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Article

A Comparative Analyzing of Zooplankton Community Diversity in Surface Layer Water of Reservoir Via eDNA Metabarcoding and Microscopy

by
Chang Woo Ji
1,
Hye-Ji Oh
2,
Kwang-Hyeon Chang
2,
Young-Seuk Park
3 and
Ihn-Sil Kwak
1,4,*
1
Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea
2
Department of Environmental Science and Engineering, Kyung Hee University, Yongin 17104, Korea
3
Department of Biology, Kyung Hee University, Seoul 02447, Korea
4
Department of Ocean Integrated Science, Chonnam National University, Yeosu 59626, Korea
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(10), 797; https://doi.org/10.3390/d14100797
Submission received: 29 August 2022 / Revised: 16 September 2022 / Accepted: 22 September 2022 / Published: 25 September 2022
(This article belongs to the Section Freshwater Biodiversity)
Browse Figures
Versions Notes

Abstract

:
We compared two sampling methods, eDNA metabarcoding and microscope identification (MSI), for the analysis of zooplankton diversity in reservoirs with its inflow and outflow streams. The dynamic patterns of Cladocera and Rotifera at different time points were similar between the two sampling methods, but there was a slight difference in the Copepoda. Specifically, the members of the Copepoda subclass could not be easily classified using the MSI method, whereas eDNA metabarcoding could detect minor taxa of Cladocera and Rotifera. Upon comparing the list of zooplankton communities in Korea with the gene database of NCBI, only ~56% of the zooplankton genera reported in Korea could be detected based on the 18S rRNA gene. However, eDNA metabarcoding detected a more diverse range of zooplankton despite the lack of genetic information. As water temperature increased after May, the zooplankton diversity decreased according to the MSI method but increased according to the eDNA metabarcoding method. Although eDNA metabarcoding has some limitations, it was able to detect a wider diversity of zooplankton compared to the MSI. eDNA metabarcoding provides a more reliable means to identify zooplankton.

Graphical Abstract

1. Introduction

Zooplankton occupies an intermediate position in the aquatic food web and plays a crucial role in energy transfer to macroinvertebrates or fish. Additionally, zooplankton could be used as an indicator to evaluate aquatic ecosystems due to their short life cycle. Particularly, the species composition, population density, and productivity of zooplankton communities rapidly respond to the direct or indirect influences of abiotic and biotic environmental factors [1,2].
However, despite the importance of zooplankton in aquatic communities, accurate classification of zooplankton species is notoriously difficult due to the occurrence of cryptic sibling species. Because of these ambiguities, the morphological classification of copepods is quite challenging, especially during the larval stage. Therefore, the identification of zooplanktonic species requires taxonomic expertise and extensive datasets [3].
The environmental DNA (eDNA) metabarcoding technique is being used as an alternative and efficient tool for the assessment of community structure and habitat condition [4,5]. This technique was initially utilized in sediments to detect the DNA of animals and plants but has since been applied to various terrestrial and aquatic samples [6]. eDNA sequencing technologies provide several advantages for the assessment of aquatic ecosystems, such as the classification of a broader taxonomic range, high detection sensitivity, and time efficiency, in addition to not requiring vast taxonomic expertise [7,8]. These advantages have made eDNA sequencing a uniquely helpful technique for evaluating aquatic habitats and ecosystems [9,10].
A standard practice when applying eDNA metabarcoding is to compare the number and diversity of taxa detected via morphological classification [11,12]. In studies of fish and amphibians, eDNA metabarcoding detected more species or showed higher overlap rates than traditional methods [13,14]. Djurhuus et al. [15] compared the eDNA metabarcoding and morphological taxonomic identification of zooplankton along with filtering methods. However, the authors only compared whether the different methods detected the presence or absence of certain taxa and did not consider the relative frequency of taxa within the zooplankton community. Most recently, Rourke, Fowler, Hughes, Broadhurst, DiBattista, Fielder, Wilkes Walburn, and Furlan [5] analyzed 63 studies and reported that eDNA sampling could have positive relationships with fish abundance and/or biomass, meaning that this method could be potentially used to monitor fish populations. In the case of zooplankton, eDNA frequency increased after 3.5 days of increased abundance of Daphnia magna in mesocosms [16]. Several studies have compared the number of zooplankton taxa detected by different methods using Venn diagrams [15,17,18]. However, few studies have compared the zooplankton abundances and frequencies obtained using microscope-assisted identification and eDNA sampling in actual aquatic habitats. Harvey, et al. [19] reported that the correlation between the morphological taxonomic method and eDNA frequency was significant for some taxa, whereas other taxa (e.g., Diplostraca) had a gene-specific relationship. This result suggests that primer selection is important for eDNA research, and the frequency between the two methods may differ depending on the type of primer. Here, we selected the widely used 18S rRNA V9 primer [20,21] to characterize the structure of zooplankton communities in aquatic ecosystems.
The objectives of this study were to (i) analyze zooplankton communities via microscope identification and eDNA metabarcoding, (ii) compare the relative frequencies obtained using these two methods using statistical analysis, and (iii) determine whether the 18S rRNA V9 primer is feasible for the characterization of zooplankton communities. Whether eDNA can reflect seasonal and spatial differences is rarely studied [22]. Our study used water sampled from a reservoir with inflow and outflow streams from March to September to determine whether eDNA is capable of temporal-spatial interpretation of zooplankton communities. Next, we compared the results of microscope identification and eDNA metabarcoding of zooplankton taxa, as well as the effects of different environmental factors on the outcomes of these two approaches. Only one primer (the 18S rRNA V9 primer) was used to detect zooplankton communities. However, other primers (e.g., mitochondrial cytochrome-c-oxidase subunit-I) were analyzed using the National Center for Biotechnology Information (NCBI) database to compare their performance to that of the 18S rRNA V9 primer pair. Collectively, our findings could enable the creation of novel strategies for the effective monitoring of not only zooplankton but also other crucial members of aquatic ecosystems, such as phytoplankton, macroinvertebrates, and fish.

