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Article

Spatial and Temporal Variations in Phytoplankton Community in Dianchi Lake Using eDNA Metabarcoding

by
Yuanyuan Lin
1,2,3,
Jingge Xu
1,3,
Liang Shen
1,2,3,
Xiaohua Zhou
4,
Liwei He
4,5,
Zheng Zhao
2 and
Shan Xu
1,2,3,*
1
School of Agronomy and Life Sciences, Kunming University, Kunming 650214, China
2
Kunming Dianchi Lake Environmental Protection Collaborative Research Center, Kunming University, Kunming 650214, China
3
Yunnan Collaborative Innovation Center for Plateau Lake Ecology and Environmental Health, Kunming 650214, China
4
School of Chemistry and Chemical Engineering, Kunming University, Kunming 650214, China
5
Yunnan Dali Research Institute, Shanghai Jiao Tong University, Dali 671000, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(1), 32; https://doi.org/10.3390/w16010032
Submission received: 9 November 2023 / Revised: 12 December 2023 / Accepted: 13 December 2023 / Published: 21 December 2023
Browse Figures
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Abstract

:
The growth and reproduction of phytoplankton are closely associated with the changes of water environment; thus, phytoplankton have been taken as environmental indicator organisms and provided references for water environment protection. However, the phytoplankton community characteristics of Dianchi Lake (a seriously polluted lake in China) are unclear under the background of the cumulative effects of historical pollutants and current control measures, and environmental DNA (eDNA) metabarcoding monitoring has rarely been applied in phytoplankton research at Dianchi Lake. Therefore, this study investigated the temporal and spatial characteristics of phytoplankton community and the environmental stressors of Dianchi Lake via eDNA metabarcoding monitoring. A total of 10 phyla, 22 classes, 50 orders, 82 families, 108 genera and 108 species of phytoplankton were detected, and distinct temporal and spatial variations in the phytoplankton community (e.g., ASV number, dominant taxon, the relative abundance) were observed in Dianchi Lake. Microcystis dominated the prokaryotic phytoplankton community from the dry period to the wet period, but interestingly, the first dominant cyanobacteria genus was changed from Microcystis (dry period) to Planktothrix (wet period). Cryptophyta dominated in the eukaryotic phytoplankton community from the dry period to the wet period, and eukaryotic-phytoplankton-dominant genera included Cryptomonas, Aulacoseira, Plagioselmis and others. A temporal–spatial heterogeneity of the relationships between the phytoplankton community and environmental factors was shown in Dianchi Lake. Dissolved oxygen was the crucial environmental stressor influencing the phytoplankton community structure in Dianchi Lake during the dry period, while pH was the crucial one during the wet period. The impacts of total phosphorus and nitrogen also showed differences at different periods. This research provides an interesting perspective on phytoplankton diversity monitoring and the health assessment and restoration of Dianchi Lake.

