Influence of cyanobacterial blooms and environmental variation on zooplankton and eukaryotic phytoplankton in a large, shallow, eutrophic lake in China

Distribution of selenium: A case study of the Drava, Danube and associated aquatic biotopes

This paper presents the results of the research on the overall distribution of selenium (Se) in various aquatic compartments (water, sediment, plankton and macrophytes) at six selected sites of the Croatian part of the Drava and Danube rivers, the connected floodplain lake and the melioration channel system carried out in two sampling periods (flooding in June and the drought period in September). In addition, the physicochemical water properties, plankton composition and biomass were analysed. Our study revealed low mean Se contents in sediments and water, indicating Se deficiency in the studied freshwater systems. The physicochemical environment, including Se distribution, was primarily influenced by hydrology rather than site-specific biogeochemical and morphological characteristics. The flooding period was characterised by higher Se content in water and higher transparency, nitrate and total nitrogen concentrations than drought conditions. At the river sites, sediment Se content was the highest during the flood period, while at all other sites, higher concentrations were found during the drought, reaching the maximum in the lake. Although Se concentrations were below the threshold for aquatic ecotoxicity, they increased in the following order: water (0.021-0.187 μg Se L-1) < sediments (0.005-0.352 mg Se kg-1) < macrophytes (0.010-0.413 mg Se kg-1) < plankton (0.044-0.518 mg Se kg-1) indicating its possible biomagnification at the bottom of the food chain. Species known for high Se accumulation potential dominated the biomass of the main plankton groups and the composition of the macrophyte community, which may provide a more sensitive and accurate steady-state compartment monitor for Se assessment in freshwater biotopes.

Strategies for regulating the intensity of different cyanobacterial blooms: Insights from the dynamics and stability of bacterioplankton communities

The occurrence of cyanobacterial blooms is increasing in frequency and magnitude due to climate change and human activities, which poses a direct threat to drinking water security. The impacts of abiotic and biotic factors on the development of blooms have been well studied; however, control strategies for different bloom intensities have rarely been explored from the perspective of the dynamics and stability of bacterioplankton communities. Here, a network analysis was used to investigate the interactions and stability of microbial communities during different periods of R. raciborskii bloom in an inland freshwater lake. The abundance and diversity of rare taxa were significantly higher than that of abundant taxa throughout the bloom cycle. At the pre-bloom (PB) stage, microbial interactions among the different bacterial groups were weak but strongly negatively correlated, indicating low robustness and weak disturbance resistance within the community. However, community stability was better, and microbial interactions became more complicated at the high-bloom (HB) and low-bloom (LB) stages. Interestingly, rare taxa were significantly responsible for community stability and connectivity despite their low relative abundance. The Mantel test revealed that Secchi depth (SD), orthophosphate (PO43--P), and dissolved oxygen (DO) were significantly positively correlated with abundant taxa, rare taxa and PB. DO was significantly positively correlated with HB, intermediate taxa, and rare taxa, while water temperature (WT), N/P and total nitrogen (TN) were significantly positively correlated with LB, abundant taxa, intermediate taxa, and rare taxa. These findings suggest that reducing the PO43--P concentration at the PB stage may be an effective approach to preventing the development of R. raciborskii blooms, while regulating rare taxa at the HB and LB stages may be a key factor in controlling R. raciborskii blooms.

Spatio-temporal dynamics of phytoplankton in a diversion reservoir and the major influencing factors: taxonomic versus functional groups classification

Identifying factors affecting phytoplankton dynamics is crucial to the management of aquatic ecosystems. A lot of scholars have conducted intensive studies on phytoplankton in lake or reservoirs, but not many studies have been conducted on diversion reservoirs. To explore the seasonal and spatial variation of phytoplankton communities and their relationship with environmental factors in the context of water diversion, a case study was carried out at XiKeng (XK) reservoir in South China. In this study, month-by-month water samples and phytoplankton were collected from this reservoir from December, 2021, to July, 2022. The results showed that the phytoplankton community was characterized by significant spatial and temporal variations. There were significant differences in phytoplankton abundance and structure in the reservoirs in terms of time. The abundance of phytoplankton cells and the proportion of Cyanobacteria in the reservoir showed a trend of increasing from autumn to spring and then decreasing from spring to summer, while the functional group evolved from S1 in autumn to SN in spring and summer. The abundance of phytoplankton was influenced by the dynamic water division and the characteristics of the reservoir itself, resulting in a spatial distribution characteristic of AIII > AII > AI. Water temperature (WT) and nutrients were the key factors driving the changes in phytoplankton abundance and community structure in the reservoir. These findings will deepen our understanding of the spatial and temporal dynamics of phytoplankton community structure in diversion reservoirs and provide a basis for freshwater water ecological management strategies.

