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Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA

Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA applied sciences Article Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA 1 1 1 1 , 2 , Hyunbin Jo , Dong-Kyun Kim , Kiyun Park and Ihn-Sil Kwak * Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea Faculty of Marine Technology, Chonnam National University, Yeosu 59626, Korea * Correspondence: inkwak@hotmail.com; Tel.: +82-61-659-7148 Received: 4 July 2019; Accepted: 15 August 2019; Published: 21 August 2019 Featured Application: Complement monitoring tool. Abstract: The nonlinearity and complexity of coastal ecosystems often cause diculties when analyzing spatial and temporal patterns of ecological traits. Environmental DNA (eDNA) monitoring has provided an alternative to overcoming the aforementioned issues associated with classical monitoring. We determined aquatic community taxonomic composition using eDNA based on a meta-barcoding approach that characterizes the general ecological features in the Gwangyang Bay coastal ecosystem. We selected the V9 region of the 18S rDNA gene (18S V9), primarily because of its broad range among eukaryotes. Our results produced more detailed spatial patterns in the study area previously categorized (inner bay, main channel of the bay and outer bay) by Kim et al. (2019). Specifically, the outer bay zone was clearly identified by CCA using genus-level identification of aquatic organisms based on meta-barcoding data. We also found significant relationships between environmental factors. Therefore, eDNA monitoring based on meta-barcoding approach holds great potential as a complemental monitoring tool to identify spatial taxonomic distribution patterns in coastal areas. Keywords: coastal ecosystem; eDNA; spatial patterns; complemental monitoring tool 1. Introduction Biological monitoring contributes to the understanding of complex ecosystem structures and functions in targeted systems [1]. Accordingly, it is crucial in detecting and assessing environmental changes in order to ensure proper management and conservation of complex ecosystems [2]. Coastal environments are among the most complex ecosystems due to tidal activity, and typically retain high economic and environmental values in light of aquatic resources and biodiversity [3]. Coastal ecosystems are often severely a ected by anthropogenic activities such as industrial fishing [4,5], marine transport and leisure activities [6], aquaculture and the aquarium trade [7,8], living seafood and lure fisheries [9,10], and non-indigenous species (NIS) which induce greater pressures on endemic ecosystems and often drive native species to extinction in the resulting habitats [11–13]. The nonlinearity and complexity of coastal ecosystems due to the aforementioned activities often causes diculties when analyzing spatial and temporal patterns of ecological traits. Environmental DNA (eDNA) as described by Ogram et al. [14] who extracted microbial DNA from the sediment, has increasingly been used in recent years for biological monitoring purposes. Recently, a large number of papers have reported the use of eDNA monitoring in analyses of soil, water and even air [15]. Andersen et al. [16] examined the possibility of monitoring large mammals using eDNA in soil samples, and eDNA from water monitoring of fish [17–20] and amphibians [21,22] has been successful. Furthermore, Hawkins et al. [23] demonstrated that a complete taxonomic list of functional feeding Appl. Sci. 2019, 9, 3450; doi:10.3390/app9173450 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3450 2 of 12 group (FFG) criteria, based on high resolution of identification (genus or species level) based on DNA techniques, can determine the e ects of watershed alterations on stream invertebrate assemblages in bulk eDNA samples. However, family level identification based on visual inspection did not reveal any di erences of FFG composition between sites. Next-generation sequencing (NGS) technologies for eDNA monitoring have provided an alternative to overcome issues such as identification problems associated with classical monitoring in a species rich coastal environment [3,20]. The values of coastal ecosystems, such as primary production (i.e., sea grass and algae) and commercial fish yields, are intertwined with multiple environmental factors, including nutrient concentrations (carbon, nitrogen, and phosphorus), phytoplankton growth, zooplankton grazing e ects, and benthic communities. The Gwangyang Bay coastal ecosystem is the most economically productive in Korean peninsula. Specifically, in the midst of three major cities (Gwangyang, Yeosu and Suncheon) in Jeonnam Province, with Gwangyang Bay, it yields 71% (1,297,815 tons) of the annual aquacultural resource output as of 2016 [24]. However, there is a large industrial area near the Bay, and the area is primarily involved in industrial activities such as oil refineries and steel production plants. Kim et al. [25] characterized the dissimilarity of water quality and sediment contamination, and identified the importance of nutrients supplied by rivers. Such findings are, however, still limited to representing general ecological features of the Gwangyang Bay coastal ecosystem. The main objective of this study was to determine aquatic community taxonomic composition using eDNA based on an NGS approach for characterizing general ecological features in the Gwangyang Bay coastal ecosystem. We analyzed the community spatial distribution with regard to environmental parameters, and the habitat types (marine, freshwater and estuarine), feeding habits (filter feeder, carnivore, producer and symbiotic) and indigenous species rate (ISR) among the three di erent zones referred to by Kim et al. [25]. Moreover, we discuss the e ectiveness and sensitivity of our NGS approach on the Gwangyang Bay coastal ecosystem. 2. Materials and Methods 2.1. Study Area Gwangyang Bay is part of the Korean National Archipelagos located o the south coast of the 6 3 1 Korean peninsula (Figure 1). The bay receives an annual mean discharge of 2298 10 m yr from the Seomjin River [26]. A significant amount of nutrients drains into the system from the watershed (~5000 km ). The water depth varies from 10 m at the Seomjin River estuary to 50 m at the outer Gwangyang Bay. The tidal cycle appears to be semi-diurnal. Compared to other Korean river estuaries which have barriers, the Seomjin River estuary remains open, and thus the water mass is exchanged between the river and ocean more actively. The natural condition of Gwangyang Bay is apt to increase primary productivity as well as biological diversity. In this respect, Gwangyang Bay (~450 km from the estuary to the outer bay) is the most economically productive coastal ecosystem in the Korean peninsula [25]. 2.2. Sampling, Data Collection and Primer Selection A survey permitted by Ha-dong local government (permission number: 2010-0165) was conducted in June 2018. We sampled the surface water (approximately the top 50 cm) in this study. In total, nineteen sampling sites were selected, which covered an extensive area from the Seomjin River estuary to the outer Gwangyang Bay (Figure 1). According to Kim et al. [25], at Gwangyang Bay, higher water temperatures corresponded to lower salinity, and vice versa. Phosphorus and nitrogen concentrations were spatially similar across the Bay. Based on the divisions indicated in Kim et al. [25], Zone I covered the inner Bay (sites 3–7, and 9), Zone II represented the main channel of the Bay (sites 8, 10, and 11), and Zone III mostly belonged to the outer Bay (sites 12–21) (Figure 1). Water samples for meta-barcoding analysis (more than 1 L per sample) were obtained at the same time and moved with dry ice to laboratory to filter (0.45 m pore-size membrane; Advantec MFS membrane filter, Dublin, Appl. Sci. 2019, 9, x FOR PEER REVIEW 3 of 12 every study site to prevent cross contamination. Water temperature and salinity were measured on- site using portable equipment (Model: YSI Professional Plus, OH, USA), while the nutrient and Appl. Sci. 2019, 9, 3450 3 of 12 chlorophyll-a concentrations were analyzed in the lab. Phosphorus and nitrogen concentrations were measured using an UV spectrophotometer based on standard analytical methods proposed by the USA) and stored at 80 C before NGS analysis. Negative controls were included for every study Korean Ministry of Oceans and Fisheries. Chlorophyll-a measurements were also based on UV site to prevent cross contamination. Water temperature and salinity were measured on-site using spectrophotometry. In contrast to nutrient measurements, chlorophyll-a samples were filtered portable equipment (Model: YSI Professional Plus, OH, USA), while the nutrient and chlorophyll-a through a 0.45 μm pore-size membrane (Model: Advantec MFS membrane filter). The filter concentrations were analyzed in the lab. Phosphorus and nitrogen concentrations were measured using membrane was then homogenized after acetone extraction prior to spectrophotometry. Organic and an UV spectrophotometer based on standard analytical methods proposed by the Korean Ministry inorganic carbon concentrations were measured using a carbon analyzer (Model: vario TOC cub, of Oceans and Fisheries. Chlorophyll-a measurements were also based on UV spectrophotometry. Langenselbold, Germany) using 850 °C combustion catalytic oxidation methods. In contrast to nutrient measurements, chlorophyll-a samples were filtered through a 0.45 m pore-size We selected the V9 region of the 18S rDNA gene (18S V9), primarily because of its broad range membrane (Model: Advantec MFS membrane filter). The filter membrane was then homogenized among eukaryotes [27,28]. NGS approaches using the 18S V9 region have recently allowed the after acetone extraction prior to spectrophotometry. Organic and inorganic carbon concentrations were characterization of marine planktonic biodiversity in the oceans [29] and prompted biomarker measured using a carbon analyzer (Model: vario TOC cub, Langenselbold, Germany) using 850 C establishment initiatives [30]. combustion catalytic oxidation methods. Figure 1. Map of the study sites with division of zones referred to by Kim et al. (2019) [25]. Figure 1. Map of the study sites with division of zones referred