Temporal variation in zooplankton and phytoplankton community species composition and the affecting factors in Lake Taihu—a large freshwater lake in China

Li, Cuicui; Feng, Weiying; Chen, Haiyan; Li, Xiaofeng; Song, Fanhao; Guo, Wenjing; Giesy, John P.; Sun, Fubao

doi:10.1016/j.envpol.2018.11.007

Cited by 92 publications

(41 citation statements)

References 34 publications

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“…We also found that WT was the most powerful environmental factor driving the seasonal changes in zooplankton (partial Mantel test, Adonis, p < 0.01) (Sellami et al, 2016;Sunagawa et al, 2015), followed by TN. This result confirmed that temperature and nutrient accumulation were the most important water environmental factors that affected the seasonal changes in the zooplankton community (Li et al, 2019a). Different zooplankton species have specific optimal temperatures (Li et al, 2019a).…”

Section: Effects Of Water Environmental Factorssupporting

confidence: 63%

“…This result confirmed that temperature and nutrient accumulation were the most important water environmental factors that affected the seasonal changes in the zooplankton community (Li et al, 2019a). Different zooplankton species have specific optimal temperatures (Li et al, 2019a). Appropriate temperature conditions promote the growth and predation of some zooplankton groups, resulting in seasonal changes in zooplankton groups.…”

Section: Effects Of Water Environmental Factorssupporting

confidence: 59%

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DNA metabarcoding of zooplankton communities: species diversity and seasonal variation revealed by 18S rRNA and COI

Zhao

Zhang

et al. 2021

PeerJ

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Background Zooplankton is an important component of aquatic organisms and has important biological and economical significance in freshwater ecosystems. However, traditional methods that rely on morphology to classify zooplankton require expert taxonomic skills. Moreover, traditional classification methods are time-consuming and labor-intensive, which is not practical for the design of conservation measures and ecological management tools based on zooplankton diversity assessment. Methods We used DNA metabarcoding technology with two different markers: the nuclear small subunit ribosomal RNA (18S rRNA) and mitochondrial cytochrome c oxidase (COI), to analyze 72 zooplankton samples collected in 4 seasons and 9 locations from the Sanmenxia Reservoir. We investigated seasonal changes in the zooplankton community and their relationship with water environmental factors. Results A total of 190 species of zooplankton were found, belonging to 12 phyla, 24 classes, 61 orders, 111 families, and 174 genera. Protozoa, especially ciliates, were the most diverse taxa. Richness and relative abundance of zooplankton showed significant seasonal changes. Both alpha and beta diversity showed seasonal trends: the diversity in summer and autumn was higher than that in winter and spring. The zooplankton diversity was most similar in winter and spring. By correlating metabarcoding data and water environmental factors, we proved that water temperature, chemical oxygen demand, total nitrogen and ammoniacal nitrogen were the main environmental factors driving the seasonal changes in zooplankton in the Sanmenxia Reservoir. Water temperature, followed by total nitrogen, were the most influential factors. This study highlights the advantages and some limitations of zooplankton molecular biodiversity assessment using two molecular markers.

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Section: Effects Of Water Environmental Factorssupporting

confidence: 63%

Section: Effects Of Water Environmental Factorssupporting

confidence: 59%

DNA metabarcoding of zooplankton communities: species diversity and seasonal variation revealed by 18S rRNA and COI

Zhao

Zhang

et al. 2021

PeerJ

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“…Arthropoda, as a kind of zooplankton, grow by feeding on algae, organic particles, and bacteria, and some even have algae as their sole source of nutrition. Thus, to a certain extent, arthropod growth might be affected by the decrease of their food sources [31]. Recently, research observed that a relative abundance of arthropods declined over time in a subtropical reservoir, and that they were one of the major contributors to eukaryotic plankton community rearrangements [15].…”

Section: Microbial Eukaryotic Community Compositionmentioning

confidence: 99%

“…Ciliophora and Cercozoa are widely distributed and ubiquitous in lakes [11,14,32,60,61] and reservoirs [15,62], and they are important consumers of algae and bacteria, which occupy a pivotal position in aquatic food webs by transferring nutrients to higher trophic levels [61]. Nematoda, Ciliophora, and Cercozoa are also affiliated with zooplankton, inducing a decrease of algal density by predation and altering the structure of phytoplankton communities by selecting specific algae [31]. Several studies have reported that variations in zooplankton species distribution and abundance may alter ecosystems via cascading effects, and found that climate variability can be indicated by zooplankton [55,56,63].…”

Section: Microbial Eukaryotic Community Compositionmentioning

confidence: 99%

Community Compositions of Phytoplankton and Eukaryotes during the Mixing Periods of a Drinking Water Reservoir: Dynamics and Interactions

