Size-related effects of physical factors on phytoplankton communities

Potential geographical distribution of harmful algal blooms caused by the toxic dinoflagellate Karenia mikimotoi in the China Sea

The fish-killing dinoflagellate Karenia mikimotoi frequently blooms in China and poses a threat to food safety and human health. To better understand harmful algal blooms (HABs) caused by K. mikimotoi and predict the risk of HABs under climate change, the combined effect of nitrate and norfloxacin (NOR) on the growth of K. mikimotoi was tested. A growth model was used to test the effects of nutrients and pollutants on the carrying capacity of the unicellular algae. The carrying capacity increased with increasing concentrations of nitrate and NOR, reaching a maximum at 62.2 μmol L-1 of nitrate and 9.03 mg L-1 of NOR. The calculated carrying capacity of K. mikimotoi in the China Sea showed a declining trend from nearshore to offshore, with a value >30 × 106 cells L-1 in the estuary of the Changjiang River and Hangzhou Bay. The HAB index proposed in this study as a measurement of HAB risk was constructed using the carrying capacity and relative abundance from the MaxEnt (maximum entropy) model. The index showed that HABs caused by K. mikimotoi consecutively occurred in Zhejiang and Fujian coastal waters and predicted that they will continue until 2100, regardless of the greenhouse gas emission scenario. The center of the integrated area moved northward, with a range of 120-900 km. The HAB index integrates the characteristics of the carrying capacity and suitability of habitats, and expresses the information contained in the intensive and extensive variables that affect HAB occurrence. This index is a promising predictor of HAB risk in coastal waters.

A light-limited growth model considering the nutrient effect for improved understanding and prevention of macroalgae bloom

A useful growth process model for Ulva prolifera with light limitation considering the nutrient effect was proposed to better understand the development of macroalgae bloom. The interrelationship between light and nutrient limitation was demonstrated to obtain the mechanism. As a case study, thresholds of light and nutrients for different stages of Ulva prolifera growth were estimated using the proposed model. Limits of light intensity, nitrate, and phosphate concentration on the initial growth were found to be 40.0 W m^-2, 6.5 μmol L^-1, and 0.27 μmol L^-1, respectively. The minimal light intensity for initial growth was found to increase monotonously with decrease in the nutrient concentration. It was also observed that the minimal light intensity for growth increases as the density of Ulva prolifera increases and the nutrient concentration decreases. Similarly, the minimal nutrient concentration for initial growth increases monotonously with decrease in the light intensity. In addition, the minimal nutrient concentration for growth increases with increase in the density of Ulva prolifera and decrease in the light intensity. It was demonstrated that the phosphate limitation on the initial growth of