2. Materials and Methods

2.1. Study Area, Sampling, and Microscope-Assisted Identification

Zooplankton sampling was conducted from March to September 2021 at three study sites in the Singal reservoir (37°14′30.4″ N 127°05′45.4″ E, maximum depth of 6.1 m), a water body with an inflow stream and an outflow stream (Figure 1). Ten liters of water were collected from the surface using a van Dorn water sampler to compare zooplankton communities according to the sampling site and method. The collected water was filtered using the same zooplankton net (diameter 27 cm, mesh 60 μm) and three samples were obtained by repeating the same method at each sampling. The collected samples were fixed to a final concentration of 5% using formalin in the field for microscope identification and then transported to an indoor laboratory. The sample was concentrated in the laboratory to obtain an appropriate number of zooplankton individuals, and a 1 to 5 mL subsample was taken to identify and count zooplankton species at the genus and species level using an Olympus BX51 microscope (Olympus, Japan). For zooplankton identification, [23,24] were referenced, and a Sedgwick Rafter cell counting chamber was used for the individuals counting. Zooplankton population density was converted to individuals per volume (inds./L). The total number of zooplankton samples were 21 samples (3 sampling site with 7 months).
We also measured several environmental variables of the surface layer water (approximately the top 50 cm) to assess water quality. Water temperature (Temp., °C), dissolved oxygen (DO, mg/L), pH, and conductivity (Cond., µS/cm) were measured on-site using portable equipment (Model: YSI Professional Plus, OH, USA). For total phosphorus (TP), total nitrogen (TN), and Chl-a concentration measurements, water samples were first filtered through a 0.45 μm pore-size membrane (Model: Advantec MFS membrane filter, Dublin, California, USA) and measurements were then performed using a UV spectrophotometer. Total organic carbon (TOC) concentrations were measured using a TOC analyzer (Model: Vario TOC cub, Langenselbold, Germany) through an 850 °C combustion catalytic-oxidation method. Suspended solid (SS) were filtered through the GF/C filter (Whatman) according to [25].