1. Introduction

Phytoplankton, comprising prokaryotes (Cyanobacteria) and eukaryotic algae, are recognized as one of the most important groups in aquatic ecosystems. They provide foods for higher-trophic-level organisms, affecting the food web, carbon cycle, nutrient element cycle and community structure of the whole aquatic ecosystem directly or indirectly [1,2]. The phytoplankton diversity varies under different water-quality conditions; thus, phytoplankton are often taken as valuable environmental indicators for the changes of water ecosystems [3,4,5]. The variations in phytoplankton diversity are mainly manifested as continuous changes in species composition, dominant taxon, relative abundance and the succession of the phytoplankton community, which are closely related to the fluctuations in environmental factors such as water temperature, dissolved oxygen, chemical oxygen demand and pH, indicating the health status of aquatic ecosystems and providing important references for water ecological environment protection [6,7,8,9].
Situated at the middle of the Yunnan-Guizhou Plateau and downstream of Kunming city, China, Dianchi Lake is an inland urban lake; there are 35 rivers entering the lake, but only one river leaving the lake. It lies within the subtropical plateau monsoon climate zone and is divided into two distinct periods, dry and wet, of which the wet period (May–October) accounts for 89% of the total annual precipitation [10,11]. Dianchi Lake is of great significance in the natural environment and social development of Kunming city and has other substantial economic values. However, Dianchi Lake has been a typical lake which is subject to eutrophication and has been considered as top priority task for lake governance in China since the 1990s due to its serious pollution situation [12]. Although the implementation of multiple water pollution control measures such as returning the farmland to the lake, intercepting the pollution and removing the silt and diverting water have made the water pollution in Dianchi effectually regulated and have improved the water quality (maintaining it in Class IV in the last 5 years, data from Kunming Municipal Environmental Monitoring Center), Dianchi Lake remains an eutrophication lake, and harmful cyanobacteria blooms have occurred frequently for the past few years [10,13,14]. The protection and elevation of the water ecological environment remain long and arduous tasks.
With the continuous outbreak of cyanobacteria blooms in Dianchi Lake, the phytoplankton diversity of Dianchi Lake has attracted more and more attention, and the characteristics and causes of the succession of phytoplankton communities have been continuously explored [15,16,17,18,19,20]. But as the impacts of increasing anthropogenic activities, such as the discharges of domestic and industrial sewage or the application of chemical fertilizers and pesticides, progressively increase impervious surface areas, artificial water diversion and multiple water pollution control measures, the habitat and biological communities of Dianchi Lake are constantly changing [21,22,23,24]. Therefore, it is necessary to study the spatial and temporal characteristics of the phytoplankton community structure and explore the relationship with environmental factors in Dianchi Lake under the background of the cumulative effects of historical pollutants and current control measures, which can provide important references for the management and conservation of Dianchi Lake.
Phytoplankton are usually monitored by appraisers based on their morphological characteristics, which is difficult to monitor on a large scale as this method is expensive, time-consuming and has a low species resolution [1,25]. Environmental DNA (eDNA) metabarcoding has provided an effective method for the monitoring of aquatic biodiversity in recent years [26,27,28,29]. According to the differences in DNA fragments between species, eDNA metabarcoding discloses the environmental biome structure rapidly and accurately, which vastly promotes the efficiency of phytoplankton monitoring, and has been widely applied in studying phytoplankton diversity [30,31,32,33,34]. However, eDNA metabarcoding has rarely been applied to Dianchi Lake; Zhang et al. [35] discussed the precision of eDNA metabarcoding in the monitoring of eukaryotic algae and identified 75 genera of eukaryotic algae in Dianchi Lake, and Lin et al. [36] explored the distribution patterns of phytoplankton and the influence factors in Dianchi Lake and its three inflow rivers in the intermediate water season. Thus, it is of great significance to point out the spatial and temporal characteristics of phytoplankton diversity using eDNA metabarcoding technology in Dianchi Lake. Here, using the water from Dianchi Lake, this study investigated the spatial and temporal variability of the Dianchi Lake phytoplankton through eDNA metabarcoding and the key environmental stressors impacting on the phytoplankton community structure were found. This study may provide baseline information for the improvement of Dianchi Lake and the protection of its aquatic biodiversity.

2. Methods

2.1. Survey Region and Sampling

Located at 102°29′ E–103° 01′ E, 24°29′ N–25°28′ N, Dianchi Lake is an inland lake in the central region of the Yunnan-Guizhou Plateau, China, belonging to the Jinsha River system in the Yangtze River Basin. The lake is slightly arched, covers about an area of 330 km2, at an average water depth of approximately 5 m. The area where the lake is located is characterized by a monsoon climate on a tropical plateau, dominated by the southwest wind year-round, with distinct dry and wet periods, and the wet period is mainly concentrated from May to October. The lake is located downstream and southwest of Kunming City and has a great deal of pollutants, such as domestic, industrial and agricultural effluents, which are dumped into the lake with the rapid population growth and urbanization; thus, Dianchi Lake has become a typical lake which is subject to eutrophication, and the water ecological environment and biodiversity have become more and more fragile [14,37,38]. The management and protection of the ecological environment of Dianchi Lake are the vital environmental cornerstone to ensuring a happy life for Kunming people. Therefore, this study concentrated on Dianchi Lake, and the monitoring sites (D1–D8) are shown in Figure 1. Surface water samples were gathered in March (dry period) and September (wet period) in 2021, respectively. Two liters of water was gathered at the monitoring site, and three biological replicates were collected per site. A volume of 1 L of water sample was applied to analyze environmental factors. The other liter of water sample was filtered through a 0.45 μm hydrophilic nylon membrane (MilliporeSigma, Burlington, MA, USA) within 24 h of sampling, and ddH2O was used as a control sample. The filter membranes were stored at −80 °C until eDNA extraction.

