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Article

Application of DNA Metabarcoding for Identifying the Diet of Asian Clam (Corbicula fluminea, Müller, 1774)

by
Yu-Ji Heo
1,
Hyunbin Jo
2,3,
Ji Yoon Kim
4,
Gu-Yeon Kim
5,6,
Gea-Jae Joo
2 and
Hyun-Woo Kim
1,*
1
Department of Environmental Education, Sunchon National University, Suncheon 57922, Republic of Korea
2
Department of Integrated Biological Science, Pusan National University, Busan 46241, Republic of Korea
3
Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea
4
Department of Biological Science, Kunsan National University, Gunsan 54150, Republic of Korea
5
Department of Science Education, Kyungnam University, Changwon 51767, Republic of Korea
6
Biodiversity Center, Kyungnam University, Changwon 51767, Republic of Korea
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 441; https://doi.org/10.3390/su15010441
Submission received: 17 October 2022 / Revised: 20 December 2022 / Accepted: 20 December 2022 / Published: 27 December 2022
(This article belongs to the Special Issue Biodiversity in Freshwater)
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Abstract

:
Corbicula has often been reported as one of the most invasive freshwater species in the world. It plays an important role in the food chains of brackish water zones in Korea, where it is predominant. However, detailed information on the Corbicula diet is still lacking. The purpose of this study was to identify the potential prey of Corbicula fluminea in the Seomjin River using a DNA metabarcoding approach, as very little is known about its feeding selectivity in natural conditions. A survey was conducted at two study sites (1 and 2) in the Seomjin River in November 2021. The two sites were selected based on increasing salinity gradient. The dominant operational taxonomic unit in the pseudofeces and gut content of C. fluminea was Microcyclops varicans (Copepoda) and Oncorhynchus mykiss (Fish), respectively. The alpha diversity at site 1 was higher than that at site 2. More diverse potential prey species were identified at the site with low salinity (i.e., site 1). The utilization of this method is strongly recommended for determining specific predator–prey relationships in complex estuarine ecosystem.

