeared between September and December, with only 1–2 individuals per species. Lee et al. (2018) also reported mostly subtropical fish species during the same period. In general, the appearance of subtropical and tropical fish species at the temperate coast can be interpreted as the result of transport from other locations due to ocean currents (Nakamura et al. 2012). After the summer, the flow of ocean currents in the South Sea changes to the northeast from Jeju Island (Kim and Bae 2011). Considering that the period from June to September is the spawning season of Pomacentrus coelestis, Chaetodontoplus septentrionalis, and Labroides dimidiatus (Sakai and Kohda 2001; Kim et al. 2005; Chen and Tzeng 2009), it is possible that their larval and juvenile fish were transported to the coastal waters of Tongyeong. Tropical fish can survive when the water temperature is at least 16–18 ℃ (Figueira and Booth 2010). However, in the study area, the winter water temperature was <15 ℃, which is unsuitable for the survival of subtropical and tropical fish. Therefore, these fish species were not observed during the winter by UVC. Additionally, these fish likely did not reproductive in this period (Nakamura et al. 2013). In contrast, tropical species may settle in the southern coast if the annual average water temperature of the south coast of Korea continues to increase and if environmental conditions such as food and water temperature are favorable for tropical and subtropical fish species (Harriott and Banks 2002; Seong et al. 2010). Additionally, subtropical and tropical fish species observed in UVC surveys were not detected through eDNA metabarcoding, suggesting that the UVC method provides a uniquely advantageous approach for the detection of rare fish species.  

2) eDNA metabarcoding 

To the best of our knowledge, our study is the first to identify marine fish species using eDNA in nearly ten years, with Thomsen et al. (2012) being the last authors to conduct a similar study. Compared with traditional methods (Fyke net, underwater visual census, and trawl), the eDNA method has been proven in various studies to be both time- and cost-efficient (Shaw et al. 2016; Thomsen et al. 2016; Hinlo et al. 2017; Yamamoto et al. 2017). We used both the eDNA metabarcoding and UVC methods to overcome the limitations of existing methods for the study of fish diversity and our findings revealed the occurrence of various fish species. The fish species detected using eDNA metabarcoding were generally identified as species that use micro-habitats such as Blenniidae and Stichaeidae, which are difficult to observe by UVC, as well as fish species with migratory and schooling behaviors such as C. pallasiiEngraulis japonicusAmmodytes personatus, and Trachurus japonicus. Additionally, a complementary relationship with the UVC method was observed. These results were similar to those of previous studies (Thomsen et al. 2012; Stat et al. 2019; Valdivia-Carrillo et al. 2019). Therefore, eDNA metabarcoding has the advantage of detecting fish that cannot be easily observed by the UVC method.

Among the fish species that appeared more than six times a year, E. japonicus and T. japonicus were well reflected in this study, as they appeared continuously in all the four seasons in the South Sea (Kim et al. 2011, 2013). In contrast, A. personatus is known to prefer relatively cold waters. Therefore, when the temperature is high during the summer, these fish become less active and tend to rest in sandy areas (Kim et al. 1994). Due to these seasonal changes in behavior, this species was detected in all seasons except in the summer. Parajulis poecilepterus inhabits places affected by warm currents. Therefore, this species did not appear in winter because of the low water temperature (Lee and Kim 1996). Clupea. pallasii migrates to the South Sea for spawning in the winter (Lee et al. 2017). However, in this study, the eDNA metabarcoding detected this species in July 2018. Further research is thus needed to clarify whether C. pallasii was included in the feed used by fish farms in the surrounding areas. 

The eDNA detection results reflect fish species that appear depending on the season. However, the number of species detected per month and the water temperature did not appear to be significantly correlated. For example, although the lowest water temperature occurred in January 2019, this period exhibited the highest number of identified species in the entire year, with 18 and 8 species of fish classified at the family and genus levels. This is also different from the results of UVC, which were conducted simultaneously. Unlike in in January 2019, a small number of species were detected in December and February because there was no significant difference in the water temperature in January, December, and February. Strickler et al. (2015) reported no significant difference in DNA degradation at 5 and 20 ℃. Therefore, the effect of water temperature was likely negligible, which is also clear from the fact that a large number of species were detected in January. Stat et al. (2019) stated that eDNA detectability largely depended on DNA concentration and biological mobility. Therefore, the eDNA levels of the species detected in January were likely high due to the presence of a large number of individuals, as well as their high activity and wide DNA distribution. In fact, most of the highly active migratory fish species such as Sardinops sagax, C. pallasii, Konosirus punctatus, A. personatus, Seriola quinqueradiata, and E. japonicus appeared in January. Additionally, fish species detected more than six times during the year were generally considered active fish species such as E. japonicus, T. japonicus, A. personatus, C. pallasii, and Pagrus major, which supports our hypothesis. In other words, the eDNA is highly reflective of the fish species present rather than water temperature, whereas UVC surveys are believed to provide a snapshot of the winter species composition in limited spaces. Scomber spp., which appeared 11 times in this study, was likely misclassified as Scomber japonicus, which occurs year-round in the South Sea (Lee and Kim 2011). However, S. japonicus had never been detected through UVC. Davis and Anderson (1989) reported that the UVC survey method may underestimate fish that swim quickly or that mainly inhabit surface waters. Therefore, mackerel was not observed in the UVC survey in this study, which was likely due to the inherent limitations of the UVC method.

Sebastes spp. is presumed to be S. inermis, as it appeared 12 times in a year, as shown by UVC. Hexagrammos spp. (both H. otakii and H. agrammus) has been reported in many UVC surveys. In this study, 12S rRNA was targeted for metabarcoding analysis, following the method used by Miya et al. (2015, 2016). When the same primers as those employed in this study were used, it is possible that both Sebastes spp. and Takifugu spp. could not be classified at the species level (Miya et al. 2015; Aizu et al. 2017; Yamamoto et al. 2017). According to Aizu et al. (2017) and Kelly et al. (2014), such a result may appear depending on the primers used or may be caused by missing information related to the mitochondrial DNA data of a specific species. Therefore, the use of various primer sets and/or database verification is necessary for fish that have not been classified at the species level. Kim and Park (2002) reported that Salvelinus leucomaenis occurred in the northeastern Korean peninsula, suggesting that this species does not inhabit the South Sea. However, these are false-positive cases, which likely resulted from contamination caused by artificial inflows from ports, fishing boats, and fish markets (Ficetola et al. 2015; Yamamoto et al. 2017).