2.2. Environmental Factors Analysis

Environmental physicochemical factors including ammonium nitrogen (NH4+), conductivity (C), chlorophyll-a (Chla), chemical oxygen demand (COD), dissolved oxygen (DO), pH, total nitrogen (TN), total phosphorus (TP) and water temperature (WT) were measured for each monitoring site. DO, pH and WT were tested by a YSI water quality analyzer in situ (YSI Incorporated, Yellow Springs, OH, USA); the analysis of other environmental factors was carried out in accordance with standard protocols [39].

2.3. eDNA Extraction, Polymerase Chain Reaction (PCR) Amplification and Sequencing

eDNA in the filter membrane was extracted using the DNeasy PowerWater Kit (QIAGEN, Dusseldorf, Germany) according to the manufacturer’s protocols, and the purity and concentration were detected by a [email protected] Fluorometer (Thermo Scientific, Waltham, MA, USA) and agarose gel electrophoresis; then, the eDNA samples were diluted and used as templates. Using the V3 primers (341F: 5′-ACCTACGGGRSGCWGCAG-3′; 518 R: 5′-GGTDTTACCGCGGCKGCTG-3′ [40]), the 16S rDNA V3 hypervariable region targeting cyanobacteria was amplified, and using the V9 primers (1380F: 5′-TCCCTGCCHTTTGTACAC-3′; 1510R: 5′-CCTTCYGCAGGTTCACCTAC-3′ [41]), the 18S rDNA V9 hypervariable region targeting eukaryotic phytoplankton was amplified. Unique 12 bp nucleotide fragments (barcode) were added to the 5′-ends of the forward primers (GenScript, Nanjing, China). The PCR procedures were as follows: a beginning denaturing step at 98 °C for 30 s, then 30 cycles of denaturing at 98 °C for 15 s, 62 °C for 15 s for annealing and extending it 30 s at 72 °C with an ultimate extension of 5 min at 72 °C. Taking the negative control as a template, negative PCR was simultaneously performed at every PCR process. PCR products were purified using a Zymoclean Gel Recovery Kit (D4008), and quantified using a [email protected] Fluorometer. The Illumina TruSeq DNA PCR-free Sample Prep Kit (FC-121-3001/3003, Illumina, San Diego, CA, USA) was used to construct the sequencing library. Paired-ends were sequenced (2 × 300 bp) on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) following the manufacturer’s protocols.

2.4. Bioinformatics

According to the QIIME2 pipeline [42], the raw data were analyzed and the main information processing steps included: (1) Using the “split_libraries.py” script with “-w 50 -s 25 -l 100 -M 3 -e 2”, the raw sequences were processed to remove low-quality sequences and distinguished by unique sample tags; (2) amplicon sequence variants (ASVs) were produced based on single-nucleotide differences in vsearch, and the taxonomies of ASVs were analyzed by the Greengenes database (http://greengenes.secondgenome.com/) and PR2 (release 4.12.0) databases for prokaryote and eukaryote communities, respectively [36]; the nomenclature of phytoplankton ASVs referred to the book “The Freshwater Algae of China, Systematics, Taxonomy and Ecology” [43] since the research region was located in Yunan China; (3) only ASVs with total reads >10 and presented simultaneously in at least two replicates were kept as high-confidence taxa and used to produce the ASV table; (4) based on the ASV table, the phytoplankton community was statistically analyzed by the “vegan” package in R software (3.5.3) using methods such as the Shannon–Wiener diversity index, a principal coordinate analysis, a redundancy analysis (RDA), Spearman’s correlation analysis and the Mantel test, and we used “ggplot2” and “Origin 2020b” to draw visualization mapping. The McNaughton index (Yi = ni/N × fi) was applied to calculate the genera with an extremely high relative sequence abundance, where Yi is the dominance index of the ith phytoplankton taxon (genus with Yi >  0.02 is dominant), N is the total number of phytoplankton reads detected, ni is the number of read of the ith phytoplankton taxon, and fi is the frequency of occurrence of the ith phytoplankton taxon [44,45].