Seasonal Variation Characteristics and the Factors Affecting Plankton Community Structure in the Yitong River, China

To explore how environmental factors affected the plankton structure in the Yitong River, we surveyed the water environmental factors and plankton population in different seasons. The results showed high total nitrogen concentrations in Yitong River throughout the year, while the total phosphorus, water temperature (WT), and chemical oxygen demand in summer were significantly higher than those in other seasons (p < 0.05), and the dissolved oxygen (DO) concentrations and TN/TP ratio were significantly lower (p < 0.01) than those in other seasons. There was no significant seasonal change in other environmental factors. Cyanophyta, Chlorophyta, and Bacillariophyta were the main phytoplankton phylum, while Protozoa and Rotifera were the main zooplankton phylum. The abundance and biomass of zooplankton and phytoplankton in the summer were higher than those in other seasons. Non-Metric Multidimensional scaling methods demonstrated obvious seasonal variation of phytoplankton in summer compared to spring and winter, while the seasonal variation of the zooplankton community was not obvious. The results of the redundancy analysis showed that WT, DO and nitrate nitrogen were the main environmental factors affecting phytoplankton abundance. In contrast to environmental factors, phytoplankton was the main factor driving the seasonal variation of the zooplankton community structure. Cyanophyta were positively correlated with the changes in the plankton community.

Algal Density Controls the Spatial Variations in Hg Bioconcentration and Bioaccumulation at the Base of the Pelagic Food Web of Lake Taihu, China

Algal density can significantly impact mercury (Hg) bioaccumulation and biomagnification in aquatic food webs, but the underlying mechanisms remain controversial especially in subtropical and tropical regions. We conducted a comprehensive field study on Hg bioconcentration in phytoplankton and bioaccumulation in size-fractionated zooplankton across 17 sampling sites in Lake Taihu, a large shallow lake in eastern China with large spatial differences in algal density. The higher algal density in the northern sites is highly associated with the lower THg bioconcentration factor (BCF) in phytoplankton and lower THg bioaccumulation factor (BAF) in zooplankton. The low Hg BCFs or BAFs at productive sites could not be explained by algal bloom dilution but attributed to the low Hg bioavailability, which is highly associated with the elevated pH levels at productive sites. The smaller body size of the dominant zooplankton species at higher algal density sites also contributed to their lower Hg bioaccumulation. Importantly, we provide evidence that high algal density is associated with a low proportion of methylmercury (MeHg) in total Hg (% MeHg) in phytoplankton, which is further transferred to zooplankton. Such a low THg BCF or BAF and low % MeHg in plankton at high algal density sites hamper the entry of Hg into the pelagic food webs, which are important but yet underestimated driving forces for the low Hg contents in pelagic fish that are commonly observed in anthropogenic-impacted eutrophic lakes in subtropical regions.

Can top-down effects of planktivorous fish removal be used to mitigate cyanobacterial blooms in large subtropical highland lakes?

Removal of planktivorous fish is used extensively in northern temperate lakes to reduce phytoplankton abundance via enhanced zooplankton grazing. However, whether this method would work also in large subtropical highland lakes to alleviate cyanobacterial blooms is unknown. We conducted a one-year pilot in situ experiment where we removed a substantial biomass of fish in a fenced-in area, followed by a 3-year whole-lake experiment where the dominant fish species (Japanese smelt) was removed in Lake Erhai in southwest China. The fencing experiments showed that between July and November, when the biomass of the removed stock reached 4 g/m², the zooplankton biomass inside the fence increased significantly compared to a control fence. In the full-lake experiment, we found that sustained removal of Japanese smelt led to an increase in the biomass of cladocerans (Daphnia spp. but especially of Bosmina spp.) and a significant decrease in the biomass of Cyanobacteria and Chlorophyta. Additionally, a marked increase in the ratio of zooplankton to phytoplankton biomass, as well as an increase in the body size of cladocerans, emphasising the importance of enhanced top-down control for mitigating cyanobacterial blooms following extensive fish removal. Our results reveal that removal of small fish (here Japanese smelt) can lead to a reduction of the phytoplankton and cyanobacteria biomass through a trophic cascade in highland deep subtropical lakes. Thus fish removal may be a feasible additional restoration tool to external nutrient loading reduction in such lakes.

An Improved Transfer Learning Model for Cyanobacterial Bloom Concentration Prediction

The outbreak of cyanobacterial blooms is a serious water environmental problem, and the harm it brings to aquatic ecosystems and water supply systems cannot be underestimated. It is very important to establish an accurate prediction model of cyanobacterial bloom concentration, which is a challenging issue. Machine learning techniques can improve the prediction accuracy, but a large amount of historical monitoring data is needed to train these models. For some waters with an inconvenient geographical location or frequent sensor failures, there are not enough historical data to train the model. To deal with this problem, a fused model based on a transfer learning method is proposed in this paper. In this study, the data of water environment with a large amount of historical monitoring data are taken as the source domain in order to learn the knowledge of cyanobacterial bloom growth characteristics and train the prediction model. The data of the water environment with a small amount of historical monitoring data are taken as the target domain in order to load the model trained in the source domain. Then, the training set of the target domain is used to participate in the inter-layer fine-tuning training of the model to obtain the transfer learning model. At last, the transfer learning model is fused with a convolutional neural network to obtain the prediction model. Various experiments are conducted for a 2 h prediction on the test set of the target domain. The results show that the proposed model can significantly improve the prediction accuracy of cyanobacterial blooms for the water environment with a low data volume.