Yan

Chen

Huang

et al. 2020

IJERPH

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In deep drinking water reservoir ecosystems, the dynamics and interactions of community compositions of phytoplankton and eukaryotes during the mixing periods are still unclear. Here, morphological characteristics combined with high-throughput DNA sequencing (HTS) were used to investigate the variations of phytoplankton and the eukaryotic community in a large canyon-shaped, stratified reservoir located at the Heihe River in Shaanxi Province for three months. The results showed that Bacillariophyta and Chlorophyta were the dominant taxa of the phytoplankton community, accounting for more than 97% of total phytoplankton abundance, which mainly consisted of Melosira sp., Cyclotella sp., and Chlorella sp., respectively. Illumina Miseq sequencing suggested that the biodiversity of eukaryotes increased over time and that species distribution was more even. Arthropoda (6.63% to 79.19%), Ochrophyta (5.60% to 35.16%), Ciliophora (1.81% to 10.93%) and Cryptomonadales (0.25% to 11.48%) were the keystone taxa in common, contributing over 50% of the total eukaryotic community. Cryptomycota as a unique fungus was observed to possess significant synchronization with algal density, reaching a maximum of 10.70% in December (when the algal density distinctly decreased) and suggesting that it might affect the growth of algae through parasitism. Co-occurrence network patterns revealed the complicated and diverse interactions between eukaryotes and phytoplankton, suggesting that eukaryotes respond to variations in dynamic structure of the phytoplankton community, although there might be antagonistic or mutualistic interactions between them. Redundancy analysis (RDA) results showed that environmental variables collectively explained a 96.7% variance of phytoplankton and 96.3% variance of eukaryotic microorganisms, indicating that the temporal variations of phytoplankton and eukaryotic microorganisms were significantly affected by environmental conditions. This study shows that potential interactions exist between phytoplankton and eukaryotic microorganism communities, andcould improve our understanding of the ecological roles of phytoplankton and eukaryotic microorganisms in changing aquatic ecosystems. However, long-term investigations are necessary in order to obtain comprehensive understandings of their complicated associations.

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“…Davis et al (2015) evaluated the responses of phytoplankton to increasing nitrogen and phosphorus concentrations in Sandusky Bay, where annual blooms of algae occur. Li et al (2019) reported that water temperature, 5-day biochemical oxygen demand (BOD 5 ), nitrate (NO 3 -N), and dissolved total organic carbon had the most significant effects on the phytoplankton community in Taihu Lake. Li et al (2020) demonstrated the effects of zooplankton on the phytoplankton community through top-down effects, while bottom-up effects were also documented in Nansi Lake.…”

Section: Introductionmentioning

confidence: 99%

Diverse drivers of phytoplankton dynamics in different phyla across the annual cycle in a freshwater lake

et al. 2021

Journal of Freshwater Ecology

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An understanding of the annual variations in the phytoplankton community in freshwater lakes, and of the drivers responsible for these variations, is essential for lake management. In this study we collected and analyzed seasonal samples of phytoplankton and assessed environmental variables in Nansi Lake from 2011 to 2015, in order to identify the seasonal dynamics of the phytoplankton community and the main drivers in each phylum. The results showed that phytoplankton diversity in Nansi Lake followed annual cycles, which were interrupted by a water diversion in 2013. The disruption of equilibrium was clearly demonstrated by the Shannon-Wiener diversity index, Pielou's evenness index, and Simpson's dominance index, whereas the species richness and abundance showed resilience, maintaining regular annual cycles. The dominant species were frequently Synedra acus K€ utz (Bacillariophyta), Closterium gracile Br eb (Chlorophyta), and Scenedesmus quadricauda Br eb (Chlorophyta) in summer and autumn. Non-metric multidimensional scaling (NMDS) analysis suggested that the phytoplankton was most widely distributed in summer, when conditions were more conducive to phytoplankton growth and reproduction. Redundancy analysis (RDA) indicated that the main drivers for the phytoplankton community varied in different taxonomic units. Specifically, the NP ratio and permanganate index (COD Mn ) had significant effects on Chlorophyta, whereas pH, dissolved oxygen (DO), and transparency significantly influenced Bacillariophyta. This study highlights the importance of seasonal phytoplankton succession and the need for identification of the main drivers in each phylum in order to achieve more effective control of phytoplankton blooms in freshwater lakes.

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Temporal variation in zooplankton and phytoplankton community species composition and the affecting factors in Lake Taihu—a large freshwater lake in China

Cited by 92 publications

References 34 publications

DNA metabarcoding of zooplankton communities: species diversity and seasonal variation revealed by 18S rRNA and COI

DNA metabarcoding of zooplankton communities: species diversity and seasonal variation revealed by 18S rRNA and COI

Community Compositions of Phytoplankton and Eukaryotes during the Mixing Periods of a Drinking Water Reservoir: Dynamics and Interactions

Diverse drivers of phytoplankton dynamics in different phyla across the annual cycle in a freshwater lake

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