3. Results

3.1. Sequencing Results and Phytoplankton Community Profile

After high-throughput sequencing, a total of 1,068,739 high-quality (>Q20) reads/1396 prokaryotic ASVs and 453,493 high-quality (>Q20) reads/1379 eukaryotic ASVs were obtained for the 16S-V3 assays and 18S-V9 assays, respectively. The rarefaction curves saturated, indicating the sequencing data sufficiently covered the taxonomic information in the eDNA samples (Figure S1). A total of 526 ASVs (89 prokaryotic/437 eukaryotic) were classified into the phytoplankton community and kept for further analysis in the study; 289 of these ASVs could be annotated to the genus level above, covering 10 phyla, 22 classes, 50 orders, 82 families, 108 genera and 108 species.

3.2. Spatial and Temporal Distributions of Phytoplankton Community in Dianchi Lake

From 89 prokaryotic ASVs, nine cyanobacterial genera were identified. Figure 2 shows the spatial and temporal variations in the cyanobacteria community in Dianchi Lake. In the dry period, Microcystis dominated in the cyanobacteria community, the reads were accounting for about 80% of the total cyanobacterial reads at each site, and the rest of cyanobacterial reads belonged to Dolichospermum, Woronichinia, Synechococcus, Pseudanabaena, Planktothrix and Phormidium. While in the wet period, Planktothrix and Microcystis dominated in the cyanobacteria community. But interestingly, except for D8, the genus with the largest relative abundance at each site was Planktothrix (accounting for about 50–80% of the total cyanobacterial reads), and the second was Microcystis.
From 437 eukaryotic phytoplankton ASVs, 9 phyla, 19 classes, 45 orders, 74 families, 99 genera and 106 species were detected. Distinct spatial and temporal distribution characteristics of eukaryotic phytoplankton community were exhibited among sampling sites (Figure 3). In the dry period, most of the eukaryotic phytoplankton reads belonged to Cryptophyta, over 65% of the total reads at each site, followed by Chrysophyta. In the wet period, Cryptophyta also dominated in the eukaryotic phytoplankton community (about 35–65% of the total reads at each site), following by Bacillariophyta.

3.3. Temporal Variation of Phytoplankton Diversity

There were significant differences in the dominant genera (Yi > 0.02) between the dry period and wet period in Dianchi Lake (Figure 4). The cyanobacterial dominant genera included Microcystis, Dolichospermum, Woronichinia, Synechococcus, Pseudanabaena and Planktothrix, and among these, Microcystis dominated the phytoplankton community from the dry period to the wet period. Interestingly, Microcystis’s relative abundance was much greater in the dry period than in the wet period, while the relative abundance of Planktothrix was the opposite. The eukaryotic phytoplankton dominant genera in Dianchi Lake included Cryptomonas, Aulacoseira, Plagioselmis, Teleaulax, Paraphysomonas, Thalassiosira, Ceratium, Cyclotella and Stoeckeria. The relative abundances of Cryptomonas, Plagioselmis, Teleaulax and Ceratium were greater in the dry period than in the wet period, while it was the reverse for the relative abundances of Aulacoseira, Paraphysomonas, Thalassiosira, Cyclotella and Stoeckeria.
Clear temporal distribution characteristics of the phytoplankton diversity were shown by the diversity analysis. A significant difference could be observed in the Shannon–Wiener index between the dry period and wet period (Wilcoxon tests p < 0.0001, Figure 5a). A clear partitioning of the phytoplankton community structure between the dry period and wet period was also exhibited by the PCoA analysis (Figure 5b) and PERMANOVA tests (R2 = 0.6876, p < 0.001), while the sampling sites from each period were grouped together, respectively. As for spatial variations, PERMANOVA tests (Table S1, p > 0.1) showed that there were no significant differences among different sites in both the dry and wet periods.