Fish detected only by eDNA are mostly bottom dwelling, such as Pholis nebulosa, as well as pelagic fish such as C. pallasii. Because the bottom is sandy and muddy at a water depth of >7 m, P. nebulosa living in the bedrock can be easily detected in the survey area using the eDNA method. However, pelagic and schooling fish are unlikely to inhabit the surveyed waters because they are highly mobile and some are migratory. Therefore, it is highly likely that the detected eDNA was transported from the outside by tides or currents. In fact, a study reported that the flow of seawater such as tides affects the detection results by transporting the eDNA outside the survey area. Therefore, in the case of the pelagic fish detected in this study, it is possible that the detected eDNA corresponded to fish that did not naturally inhabit the survey area (Valdivia-Carrillo et al. 2019). The half-life for the detection of eDNA in an artificial seawater environment has been reported to be 26 hours (Collins et al. 2018). However, the decomposition rate of DNA in the sea is expected to be faster because it depends on environmental factors such as pH, water temperature, salinity, UV radiations, and microorganisms (Strickler et al. 2015; Salter 2018). Kelly et al. (2018) compared the eDNA of fish clusters in three regions and reported that seawater flow dynamics (e.g., tidal flow) are responsible for the transport of environmental DNA from non-survey areas and affects the detection results. Andruszkiewicz et al. (2019) reported the migration of eDNA at a 10 km/day rate due to ocean currents. This suggests that the pelagic and schooling fish species detected in this study were in fact species that inhabited the southeastern sea of Korea.

In this study, the ANOSIM analysis results for presence/absence data by survey method showed significant differences between eDNA metabarcoding and the UVC. This is fish species commonly found through UVC and eDNA metabarcoding can be interpreted as the following types: 1) dive observation (number of individuals: few, number of appearances: few), eDNA (number of detections: many); 2) dive observation (number of individuals: few, number of appearances: many), eDNA (number of detections: few); 3) dive observation (number of individuals: many, number of appearances: many), and eDNA (number of detections: few) (Table 8). In the first type, Acanthopagrus schlegelii and Mugil cephalus were considered in this study. This suggests that there were more individuals in the study area, in addition to those observed in the UVC surveys. The second type includes Enneapterygius etheostomaRudarius ercodesParacentropogon rubripinnisSemicossyphus reticulatus, Pseudolabrus sieboldi, and Pterogobius zonoleucus, which are believed to have limited detection rates because of their low DNA concentration. The third type includes C. notate, which appeared 12 times in 12 surveys. Additionally, the number of individuals was large and it occupied the dominant species position. However, eDNA metabarcoding was detected only once during the entire survey period. Miya et al. (2015) reported the detection of Chromis spp. in a tank using similar procedures as those reported herein. However, because this is a one-time detection, the DNA analysis of specific fish species is not yet fully reflected. Therefore, it is necessary to precisely analyze the presence or absence of C. notata using qPCR.

The UVC method used in this study is suitable for fish in which eDNA is not detected because of the occurrence of a small number of individuals. This method is suitable for detecting fish with pelagic and high activity. Valdivia-Carrillo et al. (2019) also showed some differences in the species composition of the two methods, and eDNA detected only fish groups with pelagic and high activity such as Istiophoridae, Scombridae, Mugilidae, and Clupeidae. In contrast, fishes observed only in UVC showed a similar pattern to the results of the present study, as fishes with strong sedentary habitat were observed. Therefore, it appears that there is a complementary relationship between the two methods. It is difficult to conclude whether the fish species that appeared and were detected in this study reflect all of the fish species that have settled on the coast of the Nodaedo Island. However, considering the appearance of fish depending on seasonal changes and the transport of eDNA by ocean currents, the results of this study likely do not exclusively reflect the biodiversity of Nodaedo Island but also that of nearby areas such as the southeastern coast of Korea.

Declarations

Acknowledgements We would like to thank all personnel on board R/V Charmbadaho (36 ton) of Gyeongsang National University for their kind assistance during the cruise. 

Funding This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2016R1D1A1B03931588). 

Author Contributions Statement Original Draft Preparation and Writing: Yong-Deuk Lee, Conceptualization and Review & Editing: Woo-Seok Gwak, Analyzed the data: Yong-Deuk Lee, Gang-Min Lee. All authors reviewed the manuscript. 

Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Conflict of interest The authors declare that they have no conflicts of interest.