to by Kim et al. (2019) [25]. We selected the V9 region of the 18S rDNA gene (18S V9), primarily because of its broad 2.3. DNA Extraction and Metagenomic Sequencing range among eukaryotes [27,28]. NGS approaches using the 18S V9 region have recently allowed Genomic DNA was extracted using PowerSoil DNA Isolation Kit (Cat. No. 12888, Qiagen, the characterization of marine planktonic biodiversity in the oceans [29] and prompted biomarker Düsseldorf, Germany) according to the manufacturer’s protocol. DNA extracted for sequencing was establishment initiatives [30]. prepared according to Illumina 18S Metagenomic Sequencing Library protocols (San Diego, CA, USA). DNA quantity, quality, and integrity were measured by PicoGreen (Thermo Fisher Scientific, 2.3. DNA Extraction and Metagenomic Sequencing Waltham, MA, USA) and VICTOR Nivo Multimode Microplate Reader (PerkinElmer, Akron, OH, Genomic DNA was extracted using PowerSoil DNA Isolation Kit (Cat. No. 12888, Qiagen, USA). The 18S rRNA gene was amplified using 18S V9 primers. The primer sequences are as follows: Düsseldorf, Germany) according to the manufacturer ’s protocol. DNA extracted for sequencing 18S V9 primer including adaptor sequence (Forward Primer: 5′ was prepared according to Illumina 18S Metagenomic Sequencing Library protocols (San Diego, CA, TCGTCGGCAGCGTCAGATGTGTATAA GAGACAGCCCTGCCHTTTGTACACAC 3′/Reverse USA). DNA quantity, quality, and integrity were measured by PicoGreen (Thermo Fisher Scientific, Primer: 5′ GTCTCGTGGGCTCGGAGATG TGTATAAGAGACAGCCTTCYGCAGGTTCACCTAC Waltham, MA, USA) and VICTOR Nivo Multimode Microplate Reader (PerkinElmer, Akron, OH, USA). 3′). To amplify the target region attached with adapters, as a first PCR process, the extracted DNA The 18S rRNA gene was amplified using 18S V9 primers. The primer sequences are as follows: 18S was amplified by 18S V9 primers with one cycle of 3 min at 95 °C, 25 cycles of 30 s at 95 °C, 30 s at 55 V9 primer including adaptor sequence (Forward Primer: 5 TCGTCGGCAGCGTCAGATGTGTATAA °C, 30 s at 72 °C, and a final step of 5 min at 72 °C for amplicon PCR product. As a second process, to 0 0 GAGACAGCCCTGCCHTTTGTACACAC 3 /Reverse Primer: 5 GTCTCGTGGGCTCGGAGATG produce indexing PCR, the first PCR product was subsequently amplified with one cycle of 3 min at TGTATAAGAGACAGCCTTCYGCAGGTTCACCTAC 3 ). To amplify the target region attached with adapters, as a first PCR process, the extracted DNA was amplified by 18S V9 primers with one cycle of 3 min at 95 C, 25 cycles of 30 s at 95 C, 30 s at 55 C, 30 s at 72 C, and a final step of 5 min at 72 C for Appl. Sci. 2019, 9, 3450 4 of 12 amplicon PCR product. As a second process, to produce indexing PCR, the first PCR product was subsequently amplified with one cycle of 3 min at 95 C, eight cycles of 30 s at 95 C, 30 s at 55 C, 30 s at 72 C, and a final step of 5 min at 72 C. The final products were normalized and pooled using PicoGreen (Thermo Fisher Scientific, Waltham, MA, USA), and the size of libraries were verified using the LabChip GX HT DNA High Sensitivity Kit (PerkinElmer, Akron, OH, USA). The library was sequenced using the MiSeq— NGS platform (Illumina, San Diego, CA, USA) provided as a commercial service (Macrogen Inc., Seoul, Korea). Raw reads were trimmed with CD-HIT-OTU 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. Merged reads were processed using Qiime version 1.9 [31] and were clustered into operational taxonomic units (OTUs) with UCLUST [32], using a greedy algorithm with OTUs at a 97% OUT cuto value. Taxonomic classifications were assigned to the obtained representative sequences using BLASTn [33] and UCLUST [32]. 2.4. Data Analysis and Statistics OTUs assigned by meta-barcoding were classified into habitat types (marine, freshwater and estuarine), trophic level (first consumer [filter feeder], second consumer [carnivore], producer and symbiotic) and ISR (indigenous, non-indigenous). Canonical correspondence analysis (CCA) of OTUs exhibiting >1% relative abundance was performed using the PAST 3.0 program [34] to evaluate relationships between abundance of OTU sequences (based on genus level identification) and environmental factors (temperature (Temp.), salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)). All CCA results were constructed using relative abundance data, with natural logarithms transformation (ln 1 + X) used for sample normalization. Linear relationships (Pearson correlations) were calculated between the above environmental factors and classified OTU sequences, based on habitat using XLSTAT version 2018.6.54467 (64 bit) as a plug-in for the Microsoft Excel program [35]. 3. Results 3.1. Meta-Barcoding In total, 3,066,013 paired-end reads from the 19 samples were generated on the Illumina MiSeq— platform, of which 98.5% passed Q30 (Phred quality score > 30) for improving accuracy of sequences in this study (Supplementary Materials). Each sample yielded paired-end reads ranging from 21,101–299,305 reads (mean: 161,369 reads), which was similar to the amount of reads in the previous study [36], and all samples exhibited saturation of the number of OTUs by rarefaction curve analysis. Gamma-diversity was 352 OTUs produced with a cuto of 97% similarity. The resulting 352 OTUs were classified into 19 genus-level taxonomic groups (those representing <0.04% abundance were not plotted). Uncultured and non-assigned reads were discarded. 3.2. Spatial Distributions of Aquatic Organisms Based on Meta-Barcoding We carried out a survey in June 2018. Relationships among the environmental variables and aquatic organisms based on meta-barcoding were explored by CCA (Figure 2). The first axis explained 33.3% of the variance and distinguished Zones III-2 and III-3 with higher salinity, EC, TIC, and lower Chl-a and TN from the other zones. The second axis (19.6% of variance explained) distinguished the sites from Zone III-1 and the other zones, by having higher TOC and TC, lower temperature, and TP. Nutrient-related factors (Chl-a, TN, and TP) were correlated with Acropora sp. (small polyp stony coral) and Megabalanus sp., (barnacle) and salinity was correlated with Skeletonema sp. (diatoms) and Centropages sp. (copepods). Appl. Sci. 2019, 9, 3450 5 of 12 Appl. Sci. 2019, 9, x FOR PEER REVIEW 5 of 12 Figure 2. Canonical correspondence analysis (CCA) used to evaluate (A) the relationships between Figure 2. Canonical correspondence analysis (CCA) used to evaluate (A) the relationships between abundance of operational taxonomic units (OTUs) sequences (based on genus level identification) and abundance of operational taxonomic units (OTUs) sequences (based on genus level identification) environmental factors (temperature (Temp.), Salinity, total phosphorus (TP), total nitrogen (TN), and environmental factors (temperature (Temp.), Salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)). (B) Sites grouping based on CCA analysis. elemental carbon (EC)). (B) Sites grouping based on CCA analysis. Appl. Sci. 2019, 9, 3450 6 of 12 Appl. Sci. 2019, 9, x FOR PEER REVIEW 6 of 12 Abundance of assigned OTU sequences showed di erent patterns among the study sites (Figure 3). Abundance of assigned OTU sequences showed different patterns among the study sites (Figure The dominant OTU was assigned to Acropora sp. and the sub-dominant OTU was Acartia sp. (copepods). 3). The dominant OTU was assigned to Acropora sp. and the sub-dominant OTU was Acartia sp. Most common OTUs within the study sites were comprised of phytoplankton, followed by zooplankton (copepods). Most common OTUs within the study sites were comprised of phytoplankton, followed and amphipods (Figure 3B). Interestingly, the abundance of OTUs showed di erent compositions by zooplankton and amphipods (Figure 3B). Interestingly, the abundance of OTUs showed different among the three di erent zones (I, II, III). Zone I showed similar patterns to Zones II and III-1, whereas compositions among the three different zones (I, II, III). Zone I showed similar patterns to Zones II Zones III-2 and 3 showed di erent compositions of aquatic organisms which distinguished them from and III-1, whereas Zones III-2 and 3 showed different compositions of aquatic organisms which other zones. In particular, Zone III-3 exhibited an entirely di erent pattern, comprised of marine distinguished them from other zones. In particular, Zone III-3 exhibited an entirely different pattern, organisms such as Euchaeta sp. (copepods) and Tessarabrachion sp. (krill) compared with other zone comprised of marine organisms such as Euchaeta sp. (copepods) and Tessarabrachion sp. (krill) divisions (Figure 3B). compared with other zone divisions (Figure 3B). Figure 3. Abundance of OTU sequences along the study sites (A) with site description of physical Figure 3. Abundance of OTU sequences along the study sites (A) with site description of physical features and zone classification (B) based on genus identification level. features and zone classification (B) based on genus identification level. 