2.2. eDNA Extraction and Analytical Procedures

Two liters of water at each sampling site were divided into one-liter sub-samples and filtered through a 0.45 μm mesh net to extract eDNA. eDNA was extracted from all membrane filters using the DNeasy Blood & Tissue kit (Qiagen) with some modifications, as previously described [26]. Before the DNA extraction, the working space was thoroughly cleaned with 70% ethanol, and UV light was used on the experimental area and pipettes to prevent cross-contamination. The quantity, quality, and integrity of the extracted eDNA were measured using a microplate reader (Thermo Fisher Scientific) and a 1.5% agarose gel, and sequencing libraries were prepared according to the Illumina 18S metagenomic sequencing library protocol (Illumina). An 18S rRNA V9 region specific primer pair (1380F–1510R) was used for targeting the eukaryotic community [27]. The first PCR amplification was carried out under the following conditions: 95 °C for 3 min followed by 35 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s. Final extension was carried out for 5 min at 72 °C. The PCR products were then purified and size-selected using AMPure XP beads (Beckman Coulter) and the samples were separated in an agarose gel to confirm whether non-specific products were obtained. A second PCR was conducted for barcode sequence indexing using the NexteraXT index Kit v2 set A (Illumina) and AccuPower PCR premix (Bioneer). The second PCR was carried out under the following conditions: 95 °C for 3 min; 8 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, and an extension step for 5 min at 72 °C. The secondary PCR product was also purified using the same protocol as with the first PCR and quantified to normalize the concentration of each sample using a Qubit 3.0 fluorometer (Invitrogen) with high-sensitivity (HS) assay kits (Invitrogen). The library was then mixed with 20% PhiX (Illumina) and iSeq 100 reagent v2 (300-cycle) for sequencing using an Iseq100 system (Illumina).
The raw sequences were processed using the QIIME2 2021.4 pipeline to remove primers, quality checking, denoising, paired-end merging, and removing chimeras [28]. Taxonomic assignment of amplicon sequence variants (ASVs) was performed using the NCBI GenBank database and our custom reference database using the BLASTN algorithm. The ASVs were assigned to species based on a 97% identity threshold and were used to analyze the community structure of sampling sites. Shannon information entropy was used for calculating the diversity of zooplankton community [29]. Hierarchical clustering was conducted to characterize the community structure of the sampling sites. Through hierarchical clustering, the sampling sites were grouped by the similarity of their zooplankton community with the Ward linkage method [30] using the Bray–Curtis distance [31]. Additionally, intracross correlation (ICC) was used to identify the similarities between the two survey methods [32,33]. The relative frequencies of individual numbers and ASVs, which were measured by two different methods, were used for hierarchical clustering and ICC at the order level. The ‘vegan’ package was used for hierarchical clustering [34] and “ICC” for ICC [35] in R [36] (v.4.1.0; https://www.r-project.org/ (accessed on 1 May 2022)).

3. Results

3.1. Environmental Variables of Sampling Sites

Table 1 indicates the estimated values of environment variables at each sampling. The water temperature was below 20 °C in March and April and increased to more than 25 °C from June (Table 1). DO was relatively higher in March and April than other months. The pH varied from 7.4 to 9.5 depending on the sampling sites, and the conductivity was relatively higher in March, April, and May than other months. TOC and TN were higher at the reservoir and outflow stream than in the inflow stream, whereas TP varied depending on the sampling sites and time.

3.2. Comparative Community Analysis with eDNA Metabarcoding and MSI

The composition of the zooplankton communities varied depending on the two identification methods in the Singal reservoir. We analyzed samples from 3 sampling sites over 7 months, for a total of 21 samples (Table 2). eDNA metabarcoding appeared to be suitable for detecting more organisms. The number of Coppepoda and Rotifera according to taxon was higher with eDNA metabarcoding than MSI, whereas the number of Cladocera was higher with MSI than eDNA sampling. At the genus level, MSI could detect only one unidentified taxon of Copepoda, but eDNA sampling could detect nine genera of Copepoda. The detected numbers of Rotifera were higher with eDNA sampling than MSI. eDNA sampling could detect 43 genera, 33 families, and 7 orders of Rotifera, whereas the MSI method could detect only 35 genera, 28 families, and 6 orders of Rotifera. The relative frequency of each zooplankton taxon differed according to the sampling methods. The relative frequencies of Copepoda, Cladocera, and Rotifera detected by the eDNA method were 60.8%, 0.9%, and 38.3%, respectively. In the MSI sampling method, the relative frequencies were 30.7%, 18.8%, and 50.5%, respectively.
The ASV frequency and individual number (no./mL) of Copepoda were relatively low at the inflow stream (Figure 2). The ASV frequency exceeded 50 only in March, and was less than 50 in other months. The individual number of Copepoda, which was measured by MSI counting, was also not high. In April and May, the individual number of Copepoda were 4.4 and 2.4 no./mL, respectively. However, the ASV frequency and individual number of Copepoda were relatively high at the reservoir and outflow stream. In the reservoir, the ASV frequencies of Copepoda were 736 and 2080 in August and September, respectively. The observed numbers by MSI counting were also higher in July, August, and September. Contrary to the inflow stream, the densities of Copepoda in the outflow stream were relatively high in both survey methods, even though it was a stream, not a reservoir. Additionally, the two survey methods did not detect any zooplankton in common, and only eDNA sampling detected a very low density (nine ASV) of Copepoda in August.
The densities of Cladocera were relatively lower than other zooplankton in both sampling methods (Figure 2). In the inflow stream, 5 ASV frequencies of Cladocera were detected in May only by eDNA sampling, and 2.2 and 12 no./mL of Cladocera were detected in April and August by MSI sampling, respectively. The densities of Cladocera in the reservoir and outflow stream were also relatively higher than the inflow stream in both sampling methods. According to the eDNA sampling, the frequency of Cladocera was the highest in May, whereas the density was highest in September according to the MSI method.
The Increase/decrease patterns of Rotifera were similar between the two survey methods (Figure 2). In the inflow stream, both survey methods showed a pattern of increasing density from June and decreasing in August. In the reservoir, each survey method showed the lowest density in April and the highest in June. The highest density of Rotifera in the outflow stream, inflow stream, and reservoir was observed in June. A total of 78 Rotifera were detected with MSI sampling, whereas 256 ASVs of Rotifera were detected with the eDNA metabarcoding method. No members of the phylum Rotifera were detected in August with either survey method.
The diversity index of eDNA metabarcoding was relatively higher than that of MSI (Figure 2). The average diversity indices of zooplankton detected by eDNA and MSI sampling for 8 months in the inflow stream were 2.28 and 1.34, respectively. In the reservoir, the average diversity indices obtained with eDNA and MSI sampling were 1.75 and 1.58, respectively. The average diversity index of the outflow stream was 1.77 and 1.17 with eDNA and MSI sampling, respectively. There was no statistically significant difference in the diversity indices between the eDNA and MSI sampling at any of the sampling sites. However, the two survey methods exhibited local differences in diversity indices. In the inflow stream, the diversity index of MSI was higher than the diversity of eDNA sampling in March and April. Moreover, the diversity index of the eDNA survey increased, but the diversity index of the MSI survey decreased after May. The change in the eDNA diversity index of the reservoir was similar to that of the inflow stream. Specifically, it also increased rapidly to over 2.0 after May. The MSI diversity index of the reservoir was slightly different from that of the inflow stream and had increased by more than 2.0 since July. In the outflow stream, the eDNA diversity index increased after May but decreased after July. A similar pattern was observed in the MSI diversity index, but the increase was lower than that in the eDNA diversity index. Since July, the relatively low diversity index of outflow streams appeared to result from artificial disturbance. As mentioned above, there was a river maintenance construction in the outflow stream in summer, which may have affected our results.