Reference

  1. Aizu M, Seino S, Sado T, Miya M (2017) Environmental DNA metabarcoding with MiFish primer reveals marine fish fauna of Tsushima Island, Nagasaki for establishing a marine protected area, Proceedings of The JSFS 85th Anniversary Commemorative International Symposium “Fisheries Science for Future Generations”. Japan 04005
  2. Andruszkiewicz EA, Koseff JR, Fringer OB, et al (2019) Modeling environmental DNA transport in the coastal ocean using Lagrangian particle tracking. Front Mar Sci 477. https://doi.org/10.3389/fmars.2019.00477
  3. Chen KY, Tzeng WN (2009) Reproductive mode of the blue-striped angelfish Chaetodontoplus septentrionalis in northeastern Taiwan. Zool Stud 48:468–476
  4. Choo HS, Kim DS (1998) The effect of variations in the Tsushima warm currents on the egg and larval transport of anchovy in the southern sea of Korea. Korean J Fish Aquat Sci 31:226–244
  5. Collins RA, Wangensteen OS, O’Gorman EJ, et al (2018) Persistence of environmental DNA in marine systems. Commun Biol 1:1–11. https://doi.org/10.1038/s42003-018-0192-6
  6. Davis GE, Anderson TW (1989) Population estimates of four kelp forest fishes and an evaluation of three in situ assessment techniques. Bull Mar Sci 44:1138–1151
  7. Ficetola GF, Miaud C, Pompanon F, Taberlet P (2008) Species detection using environmental DNA from water samples. Biol Lett 4:423–425. https://doi.org/10.1098/rsbl.2008.0118
  8. Ficetola GF, Pansu J, Bonin A, et al (2015) Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol Ecol Resour 15:543–556. https://doi.org/10.1111/1755-0998.12338
  9. Figueira WF, Booth DJ (2010) Increasing ocean temperatures allow tropical fishes to survive overwinter in temperate waters. Glob Chang Biol 16:506–516. https://doi.org/10.1111/j.1365-2486.2009.01934.x
  10. Froese R, Pauly D (2022) FishBase. world wide web electronic publication. http//www. fishbase.org. Accessed 25 July 2022
  11. Grey EK, Bernatchez L, Cassey P, et al (2018) Effects of sampling effort on biodiversity patterns estimated from environmental DNA metabarcoding surveys. Sci Rep 8:1–10. https://doi.org/10.1038/s41598-018-27048-2
  12. Guo X, Miyazawa Y, Yamagata T (2006) The Kuroshio onshore intrusion along the shelf break of the East China Sea: The origin of the Tsushima Warm Current. J Phys Oceanogr 36:2205–2231. https://doi.org/10.1175/jpo2976.1
  13. Gwak WS, Lee SH, Lee YD (2016) Fish assemblages by SCUBA observations in the water off Tongyeong, Korea. Korean J Ichthyol 28:100–109
  14. Han DH, Lee DH, Park JS, et al (2017) Species composition of fish assemblage in eelgrass bed of Bongam on Hansando island, Korea. Korean J Ichthyol 29:130–138
  15. Harriott V, Banks S (2002) Latitudinal variation in coral communities in eastern Australia: a qualitative biophysical model of factors regulating coral reefs. Coral Reefs 21:83–94. https://doi.org/10.1007/s00338-001-0201-x
  16. Hinlo R, Furlan E, Suitor L, Gleeson D (2017) Environmental DNA monitoring and management of invasive fish: comparison of eDNA and fyke netting. Manag Biol Invasions 8:89–100. https://doi.org/10.3391/mbi.2017.8.1.09
  17. Huh SH, Choi HC, Baeck GW, et al (2013) Seasonal distribution of larval fishes in the central and southern surface waters of the East Sea. Korean J Fish Aquat Sci 46:216–222. https://doi.org/10.5657/kfas.2013.0216
  18. Huh SH, Kwak SN (1998) Feeding habits of Sebastes inermis in the eelgrass (Zostera marina) bed in Kwangyang Bay. Korean J Fish Aquat Sci 31:168–175
  19. Joshi NA, Fass J (2011) Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33)[Software]
  20. Kelly RP, Gallego R, Jacobs-Palmer E (2018) The effect of tides on nearshore environmental DNA. PeerJ 6:e4521. https://doi.org/10.7717/peerj.4521
  21. Kelly RP, Port JA, Yamahara KM, Crowder LB (2014) Using environmental DNA to census marine fishes in a large mesocosm. PLoS One 9:e86175. https://doi.org/10.1371/journal.pone.0086175
  22. Kim BK, Lee CR, Lee MO, Kim JK (2016) Seasonal characteristics of temperature and salinity variations around the Tongyeong and Geoje coastal waters by a cluster analysis. J Korean Soc Mar Environ Energy 19:173–184. https://doi.org/10.7846/jkosmee.2016.19.3.173
  23. Kim DS, Bae SW (2011) A study on the transport of anchovy Engraulis japornicus Egg-larvae in the south sea of Korea. J Environ Sci Int 20:1403–1415. https://doi.org/10.5322/jes.2011.20.11.1403
  24. Kim HY, Choi MS, Seo YI, et al (2011) Recruitment characteristics of jack mackerel, Trachurus japonicus, in the waters around the Geumo Islands by using both sides fyke nets. J Korean Soc Fish Ocean Technol 47:356–368. https://doi.org/10.3796/ksft.2011.47.4.356
  25. Kim IS, Park JY (2002) Freshwater fishes of Korea. Kyo hak sa, Seoul
  26. Kim IS, Choi Y, Lee CL, et al (2005) Illustrated book of Korean fishes. Kyo hak sa, Seoul
  27. Kim MJ, Youn SH, Kim J-Y, Oh C-W (2013) Feeding characteristics of the Japanese anchovy, Engraulis japonicus according to the distribution of zooplankton in the coastal waters of southern Korea. Korean J Environ Biol 31:275–287. https://doi.org/10.11626/kjeb.2013.31.4.275
  28. Kim YU, Kim UM, Kim YS (1994) Commercial fishes of the coastal and offshore waters in Korea, National fisheries Research and Development Institute. Yemoonsa, Pusan
  29. KORDI (1998) Studies on the development of marine ranching program in Korea. Korea Ocean Research & Development Institute Report
  30. Kulbicki M (1998) How the acquired behaviour of commercial reef fishes may influence the results obtained from visual censuses. J Exp Mar Bio Ecol 222:11–30. https://doi.org/10.1016/s0022-0981(97)00133-0
  31. Lee GM, Lee YD, Park JY, Gwak WS (2018) Species composition and seasonal variation of fish by SCUBA observation in the coastal water off Tongyeong, Korea. Korean J Ichthyol 30:107–113. https://doi.org/10.35399/isk.30.2.6
  32. Lee HN, Kim HS (2011) Variation of fisheries conditions of mackerel (Scomber japonicus) fishing ground for large purse seine fisheries. J Korean Soc Fish Ocean Technol 47:108–117. https://doi.org/10.3796/ksft.2011.47.2.108
  33. Lee WO, Kim IS (1996) A revision of the suborder Labroidei (Pisces: Perciformes) from Korea. Korean J Ichthyol 8:22–48
  34. Lee YD, Choi JH, Moon SY, et al (2017) Spawning characteristics of Clupea pallasii in the coastal waters off Gyeongnam, Korea, during spawning season. Ocean Sci J 52:581–586. https://doi.org/10.1007/s12601-017-0046-z
  35. Lowry M, Folpp H, Gregson M, Suthers I (2012) Comparison of baited remote underwater video (BRUV) and underwater visual census (UVC) for assessment of artificial reefs in estuaries. J Exp Mar Bio Ecol 416:243–253. https://doi.org/10.1016/j.jembe.2012.01.013