3.3. Functional Features and Non-Indigenous Species 3.3. Functional Features and Non-Indigenous Species When divided into categories (habitat types, trophic level, and ISR), averages of abundant When divided into categories (habitat types, trophic level, and ISR), averages of abundant sequences showed di erent patterns among the zones (I, II, III-1-2-3). The most dominant habitat type sequences showed different patterns among the zones (I, II, III-1-2-3). The most dominant habitat of OTU was the marine type for the three di erent zones (Figure 4A), while the types of feeding habit type of OTU was the marine type for the three different zones (Figure 4A), while the types of feeding and indigenous rate showed complex response patterns. The dominant trophic level was first consumer habit and indigenous rate showed complex response patterns. The dominant trophic level was first and second consumer, followed by producer and symbiotic trophic levels (Figure 4B). The indigenous consumer and second consumer, followed by producer and symbiotic trophic levels (Figure 4B). The rate of Zone III-3 was significantly higher than other zones, but Zones II and III-1 showed opposite indigenous rate of Zone III-3 was significantly higher than other zones, but Zones II and III-1 showed patterns opposite patt (Figureerns 4C),(Figure 4C), wh while Zones I and ile III-2 Zones I presented and III-2 presen no significant ted no sign di erence ificin ant difference in indigenous rates. The indigenous rates. The three division three divisions of Zone III from CCA s of Zone resultsIII from CCA results demon demonstrated similar patterns strated similar within the patterns three zone within the three zone types (Zones I, II-and III). types (Zones I, II-and III). We found significant relationships between environmental factors (Table 1). Specifically, We found significant relationships between environmental factors (Table 1). Specifically, temperature temperature had positive relationships with nutrient-related factors such as TP and PN, and showed had positive relationships with nutrient-related factors such as TP and PN, and showed a negative a negative relationship with TOC. Salinity showed significant negative relationships with TN and relationship with TOC. Salinity showed significant negative relationships with TN and TP, but also TP, but also displayed a positive relationship with carbon-related factors such as EC and TC. displayed a positive relationship with carbon-related factors such as EC and TC. However, there were no However, there were no significant relationships between environmental factors and divisions of significant relationships between environmental factors and divisions of categories except for habitat categories except for habitat types (marine, estuarine, freshwater). The marine habitat of OTUs types (marine, estuarine, freshwater). The marine habitat of OTUs revealed a significant positive revealed a significant positive relationship with salinity (r = 0.879) and EC (r = 0.878), respectively, relationship with salinity (r = 0.879) and EC (r = 0.878), respectively, whereas freshwater and estuarine whereas freshwater and estuarine types of OTUs showed negative relationships with salinity (r = types of OTUs showed negative relationships with salinity (r =0.888 and0.856) and EC (r =0.884 −0.888 and −0.856) and EC (r = −0.884 and −0.856) respectively. and0.856) respectively. Appl. Sci. 2019, 9, 3450 7 of 12 Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 12 Figure 4. Comparison of the habitat types ((A), marine-freshwater-estuarine), trophic level ((B), consumer 1 Figure 4. Comparison of the habitat types ((A), marine-freshwater-estuarine), trophic level ((B), [filter feeder], consumer 2 [carnivore], producer and symbiotic) and natives ((C), non-indigenous and consumer 1 [filter feeder], consumer 2 [carnivore], producer and symbiotic) and natives ((C), non- indigenous) among five zoning types based on genus identification level. indigenous and indigenous) among five zoning types based on genus identification level. Appl. Sci. 2019, 9, 3450 8 of 12 Table 1. Linear relationship (Pearson correlation) between environmental factors (temperature (Temp.), Salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)) and classified OTU sequences based on habitat types, feeding habits and natives (Bold: significant relationship). Variables Temp Salinity EC TP TN TOC TIC TC Chla Temp 1 0.192 0.216 0.505 0.464 0.599 0.275 0.220 0.422 Salinity 0.192 1 0.996 0.586 0.532 0.301 0.435 0.486 0.006 EC 0.216 0.996 1 0.615 0.564 0.325 0.440 0.478 0.034 TP 0.505 0.586 0.615 1 0.904 0.505 0.634 0.715 0.192 TN 0.464 0.532 0.564 0.904 1 0.424 0.680 0.645 0.200 TOC 0.599 0.301 0.325 0.505 0.424 1 0.127 0.355 0.248 TIC 0.275 0.435 0.440 0.634 0.680 0.127 1 0.877 0.195 TC 0.220 0.486 0.478 0.715 0.645 0.355 0.877 1 0.263 Chla 0.422 0.006 0.034 0.192 0.200 0.248 0.195 0.263 1 Marine 0.197 0.879 0.878 0.343 0.195 0.314 0.051 0.151 0.082 Fresh 0.123 0.888 0.884 0.332 0.199 0.286 0.061 0.175 0.024 Estuarine 0.269 0.856 0.856 0.349 0.188 0.337 0.041 0.123 0.139 First consumer 0.195 0.283 0.271 0.164 0.120 0.152 0.329 0.269 0.159 Second consumer 0.011 0.387 0.373 0.329 0.070 0.159 0.239 0.346 0.026 Producer 0.054 0.319 0.307 0.288 0.040 0.194 0.162 0.281 0.064 Symbiotic 0.032 0.335 0.326 0.266 0.182 0.158 0.298 0.301 0.220 Non-indigenous 0.002 0.348 0.331 0.284 0.021 0.137 0.195 0.295 0.008 Indigenous 0.002 0.348 0.331 0.284 0.021 0.137 0.195 0.295 0.008 4. Discussion 4.1. E ectiveness of eDNA Monitoring Our results showed that eDNA monitoring based on NGS holds great potential as a complementary monitoring tool to identify spatial taxonomic distribution patterns in coastal areas. We characterized detailed zonation patterns of the outer bay of Gwangyang Bay previously categorized by Kim et al. [25]. Specifically, Zones III-2 and III-3 were clearly resolved by CCA using genus-level identification of aquatic organisms based on meta-barcoding data (Figure 2). When divided into categories (habitat types, trophic level, and ISR), averages of abundant sequences showed di erent patterns among the zones (I, II, III-1-2-3). The NIS detection of Zone III-3 was significantly higher than the other zones. Therefore, eDNA based on meta-barcoding is a promising ecological tool for monitoring, such as biodiversity assessment or NIS management [24,37–40]. This approach by meta-barcoding in complex coastal ecosystems confers three distinct advantages over other methods. First, the range of aquatic organisms in coastal ecosystems open to study is widened. Second, identification of aquatic organism to the species or genus level (Appendix A), which can be converted into ecological values such as FFC and NIS, is possible. Finally, sensitivity of the meta-barcoding approach using small volumes of water (1 L) is a promising alternative to traditional methods (i.e., dredges and surber samplers) for biodiversity detection in coastal areas. Using these advantages, eDNA monitoring can provide a useful tool for use in environmental management. However, some taxa were unable to be identified to the species or genus level due to the incompleteness of reference databases (i.e., NCBI GenBank). More accurate target regions such as the cytochrome oxidase I region, which is called the standard region of barcoding, are needed to identify target species at the species level and are required for identification of aquatic organisms [41]. Another challenge associated with eDNA monitoring is the risk of false-positive and false-negative detections [42]. The reliability of the eDNA sampling method should be demonstrated using in silico, in vitro and in situ validation tests in the coastal area. Even though this approach has limitations, taxonomic expertise is not required and it can supplement observational records and field surveys to obtain marine ecosystem samples and information, as NGS reveals biodiversity and the number of NIS in one specific area along the their temporal and spatial distribution. Appl. Sci. 2019, 9, 3450 9 of 12 4.2. Ecological Values of eDNA Monitoring Our results showed that Acropora sp. was dominant in terms of abundance of OTU sequences across all study sites, even though it is not a planktonic species. The Acropora colonies post-recruitment by larval recruitment demonstrates higher eciency for survival than coral-colony growth [43]. It was concluded that larval recruitment largely determines species composition, and that reduced larval recruitment is responsible for the sparse distribution of fragmenting species [44]. Therefore, the larval stage could be easily detected among the study sites by our eDNA monitoring approach. We also found that nutrient-related factors (Chl-a, TN, and TP) were significantly correlated with Acropora sp. and Megabalanus sp. (Figure 2). It is therefore possible that the strong positive correlation between the two species could be the result of coral-inhabiting barnacles [45]. The comprehensive understanding of feeding characteristics of aquatic organisms has not been well elucidated in comparison to their importance [46–49]. Our results showed that composition of aquatic organisms have di erent patterns of feeding habits among the three di erent zone divisions designated by Kim et al. [25] (Figure 4), and we also found similar patterns using CCA based on genus-level identification resulting from our division of Zone III (Figure 2). However, previous research findings were attributed to the absence of adequate information for analysis obtained from the field sites due to diculties in culture, handling, and identification of eDNA samples derived from bulk sediment and filtered water. In this sense, our results overcame the aforementioned limitations by using a broader detection range for aquatic organisms. Although, Zhan et al. [50] has described the increased sensitivity of meta-barcoding for NIS, its application for monitoring biological invasion has only recently been demonstrated [51]. In the present work, we identified the utility of meta-barcoding for detection of NIS and their spatial distribution patterns (Figure 4). The reason behind this is the capacity to detect the presence of individuals at early life stages, such as eggs or nauplius larvae, whose identification is difficult with traditional methods [52]. The sensitivity of meta-barcoding, combined with the relatively low time and cost associated with this technique [53], makes it a promising alternative approach for the rapid and accurate detection of biodiversity shifts in aquatic organisms, allowing its potential implementation in environmental policies. Supplementary Materials: DNA sequences: DRYAD entry https://datadryad.org/review?doi=doi:10.5061/dryad. 41b1dp3. Author Contributions: Conceptualization, H.J., D.-K.K. and I.-S.K.; methodology, H.J. and K.P.; formal analysis, H.J. and D.-K.K.; investigation, H.J. and D.-K.K.; writing—original draft preparation, H.J. and I.-S.K.; writing—review and editing, D.-K.K. and I.-S.K.; project administration, I.-S.K.; funding acquisition, I.-S.K. Funding: This research was funded by the National Research Foundation of Korea, grant number NRF-2018R1A6A1A03024314. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Species Level Identification List Used in this Paper. Kingdom Species Eukaryota Acropora granulosa Eukaryota Acartia omorii Eukaryota Tessarabrachion oculatum Eukaryota Megabalanus stultus Eukaryota Euchaeta indica Eukaryota Bougainvillia muscus Eukaryota Centropages typicus Eukaryota Skeletonema costatum Eukaryota Monstrilla sp. Eukaryota Fibrocapsa japonica Eukaryota Biecheleria brevisulcata Eukaryota Paulsenella vonstoschii Appl. Sci. 2019, 9, 3450 10 of 12 Kingdom Species Eukaryota Cavernomonas mira Eukaryota Caprella californica Eukaryota Biddulphia sp. Eukaryota Candacia bispinosa Eukaryota Thalassiosira mala Eukaryota Akashiwo sanguinea Eukaryota Conticribra weissflogiopsis References 1. Jo, H.; Ventura, M.; Vidal, N.; Gim, J.S.; Buchaca, T.; Barmuta, L.A.; Erik, J.; Joo, G.J. Discovering hidden biodiversity: The use of complementary monitoring of fish diet based on DNA barcoding in freshwater ecosystems. Ecol. Evol. 2016, 6, 219–232. [CrossRef] [PubMed] 2. Robertson, G.P.; Collins, S.L.; Foster, D.R.; Brokaw, N.; Ducklow, H.W.; Gragson, T.L.; Gries, C.; Hamilton, S.K.; McGuire, A.D.; Moore, J.C.; et al. Long-term ecological research in a human-dominated world. BioScience 2006, 62, 342–353. [CrossRef] 3. Baird, D.J.; Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 2012, 21, 2039–2044. [CrossRef] [PubMed] 4. Myers, R.A.; Worm, B. Rapid worldwide depletion of predatory fish communities. Nature 2003, 423, 280. [CrossRef] [PubMed] 5. Pusceddu, A.; Bianchelli, S.; Martín, J.; Puig, P.; Palanques, A.; Masqué, P.; Danovaro, R. Chronic and intensive bottom trawling impairs deep-sea biodiversity and ecosystem functioning. Proc. Natl. Acad. Sci. USA 2014, 111, 8861–8866. [CrossRef] [PubMed] 6. Ruiz, G.M.; Fofono , P.W.; Carlton, J.T.; Wonham, M.J.; Hines, A.H. Invasion of coastal marine communities in North America: Apparent patterns, processes, and biases. Annu. Rev. Ecol. Syst. 2000, 31, 481–531. [CrossRef] 7. Naylor, R.; Williams, S.L.; Strong, D.R. Aquaculture—A gateway for exotic species. Science 2001, 294, 1655–1656. [CrossRef] 8. Padilla, D.K.; Williams, S.L. Beyond ballast water: Aquarium and ornamental trades as sources of invasive species in aquatic ecosystems. Front. Ecol. Environ. 2004, 2, 131–138. [CrossRef] 9. Chapman, J.W.; Miller, T.W.; Coan, E.V. Live seafood species as recipes for invasion. Conserv. Biol. 2003, 17, 1386–1395. [CrossRef] 10. Weigel, S.; Bester, K.; Hühnerfuss, H. Identification and quantification of pesticides, industrial chemicals, and organobromine compounds of medium to high polarity in the North Sea. Mar. Pollut. Bull. 2005, 50, 252–263. [CrossRef] 11. Ardura, A.; Planes, S. Rapid assessment of non-indigenous species in the era of the eDNA barcoding: A Mediterranean case study. Estuar. Coast. Shelf Sci. 2017, 188, 81–87. [CrossRef] 12. Carlton, J.T.; Geller, J.B. Ecological roulette: The global transport of nonindigenous marine organisms. Science 1993, 261, 78–82. [CrossRef] 13. Williams, S.L.; Grosholz, E.D. The invasive species challenge in estuarine and coastal environments: Marrying management and science. Estuaries Coasts 2008, 31, 3–20. [CrossRef] 14. Ogram, A.; Sayler, G.S.; Barkay, T. The extraction and purification of microbial DNA from sediments. J. Microbiol. Meth. 1987, 7, 57–66. [CrossRef] 15. Taberlet, P.; Coissac, E.; Hajibabaei, M.; Rieseberg, L.H. Environmental DNA. Mol. Ecol. 2012, 21, 1789–1793. [CrossRef] 16. Andersen, K.; Bird, K.L.; Rasmussen, M.; Haile, J.; Breuning-Medsen, H.; Kjaer, K.H.; Orlando, L.; Geilbert, M.T.P.; Willerslev, E. Meta-barcoding of ‘dirt’ DNA from soil reflects vertebrate biodiversity. Mol. Ecol. 2011, 21, 1966–1979. [CrossRef] 17. Minamoto, T.; Yamanaka, H.; Takahara, T.; Honjo, M.N.; Kawabata, Z. Surveillance of fish species composition using environmental DNA. Limnology 2012, 13, 193–197. [CrossRef] Appl. Sci. 2019, 9, 3450 11 of 12 18. Thomsen, P.; Kielgast, J.; Iversen, L.L.; Wiuf, C.; Rasmussen, M.; Gilbert, M.T.P.; Orlando, L.; Willerslev, E. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 2012, 21, 2565–2573. [CrossRef] 19. Thomsen, P.; Kielgast, J.; Iversen, L.L.; Møller, P.R.; Rasmussen, M.; Willerslev, E. Detection of a diverse marine fish fauna using environmental DNA from seawater samples. PLoS ONE 2012, 7, e41732. [CrossRef] 20. Yamamoto, S.; Masuda, R.; Sato, Y.; Sado, T.; Araki, H.; Kondoh, M.; Toshifumi, M.; Miya, M. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 2017, 7, 40368. [CrossRef] 21. Ficetola, G.F.; Miaud, C.; Pompanon, F.; Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 2008, 4, 423–425. [CrossRef] 22. Goldberg, C.S.; Pilliod, D.S.; Arkle, R.S.; Waits, L.P. Molecular detection of vertebrates in stream water: A demonstration using Rocky Mountain tailed frogs and Idaho giant salamanders. PLoS ONE 2011, 6, e22746. [CrossRef] 23. Hawkins, C.P.; Norris, R.H.; Hogue, J.N.; Feminella, J.W. Development and evaluation of predictive models for measuring the biological integrity of streams. Ecol. Appl. 2000, 10, 1456–1477. [CrossRef] 24. Korean Statistical Information Service (KOSIS). Available online: http://kosis.kr (accessed on 9 June 2019). 25. Kim, D.K.; Jo, H.; Han, I.; Kwak, I.S. Explicit Characterization of Spatial Heterogeneity Based on Water Quality, Sediment Contamination, and Ichthyofauna in a Riverine-to-Coastal Zone. Int. J. Environ. Res. Health 2019, 16, 409. [CrossRef] 26. Kang, C.K.; Kim, J.B.; Lee, K.S.; Kim, J.B.; Lee, P.Y.; Hong, J.S. Trophic importance of benthic microalgae to macrozoobenthos in coastal bay systems in Korea: Dual stable C and N isotope analyses. Mar. Ecol. Prog. Ser. 2003, 259, 79–92. [CrossRef] 27. Abad, D.; Albaina, A.; Aguirre, M.; Laza-Martínez, A.; Uriarte, I.; Iriarte, A.; Arantza, I.; Fernando, V.; Estonba, A. Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy. Mar. Biol. 2016, 163, 149. [CrossRef] 28. de Vargas, C.; Audic, S.; Henry, N.; Decelle, J.; Mahé, F.; Logares, R.; Lara, E.; Berney, C. Eukaryotic plankton diversity in the sunlit ocean. Science 2015, 348, 1261605. [CrossRef] 29. Albaina, A.; Aguirre, M.; Abad, D.; Santos, M.; Estonba, A. 18S rRNA V9 metabarcoding for diet characterization: A critical evaluation with two sympatric zooplanktivorous fish species. Ecol. Evol. 2016, 6, 1809–1824. [CrossRef] 30. Massana, R.; Gober, A.; Audic, S.; Bass, D.; Bittner, L.; Boutte, C.; Chambouvet, A.; Christen, R. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing. Environ. Microbiol. 2015, 17, 4035–4049. [CrossRef] 31. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Huttley, G.A. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335. [CrossRef] 32. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010, 26, 2460–2461. [CrossRef] 33. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [CrossRef] 34. Hammer, Ø.; Harper, D.A.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, 9. 35. Addinsoft. XLSTAT Statistical and Data Analysis Solution; Addinsoft: New York, NY, USA, 2019. 36. Thomsen, P.F.; Møller, P.R.; Sigsgaard, E.E.; Knudsen, S.W.; Jørgensen, O.A.; Willerslev, E. Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes. PLoS ONE 2016, 11, e0165252. [CrossRef] 37. Bourlat, S.J.; Borja, A.; Gilbert, J.; Taylor, M.I.; Davies, N.; Weisberg, S.B.; Glöckner, F.O. Genomics in marine monitoring: New opportunities for assessing marine health status. Mar. Pollut. Bull. 2013, 74, 19–31. [CrossRef] 38. Valentini, A.; Taberlet, P.; Miaud, C.; Civade, R.; Herder, J.; Thomsen, P.F.; Bellemain, E.; Besnard, A.; Coissac, E.; Boyer, F.; et al. Next-generation monitoring of aquatic biodiversity using environmental DNA barcoding. Mol. Ecol. 2016, 25, 929–942. [CrossRef] [PubMed] 39. O’Donnell, J.L.; Kelly, R.P.; Shelton, A.O.; Samhouri, J.F.; Lowell, N.C.; Williams, G.D. Spatial distribution of environmental DNA in a nearshore marine habitat. Peer J. 2017, 5, e3044. [CrossRef] [PubMed] Appl. Sci. 2019, 9, 3450 12 of 12 40. Djurhuus, A.; Pitz, K.; Sawaya, N.A.; Rojas-Márquez, J.; Michaud, B.; Montes, E.; Muller-Karger, F.; Breitbart, M. Evaluation of marine zooplankton community structure through environmental DNA metabarcoding. Limnol. Oceanogr. Methods 2018, 16, 209–221. [CrossRef] 41. Hajibabaei, M.; Smith, M.A.; Janzen, D.H.; Rodriguez, J.J.; Whitfield, J.B.; Hebert, P.D. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 2006, 6, 959–964. [CrossRef] 42. Darling, J.A.; Mahon, A.R. From molecules to management: Adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environ. Res. 2011, 111, 978–988. [CrossRef] 43. Victor, S.; Golbuu, Y.; Yukihira, H.; van Woesik, R. Acropora size-frequency distributions reflect spatially variable conditions on coral reefs of Palau. Bull. Mar. Sci. 2009, 85, 149–157. 44. Wallace, C.C. Reproduction, recruitment and fragmentation in nine sympatric species of the coral genus Acropora. Mar. Biol. 1985, 88, 217–233. [CrossRef] 45. Lewis, J.B. Recruitment, growth and mortality of a coral-inhabiting barnacle Megabalanus stultus (Darwin) upon the hydrocoral Millepora complanata Lamarck. J. Exp. Mar. Biol. Ecol. 1992, 162, 51–64. [CrossRef] 46. Rothhaupt, K. Di erences in particle size-dependent feeding eciencies of closely related rotifer species. Limnol. Oceanogr. 1990, 35, 16–23. [CrossRef] 47. Thouvenot, A.; Debroas, D.; Richardot, M.; Devaux, J. Impact of natural metazooplankton assemblage on planktonic microbial communities in a newly flooded reservoir. J. Plankton Res. 1999, 21, 179–199. [CrossRef] 48. Mohr, S.; Adrian, R. Reproductive success of the rotifer Brachionus calyciflorus feeding on ciliates and flagellates of di erent trophic modes. Freshwater Biol. 2002, 47, 1832–1839. [CrossRef] 49. Devetter, M.; Sed’a, J. Rotifer fecundity in relation to components of microbial food web in a eutrophic reservoir. Hydrobiologia 2003, 504, 167–175. [CrossRef] 50. Zhan, A.; Hulak, M.; Sylvester, F.; Huang, X.; Adebayo, A.A.; Abbott, C.L.; Adamowicz, S.J.; Heath, D.D. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 2013, 4, 558–565. [CrossRef] 51. Zaiko, A.; Samuiloviene, A.; Ardura, A.; Garcia-Vazquez, E. Metabarcoding approach for nonindigenous species surveillance in marine coastal waters. Mar. Pollut. Bull. 2015, 100, 53–59. [CrossRef] 52. Comtet, T.; Sandionigi, A.; Viard, F.; Casiraghi, M. DNA (meta) barcoding of biological invasions: A powerful tool to elucidate invasion processes and help managing aliens. Biol. Invasions 2015, 17, 905–922. [CrossRef] 53. Kelly, R.P.; Port, J.A.; Yamahara, K.M.; Martone, R.G.; Lowell, N.; Thomsen, P.F.; Mach, M.E.; Bennett, M. Environmental monitoring. Harnessing DNA to improve environmental management. Science 2014, 344, 1455–1456. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute

Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA

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applied sciences Article Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA 1 1 1 1 , 2 , Hyunbin Jo , Dong-Kyun Kim , Kiyun Park and Ihn-Sil Kwak * Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea Faculty of Marine Technology, Chonnam National University, Yeosu 59626, Korea * Correspondence: inkwak@hotmail.com; Tel.: +82-61-659-7148 Received: 4 July 2019; Accepted: 15 August 2019; Published: 21 August 2019 Featured Application: Complement monitoring tool. Abstract: The nonlinearity and complexity of coastal ecosystems often cause diculties when analyzing spatial and temporal patterns of ecological traits. Environmental DNA (eDNA) monitoring has provided an alternative to overcoming the aforementioned issues associated with classical monitoring. We determined aquatic community taxonomic composition using eDNA based on a meta-barcoding approach that characterizes the general ecological features in the Gwangyang Bay coastal ecosystem. We selected the V9 region of the 18S rDNA gene (18S V9), primarily because of its broad range among eukaryotes. Our results produced more detailed spatial patterns in the study area previously categorized (inner bay, main channel of the bay and outer bay) by Kim et al. (2019). Specifically, the outer bay zone was clearly identified by CCA using genus-level identification of aquatic organisms based on meta-barcoding data. We also found significant relationships between environmental factors. Therefore, eDNA monitoring based on meta-barcoding approach holds great potential as a complemental monitoring tool to identify spatial taxonomic distribution patterns in coastal areas. Keywords: coastal ecosystem; eDNA; spatial patterns; complemental monitoring tool 1. Introduction Biological monitoring contributes to the understanding of complex ecosystem structures and functions in targeted systems [1]. Accordingly, it is crucial in detecting and assessing environmental changes in order to ensure proper management and conservation of complex ecosystems [2]. Coastal environments are among the most complex ecosystems due to tidal activity, and typically retain high economic and environmental values in light of aquatic resources and biodiversity [3]. Coastal ecosystems are often severely a ected by anthropogenic activities such as industrial fishing [4,5], marine transport and leisure activities [6], aquaculture and the aquarium trade [7,8], living seafood and lure fisheries [9,10], and non-indigenous species (NIS) which induce greater pressures on endemic ecosystems and often drive native species to extinction in the resulting habitats [11–13]. The nonlinearity and complexity of coastal ecosystems due to the aforementioned activities often causes diculties when analyzing spatial and temporal patterns of ecological traits. Environmental DNA (eDNA) as described by Ogram et al. [14] who extracted microbial DNA from the sediment, has increasingly been used in recent years for biological monitoring purposes. Recently, a large number of papers have reported the use of eDNA monitoring in analyses of soil, water and even air [15]. Andersen et al. [16] examined the possibility of monitoring large mammals using eDNA in soil samples, and eDNA from water monitoring of fish [17–20] and amphibians [21,22] has been successful. Furthermore, Hawkins et al. [23] demonstrated that a complete taxonomic list of functional feeding Appl. Sci. 2019, 9, 3450; doi:10.3390/app9173450 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3450 2 of 12 group (FFG) criteria, based on high resolution of identification (genus or species level) based on DNA techniques, can determine the e ects of watershed alterations on stream invertebrate assemblages in bulk eDNA samples. However, family level identification based on visual inspection did not reveal any di erences of FFG composition between sites. Next-generation sequencing (NGS) technologies for eDNA monitoring have provided an alternative to overcome issues such as identification problems associated with classical monitoring in a species rich coastal environment [3,20]. The values of coastal ecosystems, such as primary production (i.e., sea grass and algae) and commercial fish yields, are intertwined with multiple environmental factors, including nutrient concentrations (carbon, nitrogen, and phosphorus), phytoplankton growth, zooplankton grazing e ects, and benthic communities. The Gwangyang Bay coastal ecosystem is the most economically productive in Korean peninsula. Specifically, in the midst of three major cities (Gwangyang, Yeosu and Suncheon) in Jeonnam Province, with Gwangyang Bay, it yields 71% (1,297,815 tons) of the annual aquacultural resource output as of 2016 [24]. However, there is a large industrial area near the Bay, and the area is primarily involved in industrial activities such as oil refineries and steel production plants. Kim et al. [25] characterized the dissimilarity of water quality and sediment contamination, and identified the importance of nutrients supplied by rivers. Such findings are, however, still limited to representing general ecological features of the Gwangyang Bay coastal ecosystem. The main objective of this study was to determine aquatic community taxonomic composition using eDNA based on an NGS approach for characterizing general ecological features in the Gwangyang Bay coastal ecosystem. We analyzed the community spatial distribution with regard to environmental parameters, and the habitat types (marine, freshwater and estuarine), feeding habits (filter feeder, carnivore, producer and symbiotic) and indigenous species rate (ISR) among the three di erent zones referred to by Kim et al. [25]. Moreover, we discuss the e ectiveness and sensitivity of our NGS approach on the Gwangyang Bay coastal ecosystem. 2. Materials and Methods 2.1. Study Area Gwangyang Bay is part of the Korean National Archipelagos located o the south coast of the 6 3 1 Korean peninsula (Figure 1). The bay receives an annual mean discharge of 2298 10 m yr from the Seomjin River [26]. A significant amount of nutrients drains into the system from the watershed (~5000 km ). The water depth varies from 10 m at the Seomjin River estuary to 50 m at the outer Gwangyang Bay. The tidal cycle appears to be semi-diurnal. Compared to other Korean river estuaries which have barriers, the Seomjin River estuary remains open, and thus the water mass is exchanged between the river and ocean more actively. The natural condition of Gwangyang Bay is apt to increase primary productivity as well as biological diversity. In this respect, Gwangyang Bay (~450 km from the estuary to the outer bay) is the most economically productive coastal ecosystem in the Korean peninsula [25]. 2.2. Sampling, Data Collection and Primer Selection A survey permitted by Ha-dong local government (permission number: 2010-0165) was conducted in June 2018. We sampled the surface water (approximately the top 50 cm) in this study. In total, nineteen sampling sites were selected, which covered an extensive area from the Seomjin River estuary to the outer Gwangyang Bay (Figure 1). According to Kim et al. [25], at Gwangyang Bay, higher water temperatures corresponded to lower salinity, and vice versa. Phosphorus and nitrogen concentrations were spatially similar across the Bay. Based on the divisions indicated in Kim et al. [25], Zone I covered the inner Bay (sites 3–7, and 9), Zone II represented the main channel of the Bay (sites 8, 10, and 11), and Zone III mostly belonged to the outer Bay (sites 12–21) (Figure 1). Water samples for meta-barcoding analysis (more than 1 L per sample) were obtained at the same time and moved with dry ice to laboratory to filter (0.45 m pore-size membrane; Advantec MFS membrane filter, Dublin, Appl. Sci. 2019, 9, x FOR PEER REVIEW 3 of 12 every study site to prevent cross contamination. Water temperature and salinity were measured on- site using portable equipment (Model: YSI Professional Plus, OH, USA), while the nutrient and Appl. Sci. 2019, 9, 3450 3 of 12 chlorophyll-a concentrations were analyzed in the lab. Phosphorus and nitrogen concentrations were measured using an UV spectrophotometer based on standard analytical methods proposed by the USA) and stored at 80 C before NGS analysis. Negative controls were included for every study Korean Ministry of Oceans and Fisheries. Chlorophyll-a measurements were also based on UV site to prevent cross contamination. Water temperature and salinity were measured on-site using spectrophotometry. In contrast to nutrient measurements, chlorophyll-a samples were filtered portable equipment (Model: YSI Professional Plus, OH, USA), while the nutrient and chlorophyll-a through a 0.45 μm pore-size membrane (Model: Advantec MFS membrane filter). The filter concentrations were analyzed in the lab. Phosphorus and nitrogen concentrations were measured using membrane was then homogenized after acetone extraction prior to spectrophotometry. Organic and an UV spectrophotometer based on standard analytical methods proposed by the Korean Ministry inorganic carbon concentrations were measured using a carbon analyzer (Model: vario TOC cub, of Oceans and Fisheries. Chlorophyll-a measurements were also based on UV spectrophotometry. Langenselbold, Germany) using 850 °C combustion catalytic oxidation methods. In contrast to nutrient measurements, chlorophyll-a samples were filtered through a 0.45 m pore-size We selected the V9 region of the 18S rDNA gene (18S V9), primarily because of its broad range membrane (Model: Advantec MFS membrane filter). The filter membrane was then homogenized among eukaryotes [27,28]. NGS approaches using the 18S V9 region have recently allowed the after acetone extraction prior to spectrophotometry. Organic and inorganic carbon concentrations were characterization of marine planktonic biodiversity in the oceans [29] and prompted biomarker measured using a carbon analyzer (Model: vario TOC cub, Langenselbold, Germany) using 850 C establishment initiatives [30]. combustion catalytic oxidation methods. Figure 1. Map of the study sites with division of zones referred to by Kim et al. (2019) [25]. Figure 1. Map of the study sites with division of zones referred to by Kim et al. (2019) [25]. We selected the V9 region of the 18S rDNA gene (18S V9), primarily because of its broad 2.3. DNA Extraction and Metagenomic Sequencing range among eukaryotes [27,28]. NGS approaches using the 18S V9 region have recently allowed Genomic DNA was extracted using PowerSoil DNA Isolation Kit (Cat. No. 12888, Qiagen, the characterization of marine planktonic biodiversity in the oceans [29] and prompted biomarker Düsseldorf, Germany) according to the manufacturer’s protocol. DNA extracted for sequencing was establishment initiatives [30]. prepared according to Illumina 18S Metagenomic Sequencing Library protocols (San Diego, CA, USA). DNA quantity, quality, and integrity were measured by PicoGreen (Thermo Fisher Scientific, 2.3. DNA Extraction and Metagenomic Sequencing Waltham, MA, USA) and VICTOR Nivo Multimode Microplate Reader (PerkinElmer, Akron, OH, Genomic DNA was extracted using PowerSoil DNA Isolation Kit (Cat. No. 12888, Qiagen, USA). The 18S rRNA gene was amplified using 18S V9 primers. The primer sequences are as follows: Düsseldorf, Germany) according to the manufacturer ’s protocol. DNA extracted for sequencing 18S V9 primer including adaptor sequence (Forward Primer: 5′ was prepared according to Illumina 18S Metagenomic Sequencing Library protocols (San Diego, CA, TCGTCGGCAGCGTCAGATGTGTATAA GAGACAGCCCTGCCHTTTGTACACAC 3′/Reverse USA). DNA quantity, quality, and integrity were measured by PicoGreen (Thermo Fisher Scientific, Primer: 5′ GTCTCGTGGGCTCGGAGATG TGTATAAGAGACAGCCTTCYGCAGGTTCACCTAC Waltham, MA, USA) and VICTOR Nivo Multimode Microplate Reader (PerkinElmer, Akron, OH, USA). 