Environmental DNA metabarcoding revealed the impacts of anthropogenic activities on phytoplankton diversity in Dianchi Lake and its three inflow rivers

Abstract

Phytoplankton diversity is closely related to environmental variables and has been widely used in ecological health assessment of rivers and lakes. Combining advantages of DNA‐based identification and high‐throughput sequencing technology, environmental DNA (eDNA) metabarcoding permits a new measurement for biodiversity monitoring in aquatic ecosystems. However, it had rarely been used to explore the variability and similarity of phytoplankton diversity between lake and its inflow rivers and the effects of environmental variables on phytoplankton. This study utilized eDNA metabarcoding to investigate the spatial distribution of phytoplankton and the impacts of environmental variables on the phytoplankton diversity in Dianchi Lake (one of the most polluted urban lakes in China) and its main inflow rivers (Panlong River, Baoxiang River, and Chai River). A total of 243 distinct phytoplankton taxa were detected, covering 9 phyla, 30 classes, 84 orders, and 132 families, and the taxonomic richness of rivers was higher than that of Dianchi Lake. Distinct biodiversity patterns (e.g., community structure, dominant taxon, ɑ‐diversity) were exhibited among Dianchi Lake and its three inflow rivers, but similar biodiversity patterns were also observed in Dianchi Lake and the estuarine sites. The patterns of phytoplankton diversity were closely related to environmental variables, which were associated with pollution sources from different anthropogenic activities (e.g., urbanization, water diversion, industrial and agricultural activities). The primary environmental variables correlated with phytoplankton diversity varied in different habitats. The total phosphorus (TP) and chemical oxygen demand (COD) positively correlated with the phytoplankton community structures in Dianchi Lake, whereas negatively correlated in Panlong River and Baoxiang River. The total nitrogen (TN) positively correlated with the phytoplankton community structures in Baoxiang River and Chai River but negatively correlated in Dianchi Lake. Overall, this study provides insights on the phytoplankton diversity monitoring and the conservation of its diversity and healthy management of Dianchi Lake.

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

“…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.…”

“…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.…”

“…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 (Y i = n i /N × f i ) was applied to calculate the genera with an extremely high relative sequence abundance, where Y i is the dominance index of the ith phytoplankton taxon (genus with Y i > 0.02 is dominant), N is the total number of phytoplankton reads detected, n i is the number of read of the ith phytoplankton taxon, and f i is the frequency of occurrence of the ith phytoplankton taxon [44,45].…”

“…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.…”

“…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.…”