scholarly journals Seasonal Succession of Phytoplankton Functional Groups and Driving Factors of Cyanobacterial Blooms in a Subtropical Reservoir in South China

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1167 ◽  
Author(s):  
Lingai Yao ◽  
Xuemin Zhao ◽  
Guang-Jie Zhou ◽  
Rongchang Liang ◽  
Ting Gou ◽  
...  
Keyword(s):  
Functional Groups ◽  
South China ◽  
Algal Cell ◽  
Seasonal Succession ◽  
Geographic Location ◽  
Water Source ◽  
Cyanobacterial Blooms ◽  
Nitrogen And Phosphorus ◽  
Phytoplankton Communities ◽  
Phytoplankton Functional Groups

Freshwater phytoplankton communities can be classified into a variety of functional groups that are based on physiological, morphological, and ecological characteristics. This classification method was used to study the temporal and spatial changes in the phytoplankton communities of Gaozhou Reservoir, which is a large municipal water source in South China. Between January 2015 and December 2017, a total of 155 taxa of phytoplankton that belong to seven phyla were identified. The phytoplankton communities were classified into 28 functional groups, nine of which were considered to be representative functional groups (relative biomass > 10%). Phytoplankton species richness was greater in the summer and autumn than in the winter and spring; cyanobacterial blooms occurred in the spring. The seasonal succession of phytoplankton functional groups was characterized by the occurrence of functional groups P (Staurastrum sp. and Closterium acerosum) and Y (Cryptomonas ovata and Cryptomonas erosa) in the winter and spring, and functional groups NA (Cosmarium sp. and Staurodesmus sp.) and P (Staurastrum sp. and Closterium acerosum) in the summer and autumn. The temperature, nitrogen, and phosphorus levels were the main factors driving seasonal changes in the phytoplankton communities of Gaozhou Reservoir. The functional group M (Microcystis aeruginosa) dominated the community during the cyanobacterial blooms in spring 2016, with the maximum algal cell density of 3.12 × 108 cells L−1. Relatively low temperature (20.8 °C), high concentrations of phosphorus (0.080–0.110 mg L−1), suitable hydrological and hydrodynamic conditions (e.g., relatively long retention time), and relatively closed geographic location in the reservoir were the key factors that stimulated the cyanobacterial blooms during the early stages.

Phytoplankton functional groups response to environmental parameters in Muling River basin of northeast China

2019 ◽  
Vol 55 ◽  
pp. 17
Author(s):  
Xu Sun ◽  
Patteson Chula Mwagona ◽  
Isaac Ekyamba Shabani ◽  
Wenjiu Hou ◽  
Xiaoyu Li ◽  
...  
Keyword(s):  
Functional Groups ◽  
River Basin ◽  
Light Availability ◽  
Seasonal Succession ◽  
Environmental Parameters ◽  
N:P Ratio ◽  
Ammonium Nitrogen ◽  
Nutrient Concentrations ◽  
Phytoplankton Functional Groups

The present study was carried out in the biggest tributary of Ussuri River of boundary between China and Russia. The Muling River basin has undergone a long-term dredging works, and waterbody became seriously turbid. The succession of phytoplankton functional groups succession and environmental factors in the river were sampled in 2015. We totally identified 83 species, belonging to 17 functional groups which 5 were predominant, including group F, M, MP, P and Y. The seasonal succession of phytoplankton functional groups was M/P-F/MP/P-MP/P. Results of Spearman correlation analysis and canonical correspondence analysis (CCA) revealed that phytoplankton functional groups were mainly influenced by nutrient concentrations and light availability including total nitrogen (TN), ammonium nitrogen (NH4+–N), nitrate nitrogen (NO3−–N), N:P ratio (N:P), water depth (D) and transparency (SD) in the basin.

scholarly journals Seasonal and spatial patterns of eukaryotic phytoplankton communities in an urban river based on marker gene

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Yang ◽  
Junping Lv ◽  
Qi Liu ◽  
Fangru Nan ◽  
Bo Li ◽  
...  
Keyword(s):  
Functional Groups ◽  
Environmental Gradients ◽  
Pearson Correlation ◽  
Marker Gene ◽  
Variance Partitioning ◽  
Linear Discriminant ◽  
Eukaryotic Phytoplankton ◽  
Phytoplankton Communities ◽  
Phytoplankton Functional Groups ◽  
Determining Factors