3.3. Characterization of Zooplankton Communities and Their Relationships with the Sampling Methods

The 20 sampling sites were divided into 3 groups according to zooplankton community structure using hierarchical clustering (Figure 3). The outflow in August was excluded because the number of individuals and ASV frequency was extremely low, which could have caused bias in the analysis. The vertical axis of the hierarchical clustering represented the sampling sites, whereas the horizontal axis represented the zooplankton community and environmental variables. The inflow streams were mainly grouped in group 1 (G1). In G1, Rotifera (Re) measured by eDNA and Rotifera (Rm) measured by MSI were high, showing a difference from the other two groups. At the G1 sampling site, the water temperature was high and the DO was low, whereas the opposite occurred in I4 and R3. The reservoirs and their affected outflow streams were mainly classified as group 2 (G2) and group 3 (G3). Unlike G1, the effect of copepods measured by eDNA metabarcoding (Ce) and MSI (Cm) was exceptional in G2 and G3. However, G2 had a relatively high water temperature and suspended solids (SS), whereas G3 had relatively low water temperature and SS.
To identify the relationships more clearly between the zooplankton communities measured by the two survey methods, the distance between the two was represented numerically (Figure 4). The zooplankton taxon with the shortest distance (0.37) between the two survey methods was the Ploima order, whereas the one with the longest distance (0.94) was Calanoida, a member of the Copepoda subclass. Except for Flosculariaceae, Cladocera and Rotifera showed relatively short distances compared to Copepoda. Additionally, the similarities between the two survey methods were compared using ICC. The ICC value of Anomopoda was 0.69, and it was the highest. Philodinida (0.56) and Ploima (0.56) were also over 0.5. Two orders of Copepoda, Cyclopoida, and Calanoida, were significantly low, and the ICC values were 0.19 and 0.09, respectively.

4. Discussion

Table 2 shows the number of detected orders, families, genera, and frequencies of ASVs and individuals, and reveals that eDNA metabarcoding could detect more various organisms at each Copepoda and Rotifera taxon level. Moreover, the relative frequencies of Copepoda and Rotifera were somewhat different. Additionally, each sampling method differed in relative frequency and number of Cladocera. This result shows that the two different sampling methods were inconsistent, thus highlighting the need for further improvement.