3.4. Relationships between the Phytoplankton Community and Environmental Factors

Correlation analyses were performed to elucidate the relationships between environmental factors and phytoplankton community. Certain environmental factors were significantly associated with the relative abundances of specific genera (|r|> 0.7, p < 0.001, Figure 6, Table S2). Planktothrix, Cylindrospermopsis, Asulcocephalium, Cyclotella and Aulacoseira were positively related with WT and TP, whereas they were negatively associated with pH and DO. Microcystis, Woronichinia, Cryptomonas and Plagioselmis were positively related with pH and NH4+, whereas they were negatively correlated with TP and WT (|r| > 0.7, p < 0.001, Figure 6, Table S2).
Based on the ASV table and environmental factors data, correlation analyses were conducted, and DO, COD, NH4+, pH, WT, TP, TN, Chl-a and C showed significantly effects on the phytoplankton community structure (Table S3, Mantel’s r > 0.3505, p < 0.001). Significant differences in the relationships between the phytoplankton community and environmental factors at different sampling sites and different seasons were demonstrated by the redundancy analysis (Figure 7). During the dry period, the impacts of environmental factors influencing the phytoplankton community were DO > TP > COD > C > pH > Chl-a > WT > NH4+ > TN, while during the wet period, the impacts of environmental factors influencing the phytoplankton community were pH > C > TN > COD > WT > NH4+ > Chl-a > TP > DO.

4. Discussion

4.1. eDNA Metabarcoding Disclosed the Spatial and Temporal Variations in Phytoplankton Diversity in Dianchi Lake

Based on eDNA metabarcoding, a total of 526 ASVs (89 prokaryotic/437 eukaryotic) were assigned to the phytoplankton community, covering 10 phyla, 22 classes, 50 orders, 82 families, 108 genera and 108 species. Obvious spatial and temporal variations in the phytoplankton community (e.g., ASV number, dominant genera, relative abundances) were observed in Dianchi Lake. Although the relative abundance of Microcystis declined during the wet period, Microcystis dominated the prokaryotic phytoplankton community from the dry period to the wet period, which was similar to a previous study, while the relative abundance of Planktothrix increased significantly from the dry period to the wet period, becoming the first dominant cyanobacteria genus, this was clearly different from previous studies [15,16,19,46]. Classifying the functional groups of phytoplankton, Reynolds et al. [47] pointed out that functional group M, represented by Microcystis, a tolerance to sun exposure and a sensitivity to erosion, was suitable for growing in a static and eutrophic water environment; while functional group S1, represented by Anabaena, Pseudanabaena and Planktothrix, was suitable for growing in a turbid, fluid and mesotrophic water environment. In this study, Planktothrix dominated during the wet period; this agreed with the physical and chemical conditions variations such as the water disturbance and turbidity increase under a strong rainfall pattern and also indicated the water quality of Dianchi Lake may have been improved because of the comprehensive lake and river management in recent years [21,48].
For the eukaryotic phytoplankton community, 9 phyla and 99 genera were detected, and distinct spatial and temporal distribution characteristics could be observed in Dianchi Lake. Cryptophyta dominated in the eukaryotic phytoplankton community both in the dry and wet periods, but the second dominant phylum during the dry period was Chrysophyta, whereas it was Bacillariophyta during the wet period. These were different from previous studies, where the phytoplankton taxa were much more abundant than with traditional monitoring, and usually Chlorophyta and Bacillariophyta dominated in the eukaryotic phytoplankton community, whereas Chrysophyta and Cryptophyta accounted for relatively little [15,16,19,46]. These may be related to the higher resolution and the preference of monitoring primers in eDNA metabarcoding monitoring [26,34,35]. Cryptophyta accounted for over 35% of the total eukaryotic phytoplankton reads both during the dry and wet periods in this study, but in our previous eDNA metabarcoding monitoring of Dianchi and its three main inflow rivers, Cryptophyta accounted for 13.31% of the total eukaryotic phytoplankton reads, whereas Chrysophyta, Bacillariophyta and Chlorophyta accounted for 25.36%, 23.03% and 19.49%, respectively [36]; this difference was most likely due to differences in sampling time. The eukaryotic phytoplankton dominant genera in Dianchi Lake included Cryptomonas, Aulacoseira, Plagioselmis, Ceratium, Cyclotella, Stoeckeria and others. It has been shown that the increase in Cryptomonas abundance indicates the organic pollution of water bodies [49], and the high level of Cryptomonas abundance during the dry and wet periods also reminded us that the organic pollution of Dianchi Lake remains serious.
eDNA metabarcoding has been considered a great advantage in monitoring rare, hidden or invasive species [50,51]. Comparing with the literature on non-native phytoplankton in China [51,52,53,54,55,56], except for the common phytoplankton species in Dianchi Lake, we found 4 non-native species among all 108 phytoplankton species. Chroomonas coerulea and Mychonastes homosphaera were detected in dry periods. Chlamydomonas raudensis and Heterosigma akashiwo were detected in wet periods. Thus, we should pay more attention to the presence of these non-native species and carefully monitor their distribution as these non-native species may cause harmful algal blooms such as Heterosigma akashiwo [55].