3′). To amplify the target region attached with adapters, as a first PCR process, the extracted DNA The 18S rRNA gene was amplified using 18S V9 primers. The primer sequences are as follows: 18S was amplified by 18S V9 primers with one cycle of 3 min at 95 °C, 25 cycles of 30 s at 95 °C, 30 s at 55 V9 primer including adaptor sequence (Forward Primer: 5 TCGTCGGCAGCGTCAGATGTGTATAA °C, 30 s at 72 °C, and a final step of 5 min at 72 °C for amplicon PCR product. As a second process, to 0 0 GAGACAGCCCTGCCHTTTGTACACAC 3 /Reverse Primer: 5 GTCTCGTGGGCTCGGAGATG produce indexing PCR, the first PCR product was subsequently amplified with one cycle of 3 min at TGTATAAGAGACAGCCTTCYGCAGGTTCACCTAC 3 ). To amplify the target region attached with adapters, as a first PCR process, the extracted DNA was amplified by 18S V9 primers with one cycle of 3 min at 95 C, 25 cycles of 30 s at 95 C, 30 s at 55 C, 30 s at 72 C, and a final step of 5 min at 72 C for Appl. Sci. 2019, 9, 3450 4 of 12 amplicon PCR product. As a second process, to produce indexing PCR, the first PCR product was subsequently amplified with one cycle of 3 min at 95 C, eight cycles of 30 s at 95 C, 30 s at 55 C, 30 s at 72 C, and a final step of 5 min at 72 C. The final products were normalized and pooled using PicoGreen (Thermo Fisher Scientific, Waltham, MA, USA), and the size of libraries were verified using the LabChip GX HT DNA High Sensitivity Kit (PerkinElmer, Akron, OH, USA). The library was sequenced using the MiSeq— NGS platform (Illumina, San Diego, CA, USA) provided as a commercial service (Macrogen Inc., Seoul, Korea). Raw reads were trimmed with CD-HIT-OTU 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. Merged reads were processed using Qiime version 1.9 [31] and were clustered into operational taxonomic units (OTUs) with UCLUST [32], using a greedy algorithm with OTUs at a 97% OUT cuto value. Taxonomic classifications were assigned to the obtained representative sequences using BLASTn [33] and UCLUST [32]. 2.4. Data Analysis and Statistics OTUs assigned by meta-barcoding were classified into habitat types (marine, freshwater and estuarine), trophic level (first consumer [filter feeder], second consumer [carnivore], producer and symbiotic) and ISR (indigenous, non-indigenous). Canonical correspondence analysis (CCA) of OTUs exhibiting >1% relative abundance was performed using the PAST 3.0 program [34] to evaluate relationships between abundance of OTU sequences (based on genus level identification) and environmental factors (temperature (Temp.), salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)). All CCA results were constructed using relative abundance data, with natural logarithms transformation (ln 1 + X) used for sample normalization. Linear relationships (Pearson correlations) were calculated between the above environmental factors and classified OTU sequences, based on habitat using XLSTAT version 2018.6.54467 (64 bit) as a plug-in for the Microsoft Excel program [35]. 3. Results 3.1. Meta-Barcoding In total, 3,066,013 paired-end reads from the 19 samples were generated on the Illumina MiSeq— platform, of which 98.5% passed Q30 (Phred quality score > 30) for improving accuracy of sequences in this study (Supplementary Materials). Each sample yielded paired-end reads ranging from 21,101–299,305 reads (mean: 161,369 reads), which was similar to the amount of reads in the previous study [36], and all samples exhibited saturation of the number of OTUs by rarefaction curve analysis. Gamma-diversity was 352 OTUs produced with a cuto of 97% similarity. The resulting 352 OTUs were classified into 19 genus-level taxonomic groups (those representing <0.04% abundance were not plotted). Uncultured and non-assigned reads were discarded. 3.2. Spatial Distributions of Aquatic Organisms Based on Meta-Barcoding We carried out a survey in June 2018. Relationships among the environmental variables and aquatic organisms based on meta-barcoding were explored by CCA (Figure 2). The first axis explained 33.3% of the variance and distinguished Zones III-2 and III-3 with higher salinity, EC, TIC, and lower Chl-a and TN from the other zones. The second axis (19.6% of variance explained) distinguished the sites from Zone III-1 and the other zones, by having higher TOC and TC, lower temperature, and TP. Nutrient-related factors (Chl-a, TN, and TP) were correlated with Acropora sp. (small polyp stony coral) and Megabalanus sp., (barnacle) and salinity was correlated with Skeletonema sp. (diatoms) and Centropages sp. (copepods). Appl. Sci. 2019, 9, 3450 5 of 12 Appl. Sci. 2019, 9, x FOR PEER REVIEW 5 of 12 Figure 2. Canonical correspondence analysis (CCA) used to evaluate (A) the relationships between Figure 2. Canonical correspondence analysis (CCA) used to evaluate (A) the relationships between abundance of operational taxonomic units (OTUs) sequences (based on genus level identification) and abundance of operational taxonomic units (OTUs) sequences (based on genus level identification) environmental factors (temperature (Temp.), Salinity, total phosphorus (TP), total nitrogen (TN), and environmental factors (temperature (Temp.), Salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)). (B) Sites grouping based on CCA analysis. elemental carbon (EC)). (B) Sites grouping based on CCA analysis. Appl. Sci. 2019, 9, 3450 6 of 12 Appl. Sci. 2019, 9, x FOR PEER REVIEW 6 of 12 Abundance of assigned OTU sequences showed di erent patterns among the study sites (Figure 3). Abundance of assigned OTU sequences showed different patterns among the study sites (Figure The dominant OTU was assigned to Acropora sp. and the sub-dominant OTU was Acartia sp. (copepods). 3). The dominant OTU was assigned to Acropora sp. and the sub-dominant OTU was Acartia sp. Most common OTUs within the study sites were comprised of phytoplankton, followed by zooplankton (copepods). Most common OTUs within the study sites were comprised of phytoplankton, followed and amphipods (Figure 3B). Interestingly, the abundance of OTUs showed di erent compositions by zooplankton and amphipods (Figure 3B). Interestingly, the abundance of OTUs showed different among the three di erent zones (I, II, III). Zone I showed similar patterns to Zones II and III-1, whereas compositions among the three different zones (I, II, III). Zone I showed similar patterns to Zones II Zones III-2 and 3 showed di erent compositions of aquatic organisms which distinguished them from and III-1, whereas Zones III-2 and 3 showed different compositions of aquatic organisms which other zones. In particular, Zone III-3 exhibited an entirely di erent pattern, comprised of marine distinguished them from other zones. In particular, Zone III-3 exhibited an entirely different pattern, organisms such as Euchaeta sp. (copepods) and Tessarabrachion sp. (krill) compared with other zone comprised of marine organisms such as Euchaeta sp. (copepods) and Tessarabrachion sp. (krill) divisions (Figure 3B). compared with other zone divisions (Figure 3B). Figure 3. Abundance of OTU sequences along the study sites (A) with site description of physical Figure 3. Abundance of OTU sequences along the study sites (A) with site description of physical features and zone classification (B) based on genus identification level. features and zone classification (B) based on genus identification level. 