4.1. Limitations and Effectiveness of eDNA Metabarcoding

Despite the mixed results and inconsistencies summarized in Table 2, eDNA metabarcoding still has important advantages, and the key to understanding these problems can be seen in Appendix A and Appendix B. Appendix A shows the summary of the relative frequencies of the genera in each zooplankton taxon (Copepoda, Cladocera, and Rotifera) during the sampling period. The bold letters in Appendix A indicate the genera detected by both methods. In Copepoda, MSI sampling could not identify details at the genus level. As mentioned above, Bucklin et al. [3] reported that copepods could not be easily distinguished based on morphological characteristics, especially at the larval stage. Copepods were thus difficult if not impossible to identify, particularly during their nauplius stage. In contrast, ten genera were identified via eDNA metabarcoding, and therefore this method enabled the identification of more organisms compared to MSI. All major genera of Cladocera in MSI except Diaphanosoma could also be detected by eDNA metabarcoding. In Rotifera, the dominant genera Brachionus, Keratella, and Polyarthra were also detected by both methods. Furthermore, there were many cases where the two sampling methods did not match for minor genera. The red letters in the MSI genus in Appendix A indicate that the genus was found using the MSI method but no 18S rRNA sequences were available in the NCBI database. Most genera that could not be detected via eDNA metabarcoding were found to lack the corresponding genetic information of 18S rRNA. This result indicates that the lack of genetic information is a limiting factor for accurate zooplankton ecosystem surveys using eDNA metabarcoding. Harvey et al. [19] also reported that NGS databases may not include sampled taxa, which could explain the discrepancies between eDNA metabarcoding and MSI. The authors also noted that a well-established database configuration was a significant factor in determining the relative success of assessing zooplankton communities.
Furthermore, we compared the sequence list registered in the NCBI database with the list of Korean species to determine the extent to which eDNA metabarcoding could detect Korean species [37]. To compare the performance of different primers, we compared not only the number of registrations of 18S rRNA but also the registration number of the 12S rRNA, 16S rRNA, and COI genes. The genes with the highest number of registered cases were COI > 18S rRNA > 16S rRNA > and 12S rRNA, with 3.5 M, 1.3 M, 0.5 M, and 0.3 M registered sequences, respectively. The actual number of registered species was 172,650 for COI and 162,665, 108,443, and 51,602 for 18S, 16S, and 12S rRNA, respectively. A total of 54,428 species were reported in Korea [37]. Among them, 9,411 genera of COI genes were registered in the NCBI database. Similarly, the number of organisms registered with 18S, 16S, and 12S rRNA gene information was 8966, 7600, and 4404, respectively. Among the 9411 genera registered in the COI, 278, 456, 1940, and 692 were counted as phytoplankton, zooplankton, macroinvertebrates, and fish, respectively. The number of each taxon corresponds to 25.1%, 29.7%, 66.6%, and 92.3% of the registered genera in South Korea. As the taxon level increased to the family/order level, the proportion also increased. The ratio of registered phytoplankton and zooplankton of 18S rRNA at the genus level was higher than the registration ratio of COI, whereas it was relatively lower in macroinvertebrates and fish. The registration ratio of 18S rRNA at the genus level according to each above taxon was 64.5%, 56.6%, 56.1%, and 32.7%, respectively. The registration ratios of 16s rRNA were similar to those of COI. Specifically, the numbers were low for phytoplankton and zooplankton but relatively high for macroinvertebrates and fish at the genus level. The registration ratios of 12s rRNA for phytoplankton and zooplankton (1.2% and 9.3%, respectively) were significantly lower than those of the other evaluated genes. The ratio of macroinvertebrates in 12S rRNA was also lower than that of the other examined genes. Only the ratio of fish in 12S rRNA was high, reaching almost 95%.
Appendix B shows two critical factors that determine the outcome of eDNA metabarcoding sampling. (1) The genetic information of aquatic organisms is not yet sufficient, especially in phytoplankton, zooplankton, and macroinvertebrates. None of the genes had a registration rate greater than 70% in these aquatic organisms. This would explain why minor taxa could not be detected via eDNA metabarcoding, as shown in Appendix A. (2) A 18s rRNA-specific primer pair was used to detect aquatic organisms in a wide range of taxa. The primers had a lower ratio in macroinvertebrates and fish but a relatively higher ratio in phytoplankton and zooplankton than in other genes. Therefore, a combination of 18S rRNA primer pairs with COI or 16S rRNA primer pairs would be required to investigate all taxa of aquatic organisms by using eDNA metabarcoding. Particularly, the 16S rRNA primer would be the only gene that could detect cyanobacteria. The registration ratio of cyanobacteria was 87.8% at the genus level when it was investigated in the same way as in Appendix B.
In Europe, DNA barcoding is being adopted in many countries to solve the aforementioned problems [38]. Economically important groups, such as fish, take precedence among most aquatic taxa. When building barcode reference libraries, a general focus on a species or organism that is particularly relevant for the assessment of species diversity and water quality is extremely rare, especially zooplankton [38]. Recent studies have confirmed that the diatom indices obtained from eDNA metabarcoding provide similar results to those from MSI at the regional and national scales [39,40,41]. However, all these studies have highlighted the need for well-constructed reference libraries. Therefore, a high-quality database must be established to facilitate the evaluation of zooplankton communities using eDNA metabarcoding.