1. Introduction

Estuaries are the among the most productive ecosystems on the earth, being an ecological transition area where freshwater and saltwater intersect and an area having a unique combination of physical, chemical, and biological components [1,2]. In particular, estuaries experience a very drastic change in salt concentration and thus contain various unique aquatic organisms [3]. In aquatic ecosystems, producers, consumers, and decomposers are systematically linked at each trophic level to form food chains, which are intertwined like a net to form a food web [4]. It is important to understand the role and function of interactions in the food web of aquatic ecosystems [5,6,7,8].
As ecosystem engineers, filter-feeding bivalves exert profound effects on their environment via feeding, metabolic activity, and movement, which can cause bioturbation effects [9,10]. In general, bivalves exhibit a high feeding capacity for phytoplankton as their main potential food [2], but recently other taxonomic groups, such as zooplankton and bacteria, have also been found to be important potential foods [3,4,5]. This selective feeding of bivalves allows them to highly modify plankton community structure [9,11]. By filtering matter from the water column [12,13,14], they can contribute to a marked decrease in particle concentrations [15,16] and an increase in water clarity [17].
The filtering function of bivalve species may potentially change the ecological conditions of aquatic ecosystems [18,19]. The small freshwater clam Corbicula fluminea is one such bivalve species, which belongs to the family Cyrenidae and inhabits shallow waters of freshwater and brackish water bodies [20,21]. Its native range is predominately distributed in Southeast Asia [22]. It has often been reported as one of the most invasive freshwater species in the world [23,24] and is now widespread across North America, South America, and Europe with limited, more recent records from Northwest Africa [25]. Haubrock et al. reported a substantial global economic impact of particularly invasive freshwater bivalves, with an estimated cost of 63.7 billion USD from 1980–2020 [26].
Despite the ecological and commercial importance of invasive bivalves, little is known about their feeding selectivity, their ontogenetic shifts in prey preference, and the levels of feeding competition between various species [27]. Since potential bivalve foods are broken down through digestive processes, identifying prey animals in the fecal content of bivalves using traditional morphological-anatomical identification is difficult [28]. To overcome the limitations of microscopic and isotopic analysis, molecular biological methods have recently been introduced for the analysis of fecal content [29]. These technologies have been promising for studying food chain interactions. Stable isotopic analysis is one of the most widely applied methods in ecological niche studies, as it can easily estimate the diets and trophic hierarchies of consumers by stepwise enrichment of heavy isotopes along the food chains in ecosystems [30,31,32]. However, it cannot provide species-specific information on prey species. Therefore, accurate species analysis of the main potential food of bivalves is important for revealing the food chain and material cycling of the target habitat. Recently, few studies have used DNA barcoding to analyze the stomach contents of Corbicula [33].
The Seomjin River is the fourth largest river among the five major rivers in Korea [34] and is the only river that has a natural function without an estuarine dam or riverside development [35]. Unlike the other major rivers in South Korea, the Seomjin River does not have a barrage in the estuary, which implies that the movement and migration of organisms are unrestricted [36]. However, salinity is increased because of seawater desalination in the Seomjin River, and seawater penetrates the vicinity of Hadong, upstream of the estuary, affecting the habitats of Corbicula as well as its size, growth, and flesh quality.
In addition to being an important fishery resource that is used as food and has high commercial value, Corbicula plays a crucial role in purifying the aquatic ecosystem as a filter-feeding bivalve [37]. It is omnivorous and plays an important role in the food chain of river ecosystems in Korea, where it is predominant [35]. Therefore, in the present study, we demonstrate the applicability and effectiveness of the DNA metabarcoding approach for identifying prey of C. fluminea. Very little is known about its feeding selectivity in natural conditions. The specific objectives were (1) to determine the efficiency of this method for identifying potential food according to the DNA analysis sample (pseudofeces (PF) and gut contents), (2) to compare the sources of potential Corbicula food between sites exhibiting different salinity, and (3) to validate the results of metabarcoding of potential Corbicula food using water-based metabarcoding data. Environmental DNA (eDNA) is an organism’s genetic material indirectly obtained from various environmental samples (e.g., air, water, and soil), instead of directly sampling from the organism [38]. A specific DNA sequence region accommodates the information for identifying a particular species of interest, and eDNA collected from an environmental sample encompasses a variety of species information from an ecosystem [39].

2. Materials and Methods

2.1. Study Area and Field Sampling

The Seomjin River is located southwest of the Korean Peninsula [34] (Figure 1). The length of the river is 212.3 km, and its catchment area is approximately 4896 km2. The Seomjin watershed has an annual precipitation of 1393 mm. Rainfall in these areas is associated with East Asian monsoons and typhoons [40]. In the estuary area, the salt concentration changes geographically as well as over time depending on the season and the amount of precipitation.
A survey was conducted at two study sites (1 and 2) predominantly characterized by shallow water (generally < 3 m) and sandy substrate in the Seomjin River in November 2021. The two sites were selected based on the salinity gradient along the estuary. We sampled the surface water (approximately top 50 cm) to determine water quality. Water temperature (°C), salinity (‰), conductivity (µS/cm), dissolved oxygen (mg/L), and pH were measured on-site using portable equipment (YSI Professional Plus; Yellow Springs, OH, USA). Then, we collected water samples (1 L per sample) from the surface layer (approximately top 50 cm) at each study site. The collected water samples were transported to the laboratory in refrigerated storage. The 1-L water samples were first filtered in the laboratory through a 0.45 μm pore-size membrane (Advantec MFS membrane filter; Dublin, OH, USA) [41] for eDNA metabarcoding through next-generation sequencing (NGS).

2.2. Corbicula fluminea Collection

Corbicula fluminea were collected from the sand bed of the river in November 2021 using a shellfish dredge vessel. The fishing gear was 123 cm wide and 22 cm high with 5.94–7.43 mm bar spaces (average 6.45 mm), and 1.8–2 cm bar length. The dredge net was made of PE material and had a length of 320 cm and a size of 11 mm. The collected C. fluminea samples were transported to the laboratory in refrigerated storage. Twenty adult C. fluminea were selected from a natural population (shell length 1.6 ± 0.5 cm, shell breadth: 1.3 ± 0.4 cm, total weight: 1.3 ± 1.4 g) (n = 20, 10 individuals from each study site), and PF and gut contents were extracted from each of them.

