Spatial and Temporal Variations in Environmental Variables in Relation to Phytoplankton Community Structure in a Eutrophic River-Type Reservoir

Abstract

This study assesses spatial and temporal variation in environmental variables in relation to phytoplankton community size and composition in a typically eutrophic river reservoir (Hai River, northern China). The aim is to identify environmental parameters governing spatial and temporal differences in phytoplankton density and composition. Physicochemical parameters, including nutrient concentrations, were determined in monthly surface water samples from 2015. The average concentration of key eutrophication indexes (i.e., total phosphorous (TP: 0.24 ± 0.11 mg·L −1 ), total nitrogen (TN: 2.96 ± 1.60 mg·L −1 ), and Chlorophyll a (Chl a: 38.5 ± 11.5 mg·m −3 )) substantially exceeded threshold values for eutrophic streams. Moreover, the eutrophication increased significantly downstream along the river reservoir as a consequence of an increasing fraction of agricultural and industrial land-use in the watershed. 103 phytoplankton species were identified, of which Chlorophyta was the dominated phylum (47 species), followed by Bacillariophyta (23 species) and Cyanophyta (18 species). No spatial difference in species distribution (ANOVA, p > 0.05) were found, while the temporal differences in species composition exhibited significant heterogeneity (ANOVA, p < 0.001). Phytoplankton abundance was highest in early summer (June and July), with maximum values increasing from 1.78 × 10 8 and 2.80 × 10 8 cells·L −1 in upstream and middle reaches, respectively, to 4.18 × 10 8 cells·L −1 furthest downstream. Cyanophyta, also known as Cyanobacteria and commonly referred to as blue-green algal, are known to constitute algae bloom in eutrophic systems. Common species are Microcystis marginata, Microcystis flos-aquae, and Oscillatoria sp. This was the dominant phyla during summer months, especially in the middle and lower reaches of the stream reservoir where it accounted for 88.9% of the phytoplankton community. Shannon weaver index (H') and Pielous's evenness index (J') were extremely low (1.91-2.43 for H' and 0.39-0.45 for J') in samples collected from the lower part of the stream during the period of algal bloom, indicating an imbalance in the phytoplankton communities. Canonical correspondence analysis (CCA) indicated that water temperature (WT) and possible pH, along with nitrate (NO 3 -N) and nitrite (NO 2 -N), were the most important explanatory parameters in regard to phytoplankton composition. This research provides an understanding of the role of physicochemical water quality parameters in governing algal blooms and phytoplankton composition in river reservoirs.

Seasonal influence of physicochemical parameters on phytoplankton diversity and assemblage pattern in Kailash Khal, a tropical wetland, Sundarbans, India

“…These loadings indicated that the main variation in algal density is related to the parameters pH and TA at the station KE. Similar results also are observed by Zhao et al (2017) from a Hai River reservoir, Tianjin City. In our study, it was seen that spatial variation in algal groups accurately reflects the water conditions of the wetland.…”

Influence of shrimp farming activities on the phytoplankton community of a nearby estuary in Northeastern Brazil

“…This may be correlated with a continuous supply of nutrients that is a eutrophic condition that induces rapid growth of some groups or species of microalgae, especially Cyanophyta. Zhao et al (2017) also reported lower J' (0.39-0.45) in an environment that had a higher abundance of Cyanophyta due to eutrophic conditions, similar to that found in shrimp ponds.…”

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

“…Even if this auto-correlation was not considered, it could be seen that some models using NO 3 -N or TN as input features along with air temperature were also very accurate. From these results, we could assume that nitrogen compounds were directly or indirectly involved in cyanobacterial blooms [23,51]. Therefore, further research could identify if the control of nitrogen compounds flowing into rivers or reservoirs according to the air temperature is possible to make the cyanobacterial blooms produce or fade.…”