4.2. Comparison of Zooplankton Community Structure According to Sampling Methods

Figure 3 shows not only the characteristics of zooplankton communities but also the relationship between environmental factors and zooplankton taxa. For example, G2 and G3 have relatively higher DO and TN than G1, and the frequencies of Copepoda investigated by both methods are also high. In particular, the method investigated by eDNA metabarcoding was high. On the other hand, DO, TN, and EC were relatively low in G1, where Rotifera was dominant. The sample of relatively high-water temperatures was grouped together in G1 and G2. However, the relative frequency of Copepoda was high in G2, and the relative frequency of Rotifera showed a high in G1. These results showed that eDNA metabarcoding could track the spatial difference. The temporal variation of water temperature will be discussed later.
The increase and decrease patterns of the relative frequency and eDNA ASV of Rotifera and Cladocera were similar, whereas the dynamic patterns of Copepoda between the two methods were somewhat different (Figure 2). The reasons for these discrepancies could be inferred from Figure 4. The ICC values of Rotifera and Cladocera were relatively higher than those of Copepoda. Although Flosculariaceae had a low ICC value, unlike other Rotifera, it is considered to have little effect on the dynamic pattern because it is a non-dominant organism. According to Fleiss et al. [42], ICC values can be graded as follows: 0–0.4, Poor; 0.4–0.75, Fair to Good; and 0.75–1.0, Excellent. In this study, the ICC values of Coppepoda were not over 0.2. In contrast, the ICC values of Anomopoda, Philodinida, and Ploima were over 0.55. The monthly changes in Cladocera and Rotifera according to the two sampling methods were fair to good. The low ICC value of Copepoda is thought to be due to the low taxonomy identification capacity of MSI. As shown in Appendix A, more than 90% of copepods were classified as Cyclopidae according to the MSI method, making it difficult to classify them accurately. Although there were some differences, this result showed that the two sampling methods could be consistent. However, there were some differences and therefore we next sought to determine which method was more efficient or practical.
The optimum growth temperature for most zooplankton is approximately 20 °C [43]. In this study, the water temperature in March and April was below 20 °C; from May, it started to rise above 20 °C. Therefore, it would be natural for the diversity index of zooplankton communities to increase after May. Coincidentally, the zooplankton community’s diversity index through eDNA metabarcoding increased after May except for some outflow streams, which had artificial disturbance. However, the diversity of the traditional method, MSI, decreased after May at the inflow stream or fluctuated at the reservoir. This low diversity after May in the inflow stream according to the MSI method is thought to be because the members of the phylum Rotifera were the dominant species, whereas Copepoda was not detected at all. Therefore, eDNA metabarcoding is considered to be a more suitable measurement tool to assess zooplankton communities.
“Biological quality factors” (BQEs) [22] are a necessary condition for the assessment of ecological status. Thus, an accurate survey of the zooplankton community is a prerequisite for ecological assessment. eDNA metabarcoding has been measuring the seasonal diversity at a large scale of the ecosystem [44]. In our study, eDNA metabarcoding tracked seasonal diversity more accurately than traditional methods in small habitats, such as the Singal Reservoir. The reason for this is that the eDNA approach can detect rare species more easily than the morphological method, which shows that a more accurate ecological status assessment is advantageous by eDNA metabarcoding. Finally, the clustering patterns of both survey methods were divided into three groups (Figure 3). The inflow and outflow sites, which were stream survey points, were grouped mainly in G1, whereas the reservoir sites and some of the affected outflow sites were grouped in G2 and G3. Among the examined environmental factors, water temperature, pH, and SS were found to be high in G1, where the density of Rotifera (Re, Rm) was relatively high in both sampling methods. G2 was also a group with high water temperature, pH, and SS, but unlike G1, which mainly showed inflow streams, G2 mainly grouped lakes and outflow streams with high Copepoda density. DO, TN, Cond, and total carbon were high in reservoirs and outlets with a high density of Copepoda (Ce and CM).