2.2.1. Treatment for Microscopic Examination and DNA Analysis

After capture, the Corbicula fluminea individuals were placed in distilled water and stored at 4 °C for 48 h for PE sampling. After 48 h, all C. fluminea had deposited PE at the bottom of their collection tubes. Next, the C. fluminea individuals were transferred to 50-mL collection tubes for dissection. Some portion of the PF samples was transferred to a 1.5-mL tube, suspended in a final concentration of 4–5% formalin, and stored at 4 °C until microscopic examination. The other portion was filtered through a 0.45 μm pore-size membrane (Advantec MFS membrane filter; Dublin, OH, USA), and PF collected on the membrane filters were stored at −70 °C until DNA extraction. Each C. fluminea was dissected following the techniques. Gut contents were removed from the gut of each C. fluminea after dissection, and some portion of the gut contents was transferred to a 1.5 mL tube, fixed at a final concentration of 4–5% formalin, and homogenized uniformly with a pestle for microscopic examination. The other portion was suspended in a tube containing 1 mL of 100% EtOH and homogenized uniformly with a pestle for DNA extraction. All homogenized gut samples were then stored at 4 °C until microscopic examination or DNA extraction.

2.2.2. Microscopic Examination of Pseudofeces and Gut Contents

The organisms in the PF and gut content samples were counted using an optical microscope (Zeiss Axiolab re; Carl Zeiss, Inc., White Plains, NY, USA) at 40–100× magnification in a Sedgwick-Rafter chamber. Then, the counts were converted to individuals per liter. Zooplankton were identified at the genus or species level, except for nauplii and copepodites [42,43], and categorized according to taxa (rotifers, cladocerans, and copepods).

2.2.3. DNA Extraction of Pseudofeces and Gut Contents and Metagenomic Sequencing

DNA was extracted using a DNeasy Blood & Tissue Kit (Cat. No. 69504; Qiagen, Düsseldorf, Germany), in accordance with the manufacturer’s protocol. We amplified two regions of the DNA extracted from the PF and gut contents of C. fluminea. The 18S rDNA V9 barcode was used because it has often been applied to semi-quantitatively estimate relative abundances within a sample [44,45,46]. We obtained the primer information from a study by Guo et al. [46], which used the universal primers for the 18S V9 region designed by Amaral–Zettler et al. [47]. Mitochondrial cytochrome oxidase subunit I (COI) is one of the most commonly sequenced regions for biodiversity analyses of animals. However, the standard COI primers target the 658 base pair (bp) barcoding region, which is considered too large for a high-throughput sequencing platform (e.g., Ion Torrent PGM) [48]. Therefore, in the present study, metabarcoding of the 18S V9 150 bp region was used to characterize the taxonomic diversity in water samples and the PF and gut contents of Corbicula fluminea of an estuarine ecosystem of the Seomjin River in the Republic of Korea.
The 18S rRNA gene was amplified using primers with an adaptor sequence: forward primer, 5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGC-CCTGCCHTTTGTACACAC 3′/reverse primer: 5′ GTCTCGTGGGCTCGGAGATGTGTATAAGA-GACAGCCTTCYGCAGGTTCACCTAC 3′. First, to amplify the target region corresponding to the adapters using the 18S V9 primers, the following PCR conditions were set: one cycle of 3 min at 95 °C; 25 cycles of 30 s at 95 °C, 30 s at 55 °C, and 30 s at 72 °C; and a final step of 5 min at 72 °C. Second, to perform indexing PCR, the first PCR product was amplified using one cycle of 3 min at 95 °C; 8 cycles of 30 s at 95 °C, 30 s at 55 °C, and 30 s at 72 °C; and a final step of 5 min at 72 °C.
The library was sequenced using a HiSeq NGS platform (Illumina, San Diego, CA, USA). Raw reads were trimmed using CD-HIT-OTU [49], and chimeras were identified and removed using rDnaTools. For paired-end merging, FLASH (Fast Length Adjustment of SHort reads) version 1.2.11 was used [50]. After a second process of identifying and removing chimera sequences using rDnaTools (Schloss et al., 2009), merged reads were processed using QIIME version 1.9 [51] and were clustered into operational taxonomic units (OTUs) using UCLUST version 5.2.32 [52] with a greedy algorithm employing OTUs at a 97% OTU cutoff value. BLASTn (Zhang et al., 2000) was performed for each OTU sequence, which was then compared with the sequences in the NCBI reference database, and taxonomic alignment information was identified as the classification information with the highest similarity (97% identity or higher) for each sequence was used. Non-assigned reads, reads without assigned OTU, and OTUs representing < 0.1% detection rate were deleted.