4.2. Phytoplankton Diversity Patterns in Dianchi Lake Were Shaped by Environmental Factors

Environmental factors play a strong part in the growth and reproduction of phytoplankton, light, temperature, COD, nutrients and others meaningfully influence the species composition, dominant taxon and the abundance of phytoplankton community [8,36,57]. Significant temporal variations in phytoplankton diversity in Dianchi Lake were shown by the Shannon–Wiener index and the PCoA analysis. The Shannon–Wiener indexes were significantly higher during the wet period than the dry period, and the samples from each period were grouped together, respectively (Figure 5). These might be attributed to the differences in environmental factors. For example, the temperature and light conditions in the wet period are more adaptive for the growth and propagation of phytoplankton than in the dry period [10,16], and certain environmental factors are meaningfully correlated to the relative abundances of specific genera [16,36,49]. Therefore, the diversities were higher during the wet period than the dry period, and there was a difference in dominant genera in different periods.
As an urban lake, Dianchi Lake is subject to strong disturbances from various anthropogenic activities, TN, TP, COD, etc. Environmental factors have continued to exceed the standard despite the implementation of multiple water pollution control measures in recent years, which has influenced the phytoplankton community structure and even caused a severe cyanobacteria bloom [10,12,14,16]. Previous studies demonstrated that the phytoplankton community structure and phytoplankton cell density in Dianchi Lake were closely linked to C, DO, TN, TP and COD [10,15,16,19]. The relationship between phytoplankton and environmental factors in the Fubao Bay of Dianchi Lake exhibited a high temporal heterogeneity, whose main factors were TN in summer, TP in autumn and BOD5 or NO2-N in winter [17]. Feng et al. [19] deemed nutrients had a great influence on the phytoplankton community in Dianchi Lake, which were positively linked to pH, TP and NH4+, and negatively linked to NO3−, N:P, TN, dissolved organic carbon (DOC) and total organic carbon (TOC). In the intermediate water season, COD, WT, TP and pH were positively associated with the phytoplankton community in Dianchi Lake, while DO, NH4+, TN and C were negative factors [36]. This study showed the temporal–spatial heterogeneity of the relationship between the phytoplankton community and environmental factors in Dianchi Lake, and the influence of environmental factors varied at different sampling sites and different periods (Figure 7). DO was deemed to be the crucial environmental factor influencing the distribution characteristics of the phytoplankton community in Dianchi Lake in the dry period, while it was pH in the wet period. These may be mainly due to the seasonal change in environmental factors affecting the growth of dominant phytoplankton taxa. DO is positively correlated with the growth of Cryptophyta, Euglenophyta and Chlorophyta [58], and at the same time, these phytoplankton release oxygen and increase the DO content [59], which would strongly influence the composition of phytoplankton during the dry period in Dianchi Lake. Microcystis prefer alkaline environment [15,19], the pH of Dianchi Lake during the wet period was slightly lower than that during the dry period in this study, thus the abundance of Microcystis decreased while it was the opposite for Planktothrix, which eventually led to the change in the phytoplankton community structure. The impacts of nutrients (TP and TN) also showed a difference in different periods. These were consistent with the temporal and spatial differences in pollution characteristics in Dianchi Lake [15], where the concentration of TN in the wet period was lower than that in the dry period, whereas that of TP was higher.