5. Conclusions

We compared and analyzed zooplankton communities in reservoirs and inflow and outflow streams using eDNA metabarcoding and MSI. Although there were discrepancies between these methods in some taxa due to a lack of information on minor taxa in the NCBI database, eDNA metabarcoding could detect a wider diversity of species. The 18S V9 rRNA primer was suitable for detecting aquatic communities, such as phytoplankton, zooplankton, and benthic invertebrates but not for cyanobacteria and fish. Combining eDNA metabarcoding and microscope identification could thus provide more comprehensive insights into the zooplankton community structure at different sampling sites within the reservoir and streams. Despite some limitations for non-dominant organisms, eDNA metabarcoding exhibited superior performance for the classification of aquatic communities.

Author Contributions

Conceptualization, I.-S.K., K.-H.C. and Y.-S.P.; methodology, I.-S.K., C.W.J. and H.-J.O.; formal analysis, I.-S.K. and C.W.J.; investigation, I.-S.K., C.W.J. and H.-J.O.; visualization, I.-S.K. and C.W.J.; writing—original draft preparation, C.W.J. and I.-S.K.; writing—review and editing, all authors. 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-2018R1A6A1A03024314) and the Korea Environment Industry and Technology Institute (KEITI) through the Aquatic Ecosystem Conservation Research Program funded by the Korea Ministry of Environment (MOE) (2020003050003 and 2021003050001).

Institutional Review Board Statement

This study does not use live vertebrate animals.

Data Availability Statement

Not applicable.

Conflicts of Interest

None of the authors have any conflict of interest to declare.

Appendix A

Diversity 14 00797 g0a1 550
Figure A1. Detected genus list of zooplankton ((a) Copepoda, (b) Cladocera, (c) Rotifera) and frequencies of the genus in each taxon. Colors indicate the summation of each taxon frequency in each sampling site during the sampling period. Bold characters indicate the genus detected in both sampling methods (MSI and eDNA metabarcoding), and red characters indicate genera that do not have 18S rRNA genetic information in the NCBI database.
Figure A1. Detected genus list of zooplankton ((a) Copepoda, (b) Cladocera, (c) Rotifera) and frequencies of the genus in each taxon. Colors indicate the summation of each taxon frequency in each sampling site during the sampling period. Bold characters indicate the genus detected in both sampling methods (MSI and eDNA metabarcoding), and red characters indicate genera that do not have 18S rRNA genetic information in the NCBI database.
Diversity 14 00797 g0a1

Appendix B

Table
Table A1. Comparison of number of registered gens on NCBI with Korean species list.
Table A1. Comparison of number of registered gens on NCBI with Korean species list.
Gene12s rRNA16s rRNA18s rRNACOI
TotalGenusSpeciesTotalGenusSpeciesTotalGenusSpeciesTotalGenusSpecies
Number of NCBI registrations281,81218,98251,602544,38736,070108,4431,290,90946,397162,6653,530,05847,139172,650
Number of NCBI registrations of Korean organism44041547401760023306308966304581694112298513
No.Phytoplankton1313122511528865127412827812167
Zooplankton14390433382077986946713945623565
Macroinvertebrate976433116171765314416366891591940698150
Fish71222245688221452451323469222145
%Phytoplankton1.23.68.322.742.661.164.584.692.825.133.946.5
Zooplankton9.314.827.72234.15156.676.989.729.738.741.9
Macroinvertebrate33.549.165.958.97481.856.178.190.366.679.185.2
Fish94.998.795.791.798.295.732.758.772.392.398.295.7