2.3. Data Collection and Statistical Analysis

Palaeontological Statistics (PAST) is a tool for analyzing and describing diversity indices, such as the Shannon–Weiner and Simpson’s indices, which are typically used in biodiversity studies. We calculated dominance (Simpson, 1949) and Shannon diversity (Shannon and Weaver, 1962) indices using abundance data (i.e., read number OTUs) in the PAST 3.0 program (Hammer et al., 2001). The diversity index for potential food of the Corbicula fluminea from each sampling site was obtained using PAST. Among the most popular metrics used to quantify diversity are the Shannon index, believed to emphasize the richness component of diversity, and the Simpson’s index, emphasizing the evenness component [53]. The Simpson’s index, which considers the number and abundance of species, was used to quantify habitat biodiversity. A greater value of the Simpson’s index indicates higher potential food diversity. The Shannon index considers the number of individuals as well as the number of taxa. It varies from 0 for communities with only a single taxon to high values for communities with many taxa, each with few individuals [54,55].

3. Results

3.1. Water Chemistry

The two study sites in the Seomjin River showed differences in water quality parameters. Site 2 had a higher salinity and electrical conductivity than site 1 (Table 1). Salinity varies depending on the tide, and consequently, salinity gradually decreases from Gwangyang Bay to the upper stream of the Seomjin River (Figure 1). We found that salt concentrations gradually decreased upstream and desalination was dominant. Since electrical conductivity is directly related to the salt concentration, it shows a similar trend as salinity [56]. The water temperature at site 2 was higher than that at site 1. The pH and dissolved oxygen concentration were higher at site 2 than at site 1. All abiotic parameters had higher values at site 2 than at site 1.

3.2. Comparison of Potential Corbicula fluminea Food Based on Next-Generation Sequencing (NGS)

In total, 10,716 paired-end reads were generated from site 1 (PF content, 6802; gut content, 3655; water, 259) and 12,660 paired-end reads were generated from site 2 (PF content, 7535; gut content, 4912; water, 213) from 42 samples using 18S V9 primer sets on the Illumina HiSeq™ platform (Illumina, San Diego, CA, USA); of these, 98.0% passed Q30 (Phred quality score > 30) for improving the accuracy of sequences in this study. Base calling accuracy, measured by the Phred quality score (Q score), is the most common metric used to assess the accuracy of a sequencing platform. For example, a Q score of 30 (Q30) indicates that the probability of an incorrect base call is 1 in 1000 times. Low Q scores can increase false-positive variant calls, which can result in inaccurate conclusions and consequently higher costs for validation experiments [57].