AbstractThe seasonal and spatial eukaryotic phytoplankton composition in the Fenhe River was investigated based on the 18S rDNA V4 region. The relationship between phytoplankton functional groups and environmental factors was explored to effectively capture the responses of these taxa to environmental gradients and their effects on ecosystem function. Our results indicated that the Chlorophyta and Bacillariophyta had higher relative abundance than other taxa, and their diversity and richness indices in spring were higher than those in other seasons. The linear discriminant analysis effect size (LEfSe) analyses detected that the potential seasonal biomarkers included Desmodesmus, Cyclotella, Pseudoschroederia, Discostella, Scenedesmus, Monoraphidium, and Nannochloropsis; the spatial biomarkers included Amphora, Neochloris, Hindakia, Pseudomuriella, Coccomyxa, Chloroidium, Scherffelia, Chromochloris, and Scotinosphaera. The systemic evolution and distribution characteristics of the first 50 representative sequences showed that the dominant genus included Desmodesmus in spring, Pseudopediastrum in summer, Mychonastes in autumn, and Monoraphidium in winter. Main seasonal variation of phytoplankton functional groups was as follows: spring (J + F + C + X1) → summer (J + F + X1 + X2) → autumn (J + F + X1 + C) → winter (X1 + J + B + X2). Pearson correlation, redundancy analysis, and variance partitioning analysis showed temperature and phosphate were the determining factors causing the changes of phytoplankton functional groups and community composition in the Fenhe River.

Seasonal succession of phytoplankton functional groups in Lake Fuxian and its driving factors

2019 ◽  
Vol 55 ◽  
pp. 24
Author(s):  
Jing Dong ◽  
Chenlu Li ◽  
Dujuan Dai ◽  
Shuangshuang Yao ◽  
Sen Li ◽  
...  
Keyword(s):  
Surface Water ◽  
Functional Groups ◽  
Redundancy Analysis ◽  
Dominant Species ◽  
Seasonal Succession ◽  
Indicator Function ◽  
Total Biomass ◽  
Phytoplankton Functional Groups ◽  
Lake Fuxian ◽  
Depth Water

The concept of phytoplankton functional groups was proposed based on data from numerous European lakes and has been widely used in lakes, reservoirs, rivers worldwide. However, the application of this concept to subtropical plateau lakes has rarely been reported. In this study, 16 sampling sites were selected across the entirety of Lake Fuxian, Yunnan, China. Eighteen phytoplankton functional groups (F, G, J, X2, X1,T, P, MP, D, C, H1, LO, S1, M, Y, E, W1 and W2) were classified according to the investigation of surface water and gradient depth samples. Nine of these groups, namely LO, H1, C, MP, P, T, X1, J and F, were identified as dominant species (>5% total biomass). Furthermore, LO, H1 and T were considered predominant (accounting for the maximum percentage of biomass in each month). The sampling showed that the seasonal succession of predominant assemblages in surface water was T (October) to H1 (January) to H1 (April) to Lo (July) and T+Lo (October) to T (January) to H1 (April) to Lo (July) in the gradient depth water. Redundancy analysis (RDA) combined with the indicator function of the phytoplankton groups suggested that WT and TN/TP were important factors in driving the succession of predominant assemblages all year around.

scholarly journals Temporal Stability of Phytoplankton Functional Groups Within Two Agricultural Irrigation Ponds in Maryland, USA

2021 ◽  
Vol 3 ◽  
Author(s):  
Jaclyn E. Smith ◽  
Jennifer L. Wolny ◽  
Matthew D. Stocker ◽  
Robert L. Hill ◽  
Yakov A. Pachepsky
Keyword(s):  
Water Quality ◽  
Functional Groups ◽  
Spatial Patterns ◽  
Functional Group ◽  
Temporal Stability ◽  
Average Concentration ◽  
Spatial And Temporal Variability ◽  
Agricultural Irrigation ◽  
Phytoplankton Communities ◽  
Phytoplankton Functional Groups

Phytoplankton functional groups and their influence on water quality have been studied in various types of water bodies but have yet to be studied in agricultural irrigation ponds. Freshwater sources (e.g., lakes, rivers, and reservoirs) have been previously shown to exhibit high spatial and temporal variability in phytoplankton populations. Improvements in the monitoring of phytoplankton populations may be achieved if patterns of stable spatial variability can be found in the phytoplankton populations through time. The objective of this work was to determine if temporally stable spatial patterns in phytoplankton communities could be detected in agricultural irrigation ponds using a functional group approach. The study was performed at two working agricultural irrigation ponds located in Maryland, USA over two summer sampling campaigns in 2017 and 2018. Concentrations of four phytoplankton groups, along with sensor-based and fluorometer based water quality parameters were measured. Temporal stability was assessed using mean relative differences between measurements in each location and averaged measurements across ponds on each sampling date. Temporally stable spatial patterns of three phytoplankton functional groups were found for both ponds over the two sampling seasons. Both ponds had locations where specific phytoplankton functional group concentrations were consistently higher or lower than the pond's average concentration for each sampling date. Zones of consistently higher or lower than average concentrations were associated with flow conditions, pond morphology, and human activities. The existence of temporally stable patterns of phytoplankton functional group concentrations can affect the outcome of a water quality assessment and should be considered in water quality monitoring designs.

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