Cumulative Effects of Physical, Chemical, and Biological Measures on Algae Growth Inhibition

Measures based on concurrent alterations of an environment’s physical, chemical, and biological factors are commonly adopted to control harmful algal blooms (HABs). It was postulated that the combinations and interactions of multiple measures could exert cumulative effects (as the overall effect may or may not be equal to the additive sum for each measure individually). However, few studies have further assessed whether the cumulative effect is synergistic, additive, or antagonistic. This study proposed a framework to distinguish and quantify the cumulative effects. We also designed an experiment to investigate the cumulative effect of the combined utilization of physical (flow velocity), chemical (copper), and biological (propionamide) measures on algae growth inhibition. The results show that the cumulative effect of physical and chemical measures on algae growth inhibition was antagonistic; the cumulative effect of physical and biological measures was antagonistic; the cumulative effect of chemical and biological measures was synergistic, and the cumulative effect of all the measures together tended to be antagonistic. These results showed that the synergistic interactions between chemical and biological measures produced antagonistic effects when physical measures were added. Through response surface methodology analysis, we also found that the physical factor was the most significant factor affecting the cumulative effect, followed by the chemical factor and then the biological factor. Our results provide a more detailed understanding of the interaction patterns among multiple measures that affect algal growth. Importantly, this understanding can be further integrated into future strategy development to fully exploit the potential of the cumulative effect at its maximum performance.

Nonlinear causal analysis reveals an effective water level regulation approach for phytoplankton blooms controlling in reservoirs

Reservoirs are a rapidly increasing water body providing water supply, irrigation, and many other benefits for human societies globally. However, due to changes in hydrological conditions, building reservoirs tends to bring adverse effects such as eutrophication and phytoplankton blooms, reducing the ecosystem service values. This study focuses on using the empirical dynamic modeling (EDM), an emerging approach for nonlinear analysis, to investigate the nonlinear causal relationship of water level fluctuation (WLF) on phytoplankton biomass and then develop a quantitative model guiding effective phytoplankton blooms controlling based on water level regulations in reservoirs. Specifically, with 9-year continued daily observed data in the Three Gorges Reservoir, we examined the causal effects of different WLF parameters on the dynamics of phytoplankton blooms for the first time. We found that the water level change in the past 24 h (ΔWL) has the strongest causal effect on the daily dynamics of phytoplankton biomass among all WLF parameters (ΔWL, |ΔWL|, and the water level), with a time lag of 2 days. Moreover, EDM revealed a nonlinear relationship between ΔWL and daily dynamics of phytoplankton biomass and achieved a successful prediction for the chlorophyll a concentration 2-day ahead. Further scenario analyses found that both the rise and fall of water level will significantly reduce the chlorophyll a concentration when phytoplankton blooms occur. Nevertheless, on the whole, the rising water level has a more substantial effect on phytoplankton blooms than falling the water level. This result reveals that regulating ΔWL is a simple and effective approach in controlling phytoplankton blooms in reservoirs. Our study reported the nonlinear causal effect of ΔWL on the dynamics of chlorophyll a and provided a quantitative approach guiding effective phytoplankton blooms controlling based on the water level regulation, which might have a broad application in algal blooms controlling in reservoirs and similar waterbodies.

Detection of Microcystin-LR in the Cells and Natural Lake Water Samples by A Unique Fluorescence-Based Method

Microcystin-LR (MC-LR) is widely distributed in natural lakes and could strongly inhibit protein phosphatase activity; it is also a potent liver tumor promoter. Over the last two decades, tremendous efforts have been devoted to enhance the detection of MC-LR in water samples. However, the traditional method is complex and costly, and achieving the fast, sensitive, and accurate determination of MC-LR in the cells and natural lakes by using fluorescence signal changes is fairly difficult. Our work explores novel fluorescent probes that are capable of concurrently analyzing and detecting MC-LR in the cells and water. In this study, we introduce, for the first time, 5-AF and 6-AF as small-molecule fluorescent probes suitable for MC-LR detection in the cells and water samples based on fluorescence signal changes. We titrated 5-AF and 6-AF with MC-LR in pure water, scanned the fluorescence of the sample, and then obtained the equation the fluorescence intensity versus MC-LR concentration curve. MC-LR in lake water samples was crudely purified, and then 5-AF was added to measure its fluorescence peak. The fluorescence intensity of 5-AF is significantly enhanced with increasing MC-LR concentration. This enhancement trend is stable and could be mathematically modeled. We also comprehensively analyzed the mechanism and recognition principle of the probe response to MC-LR in natural lake water. Moreover, we believe that 5-AF may be capable of detecting exogenous MC-LR in cells. The results of this study reveal that these unique fluorescent probes may be applied to construct near-infrared fluorescent probes that could detect MC-LR levels in vivo.