3.2.1. Metabarcoding and Taxonomic Assignment of Operational Taxonomic Unit (OTU) According to Sites

The abundances of the assigned sequences showed different patterns among the sites. The phylum Arthropoda was present in the highest proportion in the PF contents of Corbicula fluminea from both sites 1 and 2 (64.62% and 36.06%, respectively), the phylum Cordata was present in the highest proportion in the gut contents (55.56% and 57.89% for sites 1 and 2, respectively). Phylum-level taxonomic composition of the organisms present in the water samples revealed that Myzozoa (59.29%) was predominant at site 1 and Chlorophyta (45.26%) at site 2 (Figure 2).
Alpha diversity was higher at site 1 than at site 2. Alpha diversity was 26 OTUs (PF content, 20 OTUs; gut content, 5 OTUs; and water, 9 OTUs) at site 1, which were produced with a similarity cutoff of 97%, and 23 OTUs (PF content, 16 OTUs; gut content, 6 OTUs; and water 8 OTUs) at site 2, produced with a similarity cutoff of 97%. The dominant OTU in the PF contents of C. fluminea from site 1 was assigned to Microcyclops varicans (Copepoda) and from site 2 was assigned to Haplochthonius simplex (mite and tick) (Table 2).
The dominant OTU in the gut contents of C. fluminea from both site 1 and site 2 was Oncorhynchus mykiss (Fish). The dominant OTU in site 1 water was assigned to Azadinium poporum (dinoflagellates). A. poporum was also present in the gut and PF contents of C. fluminea from site 1. The dominant OTU in site 2 water was assigned to Ostreococcus sp. (green algae).

3.2.2. Metabarcoding and Taxonomic Assignment of Operational Taxonomic Unit (OTU) from the Pseudofeces and Gut Contents

There were obvious distinctions between the potential foods of Corbicula fluminea in the PF and gut contents. The abundance of the assigned sequences showed different patterns in the PF and gut contents. The dominant phylum was assigned to Arthropoda (56.46%) in the PF content and Chordata (57.14%) in the gut content (Figure 3).
Alpha diversity was 24 OTUs (site 1, 20 OTUs and site 2, 16 OTUs) in the PF content, which were produced with a similarity cutoff of 97% and 9 OTUs (site 1, 5 OTUs and site 2, 6 OTUs) in the gut content, which were produced with a similarity cutoff of 97%. Moreover, species-level taxonomic composition in the PF content was more variable than the gut communities (Table 2).
The dominant and sub-dominant OTU in the PF content was assigned to M. varicans (Copepoda) and Clausidium vancouverense (Copepoda), respectively. The dominant and sub-dominant OTU in the gut content was assigned to O. mykiss (Fish) and Trichosporon asahii (fungus), respectively.

3.3. Microscopic Examination of the Pseudofeces and Gut Contents

The population density of zooplankton was relatively higher in the gut content than in the PF content (Table 3). The population density in the PF content was 6 ind./L (Protozoa 2 ind./L, rotifers 1 ind./L, and copepods 3 ind./L) and that in the gut content was 62 ind./L (Protozoa 1 ind./L, rotifers 52 ind./L, cladocerans 2 ind./L, copepods 4 ind./L, and unidentified zooplankton 1 ind./L). Conochilus sp. and Hexarthra sp. were particularly present in high numbers in the gut content.

3.4. Potential Food Diversity Indices

The Simpson’s index for potential food was 0.738 (PF content 0.729, gut content 0.643) at site 1 and 0.851 (PF content 0.833, gut content 0.643) at site 2. The Shannon index for potential food was 1.898 (PF content 1.835, gut content 1.303) at site 1 and 2.236 (PF content 2.134, gut content 1.309) at site 2. The diversity indices at site 2 were higher than those at site 1 (Table 4).

4. Discussion

In this study, potential food analysis samples were secured through PF and gut contents, and direct comparison was conducted with new research methods. Through metabarcoding, 24 OTUs (site 1, 20 OTUs and site 2, 16 OTUs) were identified in the PF content of Corbicula fluminea, and 9 OTUs (site 1, 5 OTU and site 2, 6 OTU) were identified in the gut content of C. fluminea. Metabarcoding allowed diverse potential food sources to be identified. In addition, the applicability of the two methods for analyzing potential food sources of C. fluminea using DNA metabarcoding was confirmed. Fecal eDNA metabarcoding sheds light on the efficient monitoring and assessment of a target ecosystem [58,59]. Fecal DNA analyses can detect prey items that are difficult to identify by direct observations of feeding behavior, and these analyses also help in establishing a robust diagnostic tool for identifying morphological features from visual observation of gut contents and feces. DNA-based diet studies using metabarcoding are becoming an increasingly common way to study trophic interactions in hydroecology.