4.3. eDNA Metabarcoding Is a Useful Tool for Phytoplankton Diversity Monitoring

eDNA metabarcoding has provided a new option for the monitoring of aquatic biodiversity in recent years [26], it has been widely applied in phytoplankton monitoring, providing results with a high consistency between eDNA metabarcoding and traditional morphological methods, and it is more beneficial to the discovery of rare, hidden and invasive species [9,30,34,51,56,60]. Cheng et al. [61] monitored the microbial community in Baiyangdian Lake via eDNA metabarcoding and deemed it had a higher detection ability compared to traditional morphological methods. Zhang et al. [44] monitored the eukaryotic phytoplankton community in Danjiangkou Reservoir via eDNA metabarcoding and deemed it had an advantage over traditional morphological methods. Zhang et al. [35] found that the dominant genera in Dianchi Lake and Fuxian Lake detected by eDNA metabarcoding were mostly consistent with the dominant genera in previous morphological investigations, covering 62.5% and 71.05% of eukaryotic phytoplankton taxa, respectively. In total, 10 phyla and 108 species of phytoplankton taxa were identified in this study, which is much more than historical traditional investigations, reflecting the high detection ability of eDNA monitoring. Actually, the improvement in phytoplankton diversity in Dianchi Lake may also be correlated to the comprehensive lake and river management practices [21,48].
As a new technology, eDNA metabarcoding also faces some problems in its popularization and application, for example, the low overlap rate with traditional morphological monitoring, not being able to annotate all ASVs at the species level, not being effective to a quantitative population estimation and distinguishing whether the detected taxon is from live cells or DNA residuals, and which of these may be related to the current relatively inefficient collection and preservation technology of samples, the bias of sequencing primers, the imperfection of a local database and others [50,61,62,63]. By resolving these problems, eDNA metabarcoding would certainly have a greater application potential in phytoplankton diversity monitoring, and simultaneous environmental RNA monitoring may be an effective complementary tool [64].

5. Conclusions

Based on environmental DNA metabarcoding, this study identified 10 phyla, 22 classes, 50 orders, 82 families, 108 genera and 108 species of phytoplankton. Distinct temporal and spatial variations in the phytoplankton community (e.g., ASVs number, dominant taxon, the relative abundance) were observed in Dianchi Lake. Interestingly, Cryptophyta dominated in the eukaryotic phytoplankton community, and Microcystis dominated in the prokaryotic phytoplankton community both in dry and wet periods, and the first dominant cyanobacteria genus was changed from Microcystis (dry period) to Planktothrix (wet period). A temporal–spatial heterogeneity of the relation between the phytoplankton community and environmental factors was observed in Dianchi Lake. DO was identified as the crucial environmental factor influencing the phytoplankton community structure in Dianchi Lake during the dry period, while it was pH during the wet period. The impacts of TP and TN also showed a difference in different periods. This study reminded us that the water quality may have been improved, but the inorganic and organic pollution of Dianchi Lake remains serious, and the management and conservation of Dianchi Lake should be sustained.

Supplementary Materials

The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16010032/s1, Figure S1: Rarefaction curves of 16S-V3 and 18S-V9 metabarcoding. Table S1: PERMANOVA tests of both sites in the dry and wet periods. Table S2: Spearman’s correlation analysis between relative abundances of specific genera and environmental factors. Table S3: Correlation analysis between phytoplankton community structure and environmental factors.

Author Contributions

Y.L., S.X. and X.Z., conceived the subject. Y.L., J.X., L.S., L.H. and Z.Z. performed the experiments and analyzed the data. Y.L. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Basic Research Program-Youth Program of Science and Technology Department in Yunnan province (nos. 202201AU070026 and 202101AU070058), the Key Program of Joint Special Project (no. 202001BA070001-130), and National Natural Science Foundation of China (NSFC)—Yunnan Joint Key Grant (no. U1902202); Academician Workstation for Ecological Health Assessment and Rehabilitation of Rivers and Lakes in Kunming, Key Laboratory of River and Lake Ecological Health Assessment and Restoration in Yunnan province, International Joint Innovation Team for Yunnan Plateau Lakes and Great Lakes of North America, which is sponsored by the Yunnan Provincial Education Department.