  36. Mächler E, Deiner K, Spahn F, Altermatt F (2016) Fishing in the water: effect of sampled water volume on environmental DNA-based detection of macroinvertebrates. Environ Sci Technol 50:305–312. https://doi.org/10.1021/acs.est.5b04188
  37. Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:2957–2963. https://doi.org/10.1093/bioinformatics/btr507
  38. Mateo IR, Tobías WJ (2001) The role of neashore habitats as nursery grounds for juvenile fishes on the northeast coast of St. Croix, USVI. Proceedings of the Gulf and Caribbean Fisheries Institute 52: 512–530
  39. Miya M, Minamoto T, Yamanaka H, et al (2016) Use of a filter cartridge for filtration of water samples and extraction of environmental DNA. Journal Vis Exp e54741. https://doi.org/10.3791/54741
  40. Miya M, Sato Y, Fukunaga T, et al (2015) MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R Soc open Sci 2:150088. https://doi.org/10.1098/rsos.150088
  41. Mora C, Tittensor DP, Myers RA (2008) The completeness of taxonomic inventories for describing the global diversity and distribution of marine fishes. Proc R Soc B Biol Sci 275:149–155. https://doi.org/10.1098/rspb.2007.1315
  42. Nakamura Y, Feary DA, Kanda M, Yamaoka K (2013) Tropical fishes dominate temperate reef fish communities within western Japan. PLoS One 8:e81107. https://doi.org/10.1371/journal.pone.0081107
  43. Nakamura Y, Shibuno T, Yamaoka K (2012) Relationship between pelagic larval duration and abundance of tropical fishes on temperate coasts of Japan. J Fish Biol 80:346–357. https://doi.org/10.1111/j.1095-8649.2011.03175.x
  44. Ochi H (1986) Breeding synchrony and spawning intervals in the temperate damselfish Chromis notata. Environ Biol fishes 17:117–423. https://doi.org/10.1007/bf00001741
  45. Oh SY, Noh CH (2006) Changes of water quality during the seed production period of dark-banded rockfish Sebastes inermis in large scale tanks. J Aquac 19:25–32
  46. Oksanen J (2013) Vegan: ecological diversity. R Project 368:1–11
  47. Parrish JD (1989) Fish communities of interacting shallow-water habitats in tropical oceanic regions. Mar Ecol Prog Ser Oldend 58:143–160. https://doi.org/10.3354/meps058143
  48. Roessig JM, Woodley CM, Cech JJ, Hansen LJ (2004) Effects of global climate change on marine and estuarine fishes and fisheries. Rev fish Biol Fish 14:251–275. https://doi.org/10.1007/s11160-004-6749-0
  49. Rognes T, Flouri T, Nichols B, et al (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. https://doi.org/10.7717/peerj.2584
  50. Sakai Y, Kohda M (2001) Spawning timing of the cleaner wrasse, Labroides dimidiatus, on a warm temperate rocky shore. Ichthyol Res 48:23–30. https://doi.org/10.1007/s10228-001-8113-x
  51. Salter I (2018) Seasonal variability in the persistence of dissolved environmental DNA (eDNA) in a marine system: The role of microbial nutrient limitation. PLoS One 13:e0192409. https://doi.org/10.1371/journal.pone.0192409
  52. Seong KT, Hwang JD, Han IS, et al (2010) Characteristic for long-term trends of temperature in the Korean waters. J Korean Soc Mar Environ Saf 16:353–360
  53. Shaw JLA, Clarke LJ, Wedderburn SD, et al (2016) Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol Conserv 197:131–138. https://doi.org/10.1016/j.biocon.2016.03.010
  54. Shin HJ, Kim SW, Choi YU (2014) Morphological and genetic characteristics of pearl-spot damselfish Chromis notata (Teleostei: Pomacentridae) in coastal waters of East sea (Sea of Japan) and Jejudo. Ocean Polar Res 36:189–197. https://doi.org/10.4217/opr.2014.36.2.189
  55. Smith-Vaniz WF, Williams I (2016) Parupeneus indicus (errata version published in 2017). The IUCN Red List 2016: e.T69182614A115460779. https://dx.doi.org/10.2305/IUCN.UK.2016-3.RLTS.T69182614A69183294.en. Accessed on 09 August 2022.
  56. St. John J, Russ GR, Gladstone W (1990) Accuracy and bias of visual estimates of numbers, size structure and biomass of a coral reef fish. Mar Ecol Prog Ser 253–262. https://doi.org/10.3354/meps064253
  57. Stat M, John J, DiBattista JD, et al (2019) Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv Biol 33:196–205. https://doi.org/10.1111/cobi.13183
  58. Strickler KM, Fremier AK, Goldberg CS (2015) Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol Conserv 183:85–92. https://doi.org/10.1016/j.biocon.2014.11.038
  59. Taberlet P, Coissac E, Hajibabaei M, Rieseberg LH (2012) Environmental DNA. Molecular Ecology21: 1789-1793. https://doi.org/10.1111/j.1365-294x.2012.05542.x
  60. Takahara T, Minamoto T, Yamanaka H, et al (2012) Estimation of fish biomass using environmental DNA. PLoS One 7:e35868. https://doi.org/10.1371/journal.pone.0035868
  61. Thompson AA, Mapstone BD (1997) Observer effects and training in underwater visual surveys of reef fishes. Mar Ecol Prog Ser 154:53–63. https://doi.org/10.3354/meps154053
  62. Thomsen PF, Kielgast J, Iversen LL, et al (2012) Detection of a diverse marine fish fauna using environmental DNA from seawater samples. PLoS One 7:e41732. https://doi.org/10.1371/journal.pone.0041732
  63. Thomsen PF, Møller PR, Sigsgaard EE, et al (2016) Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes. PLoS One 11:e0165252. https://doi.org/10.1371/journal.pone.0165252
  64. Thresher RE, Gunn JS (1986) Comparative analysis of visual census techniques for highly mobile, reef-associated piscivores (Carangidae). Environ Biol Fishes 17:93–116. https://doi.org/10.1007/bf00001740
  65. Ulibarri RM, Bonar SA, Rees C, et al (2017) Comparing efficiency of American Fisheries Society standard snorkeling techniques to environmental DNA sampling techniques. North Am J Fish Manag 37:644–651. https://doi.org/10.1080/02755947.2017.1306005
  66. Valdivia-Carrillo T, Rocha-Olivares A, Reyes-Bonilla H, et al (2019) Beyond traditional biodiversity fish monitoring: environmental DNA metabarcoding and simultaneous underwater visual census detect different sets of a complex fish community at a marine biodiversity hotspot. BioRxiv 806729. https://doi.org/10.1101/806729
  67. Watson RA, Carlos GM, Samoilys MA (1995) Bias introduced by the non-random movement of fish in visual transect surveys. Ecol Modell 77:205–214. https://doi.org/10.1016/0304-3800(93)e0085-h
  68. Wood SA, Smith KF, Banks JC, et al (2013) Molecular genetic tools for environmental monitoring of New Zealand’s aquatic habitats, past, present and the future. New Zeal J Mar Freshw Res 47:90–119. https://doi.org/10.1080/00288330.2012.745885
  69. Yamamoto S, Masuda R, Sato Y, et al (2017) Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci Rep 7:1–12. https://doi.org/10.1038/srep40368
  70. Yu DW, Ji Y, Emerson BC, et al (2012) Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol Evol 3:613–623. https://doi.org/10.1111/j.2041-210x.2012.00198.x