4.1. Prey Preference of Corbicula fluminea

When PF and gut contents were analyzed by metabarcoding, various diets and potential food organisms could be identified, especially in the PF content. We performed microscopy in parallel to assess the applicability of morphological methods in analyzing the PF and gut contents. The NGS results in this study could only confirm the presence of copepods, but microscopy could identify not only copepods but also nauplii, copepodite, and crustaceans. The diversity of potential food was high in the PF content according to the NGS results and in the gut content according to microscopy results. During microscopy, the PF content was degraded owing to the characteristics of the sample and mostly organic particles were identified. Microscopic identification has the disadvantage of identifying ambiguous prey specimens because of extensive digestion and a low-level resolution, allowing the identification of taxa only higher than the family or order level. The NGS approach is advantageous for identifying smaller taxa. Microscopic identification may also be limited by the expertise and subjectivity of the scientists and may cause disturbance to the habitat; moreover, it is difficult to detect rare and endangered species [39,60]. Interestingly, we found OTUs of the fish Oncorhynchus mykiss in Corbicula fluminea individuals from two sites even though C. fluminea cannot consume O. mykiss directly. However, the adult and larval stages of this fish were difficult to distinguish through DNA barcoding. In addition, NGS cannot completely determine the number of organisms present in a sample. Analysis through a microscope enables identification according to life history and provides quantitative information. Therefore, if NGS and morphological identification are performed in parallel, complementary food source analysis will be possible. Hence, the need for such libraries should not be underestimated, and combined morphological and molecular approaches should be prioritized in future research to provide accurate sequences of correctly identified specimens.

4.2. Efficiency of Using Metabarcoding

At site 1, where the salt concentration was relatively low, the most diverse potential foods were identified. This was likely because of a survival strategy that increases feeding activity at low salinity levels. The duration of exposure to high salt concentrations negatively affects the feeding activity of these bivalves, which use a defence mechanism to solidify the shell [35]. Taxonomic resolution can vary between groups of organisms and is dependent on both the region of DNA being amplified and the overall length of variability. 18S rDNA sequences are too conserved for the discrimination of most fungal groups, and molecular identification at the species level is dependent on sequencing the ITS1-rDNA region [61]. However, 18S rDNA sequences are widely used for phylogenetic analysis and species-level identification of protists [62]. Most phylogenetic studies and/or DNA barcoding applications use a minimum of 650 bp sequence [62,63]. This is in contrast to dietary studies, in which the length of amplification products is limited owing to the rapid degradation of gut content DNA [64,65,66]. Markers with shorter fragment sizes show better results in terms of comprehensive prey detection from degraded fecal DNA [67,68]. The 18S V9 region applied in this study can be used as a general-purpose primer because of its high accuracy in prokaryotic analysis, but it is necessary to check biodiversity through primer replenishment in the future.

4.3. Surrounding Environment

Interestingly, we found OTUs from Haplochthonius simplex, which is a semi-cosmopolitan mite (Willmann, 1930) [69]. DNA meta-barcoding can provide dietary insights for estimating the impact of the food-web structure. The present study utilized DNA derived from assemblages of the PF and gut contents of Corbicula fluminea and compared these with DNA derived from water. Despite our initial hypothesis that raw water samples represent the environment of the estuary of the Seomjin River, they did not include some C. fluminea prey. In this study, samples for water quality analysis were analyzed using surface layer water. Consequently, phytoplankton accounted for most of the OTUs. However, C. fluminea is known to preferentially feed on small phytoplankton species, and Bivalvia can affect the structure and function of the food web of an ecosystem by feeding on phytoplankton. Therefore, further research on prey preference of C. fluminea is required. Competition for resources appears to be an obvious direct interaction, but the food web may be more complex [70]. It is necessary to consider the depth of water selected for analyzing the water quality in the habitats of C. fluminea [71]. Results of the current study highlight the importance of tailoring experimental and sampling design schemes to match the experimental questions being addressed.