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Diversity 14 00797 g001 550
Figure 1. Map of the study sites in Singal reservoir. Inflow and outflow are the stream sites connected with Singal reservoir.
Figure 1. Map of the study sites in Singal reservoir. Inflow and outflow are the stream sites connected with Singal reservoir.
Diversity 14 00797 g001
Diversity 14 00797 g002 550
Figure 2. Individual number, ASV frequencies, and diversity indices of zooplankton with two sampling methods (MSI and eDNA metabarcoding) in each sampling site and day.
Figure 2. Individual number, ASV frequencies, and diversity indices of zooplankton with two sampling methods (MSI and eDNA metabarcoding) in each sampling site and day.
Diversity 14 00797 g002
Diversity 14 00797 g003 550
Figure 3. Hierarchical clustering of 20 sampling sites according to the zooplankton communities and environmental variables and cluster distance according to the Bray–Curtis dissimilarity. The vertical axis indicates the sampling site (I: inflow stream, R: reservoir, O: outflow stream), and the numbers mean the sampling month. The horizontal axis indicates the environmental variables and zooplankton communities. Ce and Cm are the relative frequencies of Copepoda, which are measured with MSI and eDNA metabarcoding, respectively. Le and Lm are the relative frequencies of Cladocera and Re and Rm are the relative frequencies of Rotifera.
Figure 3. Hierarchical clustering of 20 sampling sites according to the zooplankton communities and environmental variables and cluster distance according to the Bray–Curtis dissimilarity. The vertical axis indicates the sampling site (I: inflow stream, R: reservoir, O: outflow stream), and the numbers mean the sampling month. The horizontal axis indicates the environmental variables and zooplankton communities. Ce and Cm are the relative frequencies of Copepoda, which are measured with MSI and eDNA metabarcoding, respectively. Le and Lm are the relative frequencies of Cladocera and Re and Rm are the relative frequencies of Rotifera.
Diversity 14 00797 g003
Diversity 14 00797 g004 550
Figure 4. The Bray–Curtis distance and intracross correlation values of the order of zooplankton between the sampling methods.
Figure 4. The Bray–Curtis distance and intracross correlation values of the order of zooplankton between the sampling methods.
Diversity 14 00797 g004
Table
Table 1. Environment variables at each sampling sites in Singal reservoir.
Table 1. Environment variables at each sampling sites in Singal reservoir.
MonthLocationTemp.
(°C)		DO
(mg/L)		pHCond. (μS/cm)Chl-a
(μg/L) 		TOC (mg/L)TN (mg/L)TP (mg/L)SS (mg/L)
3Inflow15.213.68.868635.73.94.40.065.2
Reservoir12.714.08.079614.33.66.60.032.2
Outflow13.414.57.41,430 14.44.77.30.4814.7
4Inflow14.913.58.4670 14.33.63.80.055.5
Reservoir14.711.47.6745 8.04.35.60.030.8
Outflow20.314.49.5769 14.94.45.00.091.4
5Inflow22.08.58.5625 22.54.23.70.065.8
Reservoir20.39.57.6708 10.44.66.20.022.4
Outflow21.28.28.01,130 50.66.96.40.385.7
6Inflow24.511.18.4655 13.54.24.30.079.9
Reservoir25.410.59.1632 19.54.74.60.041.2
Outflow25.66.78.6596 17.14.44.30.042.7
7Inflow25.69.18.7607 11.43.83.90.041.8
Reservoir28.813.68.2520 22.24.24.40.048.6
Outflow25.49.08.6485 23.34.64.20.044.6
8Inflow26.28.78.5424 13.36.32.80.1111.1
Reservoir29.68.78.3652 43.65.25.90.0415.3
Outflow26.87.28.31,050 7.07.77.41.1126.4
9Inflow25.911.68.4607 20.24.73.90.0511.1
Reservoir26.010.78.4546 16.54.33.60.027.7
Outflow25.07.28.1545 17.84.43.60.029.7
Table
Table 2. Detected number of order, family, genus, and frequencies (%) of ASV and individuals according to each sampling method in the major taxa of zooplankton.
Table 2. Detected number of order, family, genus, and frequencies (%) of ASV and individuals according to each sampling method in the major taxa of zooplankton.
OrderFamilyGenusRelative
Frequency (%)
eDNAMSIBotheDNAMSIBotheDNAMSIBotheDNAMSI
Copepoda32242292060.830.7
Cladocera1212426630.918.8
Rotifera43320151327191638.350.5
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Ji, C.W.; Oh, H.-J.; Chang, K.-H.; Park, Y.-S.; Kwak, I.-S. A Comparative Analyzing of Zooplankton Community Diversity in Surface Layer Water of Reservoir Via eDNA Metabarcoding and Microscopy. Diversity 2022, 14, 797. https://doi.org/10.3390/d14100797

AMA Style

Ji CW, Oh H-J, Chang K-H, Park Y-S, Kwak I-S. A Comparative Analyzing of Zooplankton Community Diversity in Surface Layer Water of Reservoir Via eDNA Metabarcoding and Microscopy. Diversity. 2022; 14(10):797. https://doi.org/10.3390/d14100797

Chicago/Turabian Style

Ji, Chang Woo, Hye-Ji Oh, Kwang-Hyeon Chang, Young-Seuk Park, and Ihn-Sil Kwak. 2022. "A Comparative Analyzing of Zooplankton Community Diversity in Surface Layer Water of Reservoir Via eDNA Metabarcoding and Microscopy" Diversity 14, no. 10: 797. https://doi.org/10.3390/d14100797

APA Style

Ji, C. W., Oh, H. -J., Chang, K. -H., Park, Y. -S., & Kwak, I. -S. (2022). A Comparative Analyzing of Zooplankton Community Diversity in Surface Layer Water of Reservoir Via eDNA Metabarcoding and Microscopy. Diversity, 14(10), 797. https://doi.org/10.3390/d14100797

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