Tables

Table 1. Monthly variations in the number of individuals observed by UVC from the coastal waters off Nodaedo Island, Tongyeong Korea during the period of June 2018 to May 2019; n: frequency of observed fishes by UVC

 

Number of individuals

 

 

Species

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

Total

n

Abudefduf vaigiensis

 

 

 

1

 

 

 

 

 

 

 

 

1

1

Acanthogobius hasta

 

 

 

1

 

 

 

 

 

 

 

 

1

1

Acanthopagrus schlegelii

 

 

 

 

1

 

 

 

 

 

 

 

1

1

Acentrogobius pellidebilis

 

 

2

 

 

 

 

 

5

 

1

4

12

4

Acentrogobius pflaumii

 

1

 

 

 

1

 

 

 

 

 

 

2

2

Ammodytes personatus

 

5

 

 

 

 

 

 

 

 

 

 

5

1

Ostorhinchus semilineatus

 

 

 

124

30

280

 

 

 

 

 

 

434

3

Roa modesta

 

 

 

4

3

 

 

 

 

 

 

 

7

2

Chaetodontoplus septentrionalis

 

 

 

2

2

1

 

 

 

 

 

 

5

3

Chromis notata

789

620

596

872

735

422

606

4

536

895

454

1,341

7,870

12

Conger myriaster

 

 

 

 

2

 

 

 

 

 

 

 

2

1

Ditrema temminckii

 

 

 

 

 

 

1

 

2

 

8

4

15

4

Enneapterygius etheostoma

3

9

 

 

 

2

2

1

 

 

4

13

34

7

Furcina osimae

 

 

 

1

 

 

 

 

 

 

 

 

1

1

Girella punctata

 

47

 

2

 

10

 

 

 

 

 

26

85

4

Halichoeres tenuispinis

58

62

2

129

52

60

2

 

 

 

 

21

386

8

Hexagrammos agrammus

4

6

 

1

2

2

 

4

3

7

5

2

36

10

Hexagrammos otakii

4

5

 

 

 

2

1

 

2

5

2

2

23

8

Istigobius campbelli

3

8

 

 

1

2

 

 

2

2

1

4

23

8

Istigobius hoshinonis

 

1

 

9

 

 

1

 

 

 

1

2

14

5

Labroides dimidiatus

 

 

 

1

3

1

1

 

 

1

 

 

7

5

Microcanthus strigatus

 

 

 

4

 

2

1

 

2

 

 

 

9

4

Mugil cephalus

 

2

 

 

 

 

 

 

 

 

 

 

2

1

Neoclinus bryope

 

1

 

 

 

 

 

 

 

 

 

 

1

1

Neoditrema ransonnetii

40

340

186

36

 

1

18

 

 

 

106

 

727

7

Okamejei kenojei

 

 

 

 

 

 

 

 

 

1

 

 

1

1

Omobranchus elegans

 

 

 

 

 

 

 

 

 

 

 

6

6

1

Oplegnathus fasciatus

 

17

 

6

 

6

1

 

 

 

 

1

31

5

Oplegnathus punctatus

 

1

 

 

 

 

 

 

 

 

 

 

1

1

Ostorhinchus doederleini

 

 

 

3

 

 

5

 

 

 

 

 

8

2

Ostracion immaculatum

 

 

 

 

 

1

 

 

 

 

 

 

1

1

Pagrus major

1

 

 

2

1

1

 

 

 

 

 

 

5

4

Paracentropogon rubripinnis

11

8

 

3

3

2

6

 

8

9

4

3

57

10

Parajulis poecilepterus

83

220

53

210

113

72

 

 

 

 

 

37

788

7

Paralichthys olivaceus

3

1

 

1

 

1

 

 

 

 

 

 

6

4

Parapercis sexfasciata

1

 

 

 

1

 

 

 

 

 

 

 

2

2

Parupeneus biaculeatus

 

2

 

 

 

 

 

 

 

 

 

 

2

1

Parupeneus indicus

 

 

 

1

 

2

 

 

 

 

 

 

3

2

Petroscirtes breviceps

 

4

16

26

7

5

3

 

 

 

 

 

61

6

Platycephalus indicus

 

2

 

 

 

 

 

 

1

 

 

1

4

3

Pomacentrus coelestis

 

 

 

1

 

5

 

 

 

 

 

 

6

2

Pseudoblennius cottoides

4

8

 

1

 

1

1

 

 

 

2

2

19

7

Pseudoblennius percoides

 

 

 

1

 

 

1

 

 

 

 

 

2

2

Pseudolabrus sieboldi

12

7

2

5

5

1

3

 

1

5

 

3

44

10

Pteragogus flagellifer

 

 

 

 

 

4

 

 

 

 

1

2

7

3

Pterogobius elapoides

1

9

 

 

 

 

 

 

 

 

3

2

15

4

Pterogobius zonoleucus

2

6

 

2

 

1

1

3

1

2

2

 

20

9

Rudarius ercodes

4

5

3

2

2

20

11

 

 

1

 

 

48

8

Sagamia geneionema

1

 

 

1

 

4

6

1

1

1

 

 

15

7

Sebastes inermis

1,159

340

1

565

585

325

187

43

248

51

142

923

4,569

12

Sebastes longispinis

 

 

 

 

1

 

 

 

 

 

 

 

1

1

Sebastes schlegelii

 

 

 

 

 

1

 

 

 

1

 

 

2

2

Sebastes thompsoni

 

 