4.4. Management

The survey sites we investigated exhibited a difference in salinity. The food source of the C. fluminea in their habitat environment was successfully identified by DNA metabarcoding. Although the present study was informative, the data synthesized lacked any of the high-throughput sequence analysis available currently. Paired-end reads were higher in the PF content (site 1, 6802; site 2, 7535) than in the gut content (site 1, 3655; site 2, 4912). Experiments that aim to study the food sources of a target bivalve should sample gut contents prior to an egestion period. As it is used as food, C. fluminea is a fishery resource of high commercial value [72]. If a study focuses on analyzing C. fluminea food sources according to spatio-temporal changes in the future, the growth characteristics of individual preys can be identified. As a result, it can help improve the amount of resources for C. fluminea and protect these resources. In addition, the association of the removal of particulate matter in the water and the discharge of matter such as nutrients and PF in an inorganic form with bivalve diet can be investigated [73,74,75]. However, since there are differences in feeding apparatus for each species, DNA samples should also be extracted to consider feeding habits. Moreover, prey species were identified from the pseudofeces and gut contents of C. fluminea and the water of its natural habitat, providing an ideal comparison for future research.

Author Contributions

Conceptualization, Y.-J.H. and H.J.; methodology, Y.-J.H. and H.J.; investigation, Y.-J.H. and H.J.; writing—original draft preparation, Y.-J.H. and H.J.; writing—review and editing, G.-J.J., J.Y.K. and G.-Y.K.; visualization, Y.-J.H. and H.J.; supervision, H.-W.K.; project administration, H.-W.K.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Research Foundation of Korea (grant number NRF-2020R1C1C1009066).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map illustrating the study sites in the Seomjin River.
Figure 1. Map illustrating the study sites in the Seomjin River.
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Figure 2. Relative abundance (%) of the operational taxonomic units (OTUs) in the PF content (n = 20, 10 individuals from each study site) and gut content (n = 20, 10 individuals from each study site) of C. fluminea and water (n = 2, one sample from each study site) according to phyla.
Figure 2. Relative abundance (%) of the operational taxonomic units (OTUs) in the PF content (n = 20, 10 individuals from each study site) and gut content (n = 20, 10 individuals from each study site) of C. fluminea and water (n = 2, one sample from each study site) according to phyla.
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Figure 3. Relative abundance (%) of the operational taxonomic units (OTUs) in the PF (n = 20) and gut (n = 20) contents of C. fluminea according to phlya.
Figure 3. Relative abundance (%) of the operational taxonomic units (OTUs) in the PF (n = 20) and gut (n = 20) contents of C. fluminea according to phlya.
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Table
Table 1. Summary of environmental characteristics, i.e., water chemistry variables, at each study site.
Table 1. Summary of environmental characteristics, i.e., water chemistry variables, at each study site.
Factors Site 1Site 2
Water temperature°C9.0110.84
Electrical ConductivityμS/cm173614,382
Salinity‰0.888.36
Dissolved Oxygen%101.0122.7
Dissolved Oxygenmg/L11.6012.88
pH 7.707.97
Table
Table 2. List of the operational taxonomic units (OTUs) at the phylum level in the PF content (n = 20, 10 individuals from each study site) and gut content (n = 20, 10 individuals from each study site) of C. fluminea and water (n = 2, one sample from each study site) based on the 18S V9 region.
Table 2. List of the operational taxonomic units (OTUs) at the phylum level in the PF content (n = 20, 10 individuals from each study site) and gut content (n = 20, 10 individuals from each study site) of C. fluminea and water (n = 2, one sample from each study site) based on the 18S V9 region.