Data Availability Statement

Raw sequencing data were deposited in the NCBI (https://www.ncbi.nlm.nih.gov/) Bioproject database (accession number: PRJNA1028392).

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Figure 1. Map of the Dianchi Lake with monitoring sites indicated, and the monitoring sites (D1–D8) were shown in the map.
Figure 1. Map of the Dianchi Lake with monitoring sites indicated, and the monitoring sites (D1–D8) were shown in the map.
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Figure 2. Spatial and temporal distributions of the cyanobacteria community in Dianchi Lake during the dry period (a) and wet period (b) based on the relative abundances of each genus.
Figure 2. Spatial and temporal distributions of the cyanobacteria community in Dianchi Lake during the dry period (a) and wet period (b) based on the relative abundances of each genus.
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Figure 3. Spatial and temporal distributions of the eukaryotic phytoplankton community in Dianchi Lake during the dry period (a) and wet period (b) based on the relative abundances of each phylum.
Figure 3. Spatial and temporal distributions of the eukaryotic phytoplankton community in Dianchi Lake during the dry period (a) and wet period (b) based on the relative abundances of each phylum.
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Figure 4. Temporal variation in dominant genera of the phytoplankton community during the dry period and wet period in Dianchi Lake, (a) cyanobacteria community (b) eukaryotic phytoplankton community.
Figure 4. Temporal variation in dominant genera of the phytoplankton community during the dry period and wet period in Dianchi Lake, (a) cyanobacteria community (b) eukaryotic phytoplankton community.
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Figure 5. Temporal variation in phytoplankton diversity during the dry period and wet period in Dianchi Lake. (a) α−diversity difference; (b) PCoA analysis. Significant terms ‘p < 0.0001’ are marked as ****. D1–D8 means sampling sites, _1, _2, _3 means the replicates, F_ at the front of sampling sites represents Wet period.
Figure 5. Temporal variation in phytoplankton diversity during the dry period and wet period in Dianchi Lake. (a) α−diversity difference; (b) PCoA analysis. Significant terms ‘p < 0.0001’ are marked as ****. D1–D8 means sampling sites, _1, _2, _3 means the replicates, F_ at the front of sampling sites represents Wet period.
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Figure 6. Correlation cluster analysis between environmental factors and the relative abundances of phytoplankton genera (top 20). Significant terms are marked as: *, 0.01 < p < 0.05; **, p < 0.01.
Figure 6. Correlation cluster analysis between environmental factors and the relative abundances of phytoplankton genera (top 20). Significant terms are marked as: *, 0.01 < p < 0.05; **, p < 0.01.
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Figure 7. The relations between phytoplankton community structures and environmental factors in Dianchi Lake during the dry period (a) and wet period (b) shown by the RDA ordination plot. D1–D8 means sampling sites, _1, _2, _3 means the replicates.
Figure 7. The relations between phytoplankton community structures and environmental factors in Dianchi Lake during the dry period (a) and wet period (b) shown by the RDA ordination plot. D1–D8 means sampling sites, _1, _2, _3 means the replicates.
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Lin, Y.; Xu, J.; Shen, L.; Zhou, X.; He, L.; Zhao, Z.; Xu, S. Spatial and Temporal Variations in Phytoplankton Community in Dianchi Lake Using eDNA Metabarcoding. Water 2024, 16, 32. https://doi.org/10.3390/w16010032

AMA Style

Lin Y, Xu J, Shen L, Zhou X, He L, Zhao Z, Xu S. Spatial and Temporal Variations in Phytoplankton Community in Dianchi Lake Using eDNA Metabarcoding. Water. 2024; 16(1):32. https://doi.org/10.3390/w16010032

Chicago/Turabian Style

Lin, Yuanyuan, Jingge Xu, Liang Shen, Xiaohua Zhou, Liwei He, Zheng Zhao, and Shan Xu. 2024. "Spatial and Temporal Variations in Phytoplankton Community in Dianchi Lake Using eDNA Metabarcoding" Water 16, no. 1: 32. https://doi.org/10.3390/w16010032

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