 

 

 

4

 

 

 

 

 

 

4

1

Sebastiscus marmoratus

 

 

 

1

 

 

 

 

 

 

 

 

1

1

Semicossyphus reticulatus

5

1

 

6

3

5

1

 

2

12

1

4

40

10

Seriola dumerili

 

3

 

 

 

 

 

 

 

 

 

 

3

1

Siganus fuscescens

 

 

 

22

21

3

 

 

 

 

 

 

46

3

Sillago japonica

 

2

 

 

 

 

 

 

 

 

 

 

2

1

Springerichthys bapturus

1

 

 

 

 

 

2

 

 

 

 

 

3

2

Stephanolepis cirrhifer

 

1

4

15

2

9

3

 

5

4

5

2

50

10

Stethojulis interrupta

 

 

 

29

5

3

6

 

 

 

 

 

43

4

Syngnathus schlegeli

 

 

 

 

 

 

 

 

1

 

 

 

1

1

Takifugu niphobles

 

 

 

2

 

 

 

 

 

 

1

2

5

3

Takifugu poecilonotus

 

 

 

3

 

1

 

 

 

1

 

 

5

3

Thalassoma lunare

 

 

 

 

 

3

 

 

 

 

 

 

3

1

Thamnaconus modestus

 

2

13

36

2

9

 

 

 

 

 

 

62

5

Trachurus japonicus

 

170

 

2

135

 

30

 

 

 

 

 

337

4

Tridentiger trigonocephalus

 

 

3

 

 

 

 

 

 

 

 

 

3

1

Vellitor centropomus

 

 

 

 

 

 

 

 

 

1

 

 

1

1

Total

2,189

1,916

881

2,133

1,717

1,275

900

56

820

999

743

2,407

16,036

 

Number of species

21

33

12

39

25

38

25

6

16

17

18

23

 

 

 

Table 2. Monthly variations in the number of reads detected by eDNA metabarcoding from the coastal waters off Nodaedo Island, Tongyeong Korea during the period of June 2018 to May 2019; n: frequency of detected fishes by eDNA

 

Number of reads

 

 

Species

Jun.

Jul.

Aug.

Sep.

Oct.

Nov.

Dec.

Jan.

Feb.

Mar.

Apr.

May

Total

n

Acanthopagrus schlegelii

 

 

2,567

 

 

 

 

1,312

 

11,825

 

456

15,704

4

Acentrogobius pflaumii

 

 

790

 

 

 

 

 

 

 

 

 

790

1

Ammodytes personatus

 

 

 

2,817

2,613

2,200

1,304

212

 

3,505

 

1,055

12,651

7

Benthosema pterotum

 

 

 

 

 

170

 

141

 

 

 

 

311

2

Chromis notata

 

105

 

 

 

 

 

 

 

 

 

 

105

1

Clupea pallasii

 

1,507

 

 

707

2,541

 

15,422

 

16,529

10,089

1,363

46,795

7

Collichthys lucidus

 

 

 

 

 

325

 

 

 

 

 

 

325

1

Coryphaena hippurus

 

 

 

 

 

 

170

 

 

 

 

 

170

1

Decapterus maruadsi

 

 

 

 

 

 

 

 

345

 

 

 

345

1

Dictyosoma burgeri

959

 

 

 

 

 

1,928

 

 

 

 

 

2,887

2

Dictyosoma rubrimaculatum

4,014

 

 

 

 

 

 

 

 

 

 

 

4,014

1

Ditrema spp.

1,904

943

5,376

4,928

 

3,314

2,361

 

677

 

 

 

19,503

7

Echelus uropterus

 

 

 

 

 

102

 

 

 

 

 

 

102

1

Engraulis japonicus

20,868

 

11,916

8,494

 

717

4,662

26,376

7,527

976

 

6,557

81,536

9

Enneapterygius etheostoma

438

 

 

 

 

 

 

 

 

 

 

 

438

1

Ernogrammus hexagrammus

259

 

 

 

 

 

 

 

 

 

 

 

259

1

Etrumeus sadina

 

 

 

 

 

 

 

473

 

 

 

 

473

1

Gadus spp.

 

 

 

 

 

 

 

3,678

 

 

 

 

3,678

1

Girella punctata

 

 

 

 

675

1,654

 

 

 

1,126

 

2,780

3,455

4

Gymnogobius castaneus

 

 

1,786

 

 

 

 

 

 

 

 

 

1,786

1

Halichoeres tenuispinis

528

889

 

 

 

 

 

 

 

 

 

 

1,417

2

Hexagrammos spp.

4,982

 

 

 

711

 

2,541

415

 

 

 

44,031

8,649

5

Hyporhamphus sajori

1,649

 

 

 

 

1,291

 

 

 

 

 

 

2,940

2

Iso flosmaris

 

 

 

 

1,330

2,483

 

 

 

 

 

 

3,813

2

Istigobius campbelli

 

 

 

 

342

 

 

 

 

 

 

 

342

1

Istigobius hoshinonis

 

 

 

 

 

 

 

 

 

433

 

 

433

1

Konosirus punctatus

 

 

 

 

 

 

7,976

25,507

 

 

 

 

33,483

2

Larimichthys polyactis

 

 

 

1,065

 

 

6,386

2,415

1,656

 

 

 

11,522

4

Lateolabrax japonicus

1,085

8,757

3,440

 

 

961

 

3,619

 

2,122

 

 

19,984

6

Lepidotrigla microptera

 

 

 

 

643

 

 

 

 

 

 

 

643

1

Liparis tessellatus

 

 

 

 

 

 

 

133

 

 

 

 

133

1

Lophius litulon

 

960

 

 

 

 

 

 

 

 

 

 

960

1

Mugil cephalus

 

 

5,763

 

 

 

 

1,231

 

4,848

5,536

 

17,378

4

Neoclinus bryope

 

498

 

 

 

 

 

 

 

 

 

 

498

1

Neoditrema ransonnetii

254

 

4,316

4,825

1,194

10,816

2,218

355

 

 

 

 

23,978

7

Oplegnathus fasciatus

 

 

 

 

 

391

 

 

 

 

 

 

391

1

Pagrus major

331

 

1,999

 

579

615

870

1,284

 

828

 

4,002

6,506

8

Parablennius yatabei

 

 

3,015

 

 

1,506

 

1,358

 

 

 

 

5,879

3

Paracentropogon rubripinnis

365

7,771

3,949

 

 

561

 

 

2,459

4,316

 

 

19,421

6

Parajulis poecilepterus

184

9,104

2,950

1,152

2,368

 

 

 

 

 

 

1,788

15,758

6

Paralichthys olivaceus

910

 

 

 

 

 

 

 

 

 

 

 

910

1

Pholis nebulosa

 

 

 

 

 

 

 

 

 

 

 

779

0

1

Platycephalus spp.

 

 

3,091

 

 

 

 

 

 

 

 

 

3,091

1

Pleuronichthys cornutus

 

 

 

 

 

 

 

 

 

1,262

 

 

1,262

1

Pseudolabrus sieboldi

195

 

 

 

 

 

 

 

 

2,038

 

 

2,233

2

Pseudopleuronectes spp.

 

 

 

 

 

 

 

2,966

1,249

 

 

 

4,215

2

Pterogobius zonoleucus

696

 

 

 

 

 

 

 

 

 

 

 

696

1

Rudarius ercodes

1,892

 

 

1,314

1,354

 

 

 

 

 

 

 

4,560

3

Sardinella zunasi

 

 

 

 

 

 

 

7,125

3,706

 

7,592

 

18,423

3

Sardinops sagax

 

 

 

 

682

1,163

 

1,041

 

 

 

 

2,886

3

Scomber spp.

 

3,633

940

11,961

7,591

3,865

9,924

711

6,122

2,885

8,687

1,321

56,319

11

Scomberomorus niphonius

 

 

3,018

 

 

300

 

 

 

 

 

 

3,318

2

Scombrops boops

 

 

 

 

 

664

 

 

 

 

 

 

664

1

Sebastes spp.

15,804

2,901

 

7,067

6,472

19,132

5,683

19,947

 

17,709

8,001

5,637

102,716

10

Sebastiscus marmoratus

 

 

 

 

 

 

 

1,375

 

 

 

 

1,375

1

Semicossyphus reticulatus

1,734

3,182

 

 

583

 

 

 

 

 

 

 

5,499

3

Seriola quinqueradiata

280

 

 

 

 

 

 

304

4,735

 

 

 

5,319

3

Setipinna taty

 

 

 

 

 

 

650

 

 

 

 

 

650

1

Siganus fuscescens

 

 

 

2,602

 

1,416

 

 

 

 

 

 

4,018

2

Springerichthys bapturus

 

 

 

 

 

 

 

 

 

2,261

 

 

2,261

1

Stephanolepis cirrhifer

 

3,041

406

6,322

9,800

2,281

298

 

 

 

 

 

22,148

6

Takifugu spp.

523

3,743

 

3,846

1,202

 

888

529

 

 

 

 

10,731

6

Thamnaconus modestus

146

 

 

 

3,485

 

 

 

 

 

 

 

3,631

2

Thryssa kammalensis

 

 

 

 

 

623

 

 

 

 

 

 

623

1

Thunnus spp.

 

 

 

 

 

 

 

210

 

 

 

 

210

1

Trachurus japonicus

 

 

8,545

1,071

15,207

1,767

6,805

1,786

 

2,408

 

392

37,589

8

Tribolodon hakonensis

 

2,447

 

 

 

 

 

 

 

 

 

 

2,447

1

Zoarchias glaber

 

 

 

 

 

 

 

 

 

 

 

807

0

1

Total

20,868

9,104

11,916

11,961

15,207

19,132

9,924

26,376

7,527

17,709

10,089

44,031

738,217

 

Number of species

23

15

17

13

19

25

16

26

9

16

5

13

 

 

 

Table 3. Comparison of present study with previous fish species composition studies conducted by underwater visual census in the coastal waters off Tongyeong

Previous and

Present study

Survey method

No. species

Total number of Individuals

Dominant species

Subdominant species

Gwak et al. (2016)

UVC

43

1,673

Sebastes inermis

Ditrema temminckii

Lee et al. (2018)

UVC

45

5,358

Sebastes inermis

Rudarius ercodes

Present study

UVC

69

16,036

Chromis notata

Sebastes inermis

 

Table 4. List of tropical and subtropical species observed off Nodaedo Island in Tongyeong (F: frequency, M: month)

Species

F / M

Distribution

References

Climate zone

Springerichthys bapturus

2 / Jun.~Dec.

Dokdo, Ulleungdo Island

Kim et al, 2005

Temperate

Stethojulis interrupta

4 / Sep.~Dec.

Jeju Island

Kim et al, 2005

Temperate

Abudefduf vaigiensis

1 / Sep.

Jeju Island

Kim et al, 2005

Tropical

Pomacentrus coelestis

2 / Sep.~Nov.

Jeju Island

Kim et al, 2005

Tropical

Parupeneus indicus

2 / Sep.~Nov.

Jeju Island

Kim et al, 2005

Tropical

Thalassoma lunare

1 / Nov.

Jeju Island

Kim et al, 2005

Subtropical

Parupeneus biaculeatus

1 / Jul.

Northwest pacific

Froese and Pauly, 2019

Subtropical

 

Table 5. R-value resulting from one-way ANOSIM test. the R-value after bonferroni correction was significance

Source

Global-R

P

Nodaedo Island

 

 

eDNA metabarcoding vs UVC

0.695

0.001

Bold text indicates a statistically significant difference.

 

Table 6. Species contribution (%) to average dissimilarity resulting from SIMPER between eDNA metabarcoding and UVC

 

Average

dissimilarity (%)

Species

Average

abundance

Average

dissimilarity

Species

contribution (%)

Cumulative

contribution (%)

Nodaedo

Island

eDNA metabarcoding

vs

UVC

75.94

Scomber spp.