PhylumGenus + SpeciesPseudofecesGutWaterTotalIdentityQueryAccess ID
Site 1Site 2TotalSite 1Site 2TotalSite 1Site 2Total
BacillariophytaNavicula arenaria55116600010167100100KJ961668.1
CiliophoraDiophrys appendiculata210210000002188.27100JF694041.1
AscomycotaAspergillus penicillioides29110000001199.43100NG_063229.1
ArthropodaClausidium vancouverense770770000007794.83100JF781553.1
ArthropodaHaplochthonius simplex865730000007399.3683EU675634.1
ArthropodaHydrachnidae sp.213000000394.41100MT921251.1
CercozoaNeocercomonas sp.3434680000116992.7100MG775615.1
ㅡAzadinium poporum808101132013214194.83100LS974157.1
ArthropodaMicrocyclops varicans248124900000024993.18100MK106114.1
BacillariophytaNavicula sp.267330001013497.7100KJ961665.1
ChlorophytaDesmodesmus communis130130000001399.43100KF864475.1
ChlorophytaMychonastes rotundus101000000198.85100GQ477053.1
ChordataHemibarbus labeo335380110003997.24100MH843153.1
BacillariophytaConticribra weissflogii111120000001297.13100EF585582.1
BacillariophytaMelosira varians303000112597.7100X85402.2
BasidiomycotaTrichosporon asahii311140550001999.43100MN268783.1
ArthropodaLiposcelis sp.16700000079681AY077779.1
ChordataOncorhynchus mykiss167510150002299.44100XR_005038417.1
ㅡTeleaulax acuta202000043434510095HM126531.1
PlatyhelminthesAlloglossidium fonti101112011496.13100MH041398.1
ImbricateaProtaspis grandis088000000898.29100DQ303924.1
CiliophoraMetopus contortus011110000001198.0398KY432957.1
ArthropodaCyclops sp.011101000297.73100AY626998.1
ArthropodaMytilicola orientalis011011000298.8296HM775190.1
ㅡHeterocapsa niei000101141424395.93100KU900227.1
ㅡKatablepharis japonica0000118010909197.19100LT993783.1
ㅡNannochloropsis oculata000000101199.4100KU900229.1
ㅡAmoebophrya sp.000000178810090MK368243.1
ChlorophytaOstreococcus sp.000000886949499.43100JN862917.1
Resd of OTUs520208728919282261904161172
Number of OTUs201624569981229
Table
Table 3. Zooplankton composition(ind./L) of the PF content of C. fluminea from the sampling sites (n = 20, 10 individuals from each study site).
Table 3. Zooplankton composition(ind./L) of the PF content of C. fluminea from the sampling sites (n = 20, 10 individuals from each study site).
TaxonomyGenus + SpeciesPFGut
Site1Site2TotalSite1Site2Total
Protozoa-022101
RotiferaBrachionus sp.000145
RotiferaCollotheca sp.000011
RotiferaConochilus sp.01131316
RotiferaFilinia sp.000101
RotiferaHexarthra sp.000011
RotiferaKeratella sp.0004812
RotiferaLecane sp.000011
RotiferaLepadella sp.000022
RotiferaMonostyla sp.000123
RotiferaPloesoma sp.000022
RotiferaPolyarthra sp.000011
Rotifera-000639
Cladocera-000202
CopepodaCalanoida 000101
CopepodaCopepoda000000
CopepodaCyclops101000
CopepodaCopepodite101000
CopepodaNauplius000303
Copepoda-101000
Zooplankton-000011
Number of species235101217
Number of individual (ind./L)246233962
Table
Table 4. Community index (Simpson’s index and Shannon index) of the two sites.
Table 4. Community index (Simpson’s index and Shannon index) of the two sites.
Simpson’s IndexShannon Index
Site 1Site 2Site 1Site 2
PF Content0.7290.8331.8352.134
Gut Content0.6420.6431.3031.309
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Heo, Y.-J.; Jo, H.; Kim, J.Y.; Kim, G.-Y.; Joo, G.-J.; Kim, H.-W. Application of DNA Metabarcoding for Identifying the Diet of Asian Clam (Corbicula fluminea, Müller, 1774). Sustainability 2023, 15, 441. https://doi.org/10.3390/su15010441

AMA Style

Heo Y-J, Jo H, Kim JY, Kim G-Y, Joo G-J, Kim H-W. Application of DNA Metabarcoding for Identifying the Diet of Asian Clam (Corbicula fluminea, Müller, 1774). Sustainability. 2023; 15(1):441. https://doi.org/10.3390/su15010441

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Heo, Yu-Ji, Hyunbin Jo, Ji Yoon Kim, Gu-Yeon Kim, Gea-Jae Joo, and Hyun-Woo Kim. 2023. "Application of DNA Metabarcoding for Identifying the Diet of Asian Clam (Corbicula fluminea, Müller, 1774)" Sustainability 15, no. 1: 441. https://doi.org/10.3390/su15010441

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