3.3. Functional Features and Non-Indigenous Species 3.3. Functional Features and Non-Indigenous Species When divided into categories (habitat types, trophic level, and ISR), averages of abundant When divided into categories (habitat types, trophic level, and ISR), averages of abundant sequences showed di erent patterns among the zones (I, II, III-1-2-3). The most dominant habitat type sequences showed different patterns among the zones (I, II, III-1-2-3). The most dominant habitat of OTU was the marine type for the three di erent zones (Figure 4A), while the types of feeding habit type of OTU was the marine type for the three different zones (Figure 4A), while the types of feeding and indigenous rate showed complex response patterns. The dominant trophic level was first consumer habit and indigenous rate showed complex response patterns. The dominant trophic level was first and second consumer, followed by producer and symbiotic trophic levels (Figure 4B). The indigenous consumer and second consumer, followed by producer and symbiotic trophic levels (Figure 4B). The rate of Zone III-3 was significantly higher than other zones, but Zones II and III-1 showed opposite indigenous rate of Zone III-3 was significantly higher than other zones, but Zones II and III-1 showed patterns opposite patt (Figureerns 4C),(Figure 4C), wh while Zones I and ile III-2 Zones I presented and III-2 presen no significant ted no sign di erence ificin ant difference in indigenous rates. The indigenous rates. The three division three divisions of Zone III from CCA s of Zone resultsIII from CCA results demon demonstrated similar patterns strated similar within the patterns three zone within the three zone types (Zones I, II-and III). types (Zones I, II-and III). We found significant relationships between environmental factors (Table 1). Specifically, We found significant relationships between environmental factors (Table 1). Specifically, temperature temperature had positive relationships with nutrient-related factors such as TP and PN, and showed had positive relationships with nutrient-related factors such as TP and PN, and showed a negative a negative relationship with TOC. Salinity showed significant negative relationships with TN and relationship with TOC. Salinity showed significant negative relationships with TN and TP, but also TP, but also displayed a positive relationship with carbon-related factors such as EC and TC. displayed a positive relationship with carbon-related factors such as EC and TC. However, there were no However, there were no significant relationships between environmental factors and divisions of significant relationships between environmental factors and divisions of categories except for habitat categories except for habitat types (marine, estuarine, freshwater). The marine habitat of OTUs types (marine, estuarine, freshwater). The marine habitat of OTUs revealed a significant positive revealed a significant positive relationship with salinity (r = 0.879) and EC (r = 0.878), respectively, relationship with salinity (r = 0.879) and EC (r = 0.878), respectively, whereas freshwater and estuarine whereas freshwater and estuarine types of OTUs showed negative relationships with salinity (r = types of OTUs showed negative relationships with salinity (r =0.888 and0.856) and EC (r =0.884 −0.888 and −0.856) and EC (r = −0.884 and −0.856) respectively. and0.856) respectively. Appl. Sci. 2019, 9, 3450 7 of 12 Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 12 Figure 4. Comparison of the habitat types ((A), marine-freshwater-estuarine), trophic level ((B), consumer 1 Figure 4. Comparison of the habitat types ((A), marine-freshwater-estuarine), trophic level ((B), [filter feeder], consumer 2 [carnivore], producer and symbiotic) and natives ((C), non-indigenous and consumer 1 [filter feeder], consumer 2 [carnivore], producer and symbiotic) and natives ((C), non- indigenous) among five zoning types based on genus identification level. indigenous and indigenous) among five zoning types based on genus identification level. Appl. Sci. 2019, 9, 3450 8 of 12 Table 1. Linear relationship (Pearson correlation) between environmental factors (temperature (Temp.), Salinity, total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TO), elemental carbon (EC)) and classified OTU sequences based on habitat types, feeding habits and natives (Bold: significant relationship). Variables Temp Salinity EC TP TN TOC TIC TC Chla Temp 1 0.192 0.216 0.505 0.464 0.599 0.275 0.220 0.422 Salinity 0.192 1 0.996 0.586 0.532 0.301 0.435 0.486 0.006 EC 0.216 0.996 1 0.615 0.564 0.325 0.440 0.478 0.034 TP 0.505 0.586 0.615 1 0.904 0.505 0.634 0.715 0.192 TN 0.464 0.532 0.564 0.904 1 0.424 0.680 0.645 0.200 TOC 0.599 0.301 0.325 0.505 0.424 1 0.127 0.355 0.248 TIC 0.275 0.435 0.440 0.634 0.680 0.127 1 0.877 0.195 TC 0.220 0.486 0.478 0.715 0.645 0.355 0.877 1 0.263 Chla 0.422 0.006 0.034 0.192 0.200 0.248 0.195 0.263 1 Marine 0.197 0.879 0.878 0.343 0.195 0.314 0.051 0.151 0.082 Fresh 0.123 0.888 0.884 0.332 0.199 0.286 0.061 0.175 0.024 Estuarine 0.269 0.856 0.856 0.349 0.188 0.337 0.041 0.123 0.139 First consumer 0.195 0.283 0.271 0.164 0.120 0.152 0.329 0.269 0.159 Second consumer 0.011 0.387 0.373 0.329 0.070 0.159 0.239 0.346 0.026 Producer 0.054 0.319 0.307 0.288 0.040 0.194 0.162 0.281 0.064 Symbiotic 0.032 0.335 0.326 0.266 0.182 0.158 0.298 0.301 0.220 Non-indigenous 0.002 0.348 0.331 0.284 0.021 0.137 0.195 0.295 0.008 Indigenous 0.002 0.348 0.331 0.284 0.021 0.137 0.195 0.295 0.008 4. Discussion 4.1. E ectiveness of eDNA Monitoring Our results showed that eDNA monitoring based on NGS holds great potential as a complementary monitoring tool to identify spatial taxonomic distribution patterns in coastal areas. We characterized detailed zonation patterns of the outer bay of Gwangyang Bay previously categorized by Kim et al. [25]. Specifically, Zones III-2 and III-3 were clearly resolved by CCA using genus-level identification of aquatic organisms based on meta-barcoding data (Figure 2). When divided into categories (habitat types, trophic level, and ISR), averages of abundant sequences showed di erent patterns among the zones (I, II, III-1-2-3). The NIS detection of Zone III-3 was significantly higher than the other zones. Therefore, eDNA based on meta-barcoding is a promising ecological tool for monitoring, such as biodiversity assessment or NIS management [24,37–40]. This approach by meta-barcoding in complex coastal ecosystems confers three distinct advantages over other methods. First, the range of aquatic organisms in coastal ecosystems open to study is widened. Second, identification of aquatic organism to the species or genus level (Appendix A), which can be converted into ecological values such as FFC and NIS, is possible. Finally, sensitivity of the meta-barcoding approach using small volumes of water (1 L) is a promising alternative to traditional methods (i.e., dredges and surber samplers) for biodiversity detection in coastal areas. Using these advantages, eDNA monitoring can provide a useful tool for use in environmental management. However, some taxa were unable to be identified to the species or genus level due to the incompleteness of reference databases (i.e., NCBI GenBank). More accurate target regions such as the cytochrome oxidase I region, which is called the standard region of barcoding, are needed to identify target species at the species level and are required for identification of aquatic organisms [41]. Another challenge associated with eDNA monitoring is the risk of false-positive and false-negative detections [42]. The reliability of the eDNA sampling method should be demonstrated using in silico, in vitro and in situ validation tests in the coastal area. Even though this approach has limitations, taxonomic expertise is not required and it can supplement observational records and field surveys to obtain marine ecosystem samples and information, as NGS reveals biodiversity and the number of NIS in one specific area along the their temporal and spatial distribution. Appl. Sci. 2019, 9, 3450 9 of 12 4.2. Ecological Values of eDNA Monitoring Our results showed that Acropora sp. was dominant in terms of abundance of OTU sequences across all study sites, even though it is not a planktonic species. The Acropora colonies post-recruitment by larval recruitment demonstrates higher eciency for survival than coral-colony growth [43]. It was concluded that larval recruitment largely determines species composition, and that reduced larval recruitment is responsible for the sparse distribution of fragmenting species [44]. Therefore, the larval stage could be easily detected among the study sites by our eDNA monitoring approach. We also found that nutrient-related factors (Chl-a, TN, and TP) were significantly correlated with Acropora sp. and Megabalanus sp. (Figure 2). It is therefore possible that the strong positive correlation between the two species could be the result of coral-inhabiting barnacles [45]. The comprehensive understanding of feeding characteristics of aquatic organisms has not been well elucidated in comparison to their importance [46–49]. Our results showed that composition of aquatic organisms have di erent patterns of feeding habits among the three di erent zone divisions designated by Kim et al. [25] (Figure 4), and we also found similar patterns using CCA based on genus-level identification resulting from our division of Zone III (Figure 2). However, previous research findings were attributed to the absence of adequate information for analysis obtained from the field sites due to diculties in culture, handling, and identification of eDNA samples derived from bulk sediment and filtered water. In this sense, our results overcame the aforementioned limitations by using a broader detection range for aquatic organisms. Although, Zhan et al. [50] has described the increased sensitivity of meta-barcoding for NIS, its application for monitoring biological invasion has only recently been demonstrated [51]. In the present work, we identified the utility of meta-barcoding for detection of NIS and their spatial distribution patterns (Figure 4). The reason behind this is the capacity to detect the presence of individuals at early life stages, such as eggs or nauplius larvae, whose identification is difficult with traditional methods [52]. The sensitivity of meta-barcoding, combined with the relatively low time and cost associated with this technique [53], makes it a promising alternative approach for the rapid and accurate detection of biodiversity shifts in aquatic organisms, allowing its potential implementation in environmental policies. Supplementary Materials: DNA sequences: DRYAD entry https://datadryad.org/review?doi=doi:10.5061/dryad. 41b1dp3. Author Contributions: Conceptualization, H.J., D.-K.K. and I.-S.K.; methodology, H.J. and K.P.; formal analysis, H.J. and D.-K.K.; investigation, H.J. and D.-K.K.; writing—original draft preparation, H.J. and I.-S.K.; writing—review and editing, D.-K.K. and I.-S.K.; project administration, I.-S.K.; funding acquisition, I.-S.K. Funding: This research was funded by the National Research Foundation of Korea, grant number NRF-2018R1A6A1A03024314. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Species Level Identification List Used in this Paper. Kingdom Species Eukaryota Acropora granulosa Eukaryota Acartia omorii Eukaryota Tessarabrachion oculatum Eukaryota Megabalanus stultus Eukaryota Euchaeta indica Eukaryota Bougainvillia muscus Eukaryota Centropages typicus Eukaryota Skeletonema costatum Eukaryota Monstrilla sp. Eukaryota Fibrocapsa japonica Eukaryota Biecheleria brevisulcata Eukaryota Paulsenella vonstoschii Appl. Sci. 2019, 9, 3450 10 of 12 Kingdom Species Eukaryota Cavernomonas mira Eukaryota Caprella californica Eukaryota Biddulphia sp. Eukaryota Candacia bispinosa Eukaryota Thalassiosira mala Eukaryota Akashiwo sanguinea Eukaryota Conticribra weissflogiopsis References 1. Jo, H.; Ventura, M.; Vidal, N.; Gim, J.S.; Buchaca, T.; Barmuta, L.A.; Erik, J.; Joo, G.J. Discovering hidden biodiversity: The use of complementary monitoring of fish diet based on DNA barcoding in freshwater ecosystems. Ecol. Evol. 2016, 6, 219–232. [CrossRef] [PubMed] 2. Robertson, G.P.; Collins, S.L.; Foster, D.R.; Brokaw, N.; Ducklow, H.W.; Gragson, T.L.; Gries, C.; Hamilton, S.K.; McGuire, A.D.; Moore, J.C.; et al. Long-term ecological research in a human-dominated world. BioScience 2006, 62, 342–353. [CrossRef] 3. Baird, D.J.; Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 2012, 21, 2039–2044. [CrossRef] [PubMed] 4. Myers, R.A.; Worm, B. Rapid worldwide depletion of predatory fish communities. Nature 2003, 423, 280. [CrossRef] [PubMed] 5. Pusceddu, A.; Bianchelli, S.; Martín, J.; Puig, P.; Palanques, A.; Masqué, P.; Danovaro, R. Chronic and intensive bottom trawling impairs deep-sea biodiversity and ecosystem functioning. Proc. Natl. Acad. Sci. USA 2014, 111, 8861–8866. [CrossRef] [PubMed] 6. Ruiz, G.M.; Fofono , P.W.; Carlton, J.T.; Wonham, M.J.; Hines, A.H. Invasion of coastal marine communities in North America: Apparent patterns, processes, and biases. Annu. Rev. Ecol. Syst. 2000, 31, 481–531. [CrossRef] 7. Naylor, R.; Williams, S.L.; Strong, D.R. Aquaculture—A gateway for exotic species. Science 2001, 294, 1655–1656. [CrossRef] 8. Padilla, D.K.; Williams, S.L. Beyond ballast water: Aquarium and ornamental trades as sources of invasive species in aquatic ecosystems. Front. Ecol. Environ. 2004, 2, 131–138. [CrossRef] 9. Chapman, J.W.; Miller, T.W.; Coan, E.V. Live seafood species as recipes for invasion. Conserv. Biol. 2003, 17, 1386–1395. [CrossRef] 10. Weigel, S.; Bester, K.; Hühnerfuss, H. Identification and quantification of pesticides, industrial chemicals, and organobromine compounds of medium to high polarity in the North Sea. Mar. Pollut. Bull. 2005, 50, 252–263. [CrossRef] 11. Ardura, A.; Planes, S. Rapid assessment of non-indigenous species in the era of the eDNA barcoding: A Mediterranean case study. Estuar. Coast. Shelf Sci. 2017, 188, 81–87. [CrossRef] 12. Carlton, J.T.; Geller, J.B. Ecological roulette: The global transport of nonindigenous marine organisms. Science 1993, 261, 78–82. [CrossRef] 13. Williams, S.L.; Grosholz, E.D. The invasive species challenge in estuarine and coastal environments: Marrying management and science. Estuaries Coasts 2008, 31, 3–20. [CrossRef] 14. Ogram, A.; Sayler, G.S.; Barkay, T. The extraction and purification of microbial DNA from sediments. J. Microbiol. Meth. 1987, 7, 57–66. [CrossRef] 15. Taberlet, P.; Coissac, E.; Hajibabaei, M.; Rieseberg, L.H. Environmental DNA. Mol. Ecol. 2012, 21, 1789–1793. [CrossRef] 16. Andersen, K.; Bird, K.L.; Rasmussen, M.; Haile, J.; Breuning-Medsen, H.; Kjaer, K.H.; Orlando, L.; Geilbert, M.T.P.; Willerslev, E. Meta-barcoding of ‘dirt’ DNA from soil reflects vertebrate biodiversity. Mol. Ecol. 2011, 21, 1966–1979. [CrossRef] 17. Minamoto, T.; Yamanaka, H.; Takahara, T.; Honjo, M.N.; Kawabata, Z. Surveillance of fish species composition using environmental DNA. Limnology 2012, 13, 193–197. [CrossRef] Appl. Sci. 2019, 9, 3450 11 of 12 18. Thomsen, P.; Kielgast, J.; Iversen, L.L.; Wiuf, C.; Rasmussen, M.; Gilbert, M.T.P.; Orlando, L.; Willerslev, E. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 2012, 21, 2565–2573. [CrossRef] 19. Thomsen, P.; Kielgast, J.; Iversen, L.L.; Møller, P.R.; Rasmussen, M.; Willerslev, E. Detection of a diverse marine fish fauna using environmental DNA from seawater samples. PLoS ONE 2012, 7, e41732. [CrossRef] 20. Yamamoto, S.; Masuda, R.; Sato, Y.; Sado, T.; Araki, H.; Kondoh, M.; Toshifumi, M.; Miya, M. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 2017, 7, 40368. [CrossRef] 21. Ficetola, G.F.; Miaud, C.; Pompanon, F.; Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 2008, 4, 423–425. [CrossRef] 22. Goldberg, C.S.; Pilliod, D.S.; Arkle, R.S.; Waits, L.P. Molecular detection of vertebrates in stream water: A demonstration using Rocky Mountain tailed frogs and Idaho giant salamanders. PLoS ONE 2011, 6, e22746. [CrossRef] 23. Hawkins, C.P.; Norris, R.H.; Hogue, J.N.; Feminella, J.W. Development and evaluation of predictive models for measuring the biological integrity of streams. Ecol. Appl. 2000, 10, 1456–1477. [CrossRef] 24. Korean Statistical Information Service (KOSIS). Available online: http://kosis.kr (accessed on 9 June 2019). 25. Kim, D.K.; Jo, H.; Han, I.; Kwak, I.S. Explicit Characterization of Spatial Heterogeneity Based on Water Quality, Sediment Contamination, and Ichthyofauna in a Riverine-to-Coastal Zone. Int. J. Environ. Res. Health 2019, 16, 409. [CrossRef] 26. Kang, C.K.; Kim, J.B.; Lee, K.S.; Kim, J.B.; Lee, P.Y.; Hong, J.S. Trophic importance of benthic microalgae to macrozoobenthos in coastal bay systems in Korea: Dual stable C and N isotope analyses. Mar. Ecol. Prog. Ser. 2003, 259, 79–92. [CrossRef] 27. Abad, D.; Albaina, A.; Aguirre, M.; Laza-Martínez, A.; Uriarte, I.; Iriarte, A.; Arantza, I.; Fernando, V.; Estonba, A. Is metabarcoding suitable for estuarine plankton monitoring? A comparative study with microscopy. Mar. Biol. 2016, 163, 149. [CrossRef] 28. de Vargas, C.; Audic, S.; Henry, N.; Decelle, J.; Mahé, F.; Logares, R.; Lara, E.; Berney, C. Eukaryotic plankton diversity in the sunlit ocean. Science 2015, 348, 1261605. [CrossRef] 29. Albaina, A.; Aguirre, M.; Abad, D.; Santos, M.; Estonba, A. 18S rRNA V9 metabarcoding for diet characterization: A critical evaluation with two sympatric zooplanktivorous fish species. Ecol. Evol. 2016, 6, 1809–1824. [CrossRef] 30. Massana, R.; Gober, A.; Audic, S.; Bass, D.; Bittner, L.; Boutte, C.; Chambouvet, A.; Christen, R. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing. Environ. Microbiol. 2015, 17, 4035–4049. [CrossRef] 31. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Huttley, G.A. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335. [CrossRef] 32. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010, 26, 2460–2461. [CrossRef] 33. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [CrossRef] 34. Hammer, Ø.; Harper, D.A.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, 9. 35. Addinsoft. XLSTAT Statistical and Data Analysis Solution; Addinsoft: New York, NY, USA, 2019. 36. Thomsen, P.F.; Møller, P.R.; Sigsgaard, E.E.; Knudsen, S.W.; Jørgensen, O.A.; Willerslev, E. Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes. PLoS ONE 2016, 11, e0165252. [CrossRef] 37. Bourlat, S.J.; Borja, A.; Gilbert, J.; Taylor, M.I.; Davies, N.; Weisberg, S.B.; Glöckner, F.O. Genomics in marine monitoring: New opportunities for assessing marine health status. Mar. Pollut. Bull. 2013, 74, 19–31. [CrossRef] 38. Valentini, A.; Taberlet, P.; Miaud, C.; Civade, R.; Herder, J.; Thomsen, P.F.; Bellemain, E.; Besnard, A.; Coissac, E.; Boyer, F.; et al. Next-generation monitoring of aquatic biodiversity using environmental DNA barcoding. Mol. Ecol. 2016, 25, 929–942. [CrossRef] [PubMed] 39. O’Donnell, J.L.; Kelly, R.P.; Shelton, A.O.; Samhouri, J.F.; Lowell, N.C.; Williams, G.D. Spatial distribution of environmental DNA in a nearshore marine habitat. Peer J. 2017, 5, e3044. [CrossRef] [PubMed] Appl. Sci. 2019, 9, 3450 12 of 12 40. Djurhuus, A.; Pitz, K.; Sawaya, N.A.; Rojas-Márquez, J.; Michaud, B.; Montes, E.; Muller-Karger, F.; Breitbart, M. Evaluation of marine zooplankton community structure through environmental DNA metabarcoding. Limnol. Oceanogr. Methods 2018, 16, 209–221. [CrossRef] 41. Hajibabaei, M.; Smith, M.A.; Janzen, D.H.; Rodriguez, J.J.; Whitfield, J.B.; Hebert, P.D. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 2006, 6, 959–964. [CrossRef] 42. Darling, J.A.; Mahon, A.R. From molecules to management: Adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environ. Res. 2011, 111, 978–988. [CrossRef] 43. Victor, S.; Golbuu, Y.; Yukihira, H.; van Woesik, R. Acropora size-frequency distributions reflect spatially variable conditions on coral reefs of Palau. Bull. Mar. Sci. 2009, 85, 149–157. 44. Wallace, C.C. Reproduction, recruitment and fragmentation in nine sympatric species of the coral genus Acropora. Mar. Biol. 1985, 88, 217–233. [CrossRef] 45. Lewis, J.B. Recruitment, growth and mortality of a coral-inhabiting barnacle Megabalanus stultus (Darwin) upon the hydrocoral Millepora complanata Lamarck. J. Exp. Mar. Biol. Ecol. 1992, 162, 51–64. [CrossRef] 46. Rothhaupt, K. Di erences in particle size-dependent feeding eciencies of closely related rotifer species. Limnol. Oceanogr. 1990, 35, 16–23. [CrossRef] 47. Thouvenot, A.; Debroas, D.; Richardot, M.; Devaux, J. Impact of natural metazooplankton assemblage on planktonic microbial communities in a newly flooded reservoir. J. Plankton Res. 1999, 21, 179–199. [CrossRef] 48. Mohr, S.; Adrian, R. Reproductive success of the rotifer Brachionus calyciflorus feeding on ciliates and flagellates of di erent trophic modes. Freshwater Biol. 2002, 47, 1832–1839. [CrossRef] 49. Devetter, M.; Sed’a, J. Rotifer fecundity in relation to components of microbial food web in a eutrophic reservoir. Hydrobiologia 2003, 504, 167–175. [CrossRef] 50. Zhan, A.; Hulak, M.; Sylvester, F.; Huang, X.; Adebayo, A.A.; Abbott, C.L.; Adamowicz, S.J.; Heath, D.D. High sensitivity of 454 pyrosequencing for detection of rare species in aquatic communities. Methods Ecol. Evol. 2013, 4, 558–565. [CrossRef] 51. Zaiko, A.; Samuiloviene, A.; Ardura, A.; Garcia-Vazquez, E. Metabarcoding approach for nonindigenous species surveillance in marine coastal waters. Mar. Pollut. Bull. 2015, 100, 53–59. [CrossRef] 52. Comtet, T.; Sandionigi, A.; Viard, F.; Casiraghi, M. DNA (meta) barcoding of biological invasions: A powerful tool to elucidate invasion processes and help managing aliens. Biol. Invasions 2015, 17, 905–922. [CrossRef] 53. Kelly, R.P.; Port, J.A.; Yamahara, K.M.; Martone, R.G.; Lowell, N.; Thomsen, P.F.; Mach, M.E.; Bennett, M. Environmental monitoring. Harnessing DNA to improve environmental management. Science 2014, 344, 1455–1456. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Journal

Applied Sciences – Multidisciplinary Digital Publishing Institute

Published: Aug 21, 2019

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