0.92

2.68

3.53

3.53

Chromis notata

0.08

2.63

3.46

6.99

Engraulis japonicus

0.75

2.06

2.71

9.70

Pterogobius zonoleucus

0.08

2.04

2.69

12.39

Pseudolabrus sieboldi

0.17

1.97

2.59

14.98

 

Table 7. Classification of fish detected and observed by eDNA metabarcoding and UVC from the coastal waters off Nodaedo Island, Tongyeong Korea during June 2018 to May 2019

Class

Order

Family

Species

eDNA metabarcoding

UVC

Actinopteri

Acanthuriformes

Chaetodontidae

Roa modesta

 

 

 

Pomacanthidae

Chaetodontoplus septentrionalis

 

 

 

Siganidae

Siganus fuscescens

 

Acropomatiformes

Lateolabracidae

Lateolabrax japonicus

 

 

 

Scombropidae

Scombrops boops

 

 

Anguilliformes

Congridae

Conger myriaster

 

 

 

Ophichthidae

Echelus uropterus

 

 

 

Isonidae

Iso flosmaris

 

 

Beloniformes

Hemiramphidae

Hyporhamphus sajori

 

 

Blenniiformes

Blenniidae

Omobranchus elegans

 

 

 

 

Parablennius yatabei

 

 

 

 

Petroscirtes breviceps

 

 

 

Chaenopsidae

Neoclinus bryope

 

 

Tripterygiidae

Enneapterygius etheostoma

 

 

 

Springerichthys bapturus

 

Carangiformes

Carangidae

Decapterus maruadsi

 

 

 

 

Seriola dumerili

 

 

 

 

Seriola quinqueradiata

 

 

 

 

Trachurus japonicus

 

 

Coryphaenidae

Coryphaena hippurus

 

 

Centrarchiformes

Girellidae

Girella punctata

 

 

Microcanthidae

Microcanthus strigatus

 

 

 

Oplegnathidae

Oplegnathus fasciatus

 

 

 

Oplegnathus punctatus

 

 

Clupeiformes

Clupeidae

Clupea pallasii

 

 

 

 

Konosirus punctatus

 

 

 

 

Sardinella zunasi

 

 

 

 

Sardinops sagax

 

 

 

Dussumieriidae

Etrumeus sadina

 

 

 

Engraulidae

Engraulis japonicus

 

 

 

 

Setipinna taty

 

 

 

 

Thryssa kammalensis

 

 

Cypriniformes

Leuciscidae

Tribolodon hakonensis

 

 

Gadiformes

Gadidae

Gadus spp.

 

 

Gobiiformes

Gobiidae

Acanthogobius hasta

 

 

 

 

Acentrogobius pellidebilis

 

 

 

 

Acentrogobius pflaumii

 

 

 

Gymnogobius castaneus

 

 

 

 

Istigobius campbelli

 

 

 

Istigobius hoshinonis

 

 

 

Pterogobius elapoides

 

 

 

 

Pterogobius zonoleucus

 

 

 

Sagamia geneionema

 

 

 

 

Tridentiger trigonocephalus

 

 

Kurtiformes

Apogonidae

Ostorhinchus doederleini

 

 

 

 

Ostorhinchus semilineatus

 

 

Lophiiformes

Lophiidae

Lophius litulon

 

 

Mugiliformes

Mugilidae

Mugil cephalus

 

Mulliformes

Mullidae

Parupeneus biaculeatus

 

 

 

 

Parupeneus indicus

 

 

Myctophiformes

Myctophidae

Benthosema pterotum

 

 

Perciformes

Cottidae

Furcina osimae

 

 

 

 

Pseudoblennius cottoides

 

 

 

 

Pseudoblennius percoides

 

 

 

 

Vellitor centropomus

 

 

 

Embiotocidae

Ditrema temminckii

Ditrema spp.

 

 

Embiotocidae

Neoditrema ransonnetii

 

 

Pomacentridae

Abudefduf vaigiensis

 

 

 

 

Chromis notata

 

 

 

Pomacentrus coelestis

 

 

 

Labridae

Halichoeres tenuispinis

 

 

 

Labroides dimidiatus

 

 

 

 

Parajulis poecilepterus

 

 

 

Pseudolabrus sieboldi

 

 

 

Pteragogus flagellifer

 

 

 

 

Semicossyphus reticulatus

 

 

 

Stethojulis interrupta

 

 

 

 

Thalassoma lunare

 

 

 

Sciaenidae

Collichthys lucidus

 

 

 

 

Larimichthys polyactis

 

 

 

Sillaginidae

Sillago japonica

 

 

 

Sparidae

Acanthopagrus schlegelii

 

 

 

Pagrus major

 

 

Hexagrammidae

Hexagrammos agrammus

Hexagrammos spp.

 

 

 

Hexagrammos otakii

 

 

Liparidae

Liparis tessellatus

 

 

 

Platycephalidae

Platycephalus indicus

Platycephalus spp.

 

 

Sebastidae

Sebastes inermis

Sebastes spp.

 

 

 

Sebastes longispinis

 

 

 

Sebastes schlegelii

 

 

 

Sebastes thompsoni

 

 

 

Sebastiscus marmoratus

 

 

Tetrarogidae

Paracentropogon rubripinnis

 

 

Triglidae

Lepidotrigla microptera

 

 

 

Ammodytidae

Ammodytes personatus

 

 

Pinguipedidae

Parapercis sexfasciata

 

 

 

Neozoarcidae

Zoarchias glaber

 

 

 

Pholidae

Pholis nebulosa

 

 

 

Stichaeidae

Dictyosoma burgeri

 

 

 

 

Dictyosoma rubrimaculatum

 

 

 

 

Ernogrammus hexagrammus

 

 

Pleuronectiformes

Paralichthyidae

Paralichthys olivaceus

 

 

Pleuronectidae

Pleuronichthys cornutus

 

 

 

 

Pseudopleuronectes spp.

 

 

Scombriformes

Scombridae

Scomber spp.

 

 

 

 

Scomberomorus niphonius

 

 

 

 

Thunnus spp.

 

 

Syngnathiformes

Syngnathidae

Syngnathus schlegeli

 

 

Tetraodontiformes

Monacanthidae

Rudarius ercodes

 

 

 

Stephanolepis cirrhifer

 

 

 

Thamnaconus modestus

 

 

Ostraciidae

Ostracion immaculatum

 

 

 

Tetraodontidae

Takifugu niphobles

Takifugu spp.

 

 

 

Takifugu poecilonotus

Elasmobranchii

Rajiformes

Rajidae

Okamejei kenojei

 

Table 8. Types according to the number of appearances and individuals in UVC and the number of detected in eDNA metabarcoding

Type

UVC

eDNA metabarcoding

No. of appearances

No. of individuals

No. of detections

1

Few

Few

Many

2

Many

Few

Few

3

Many

Many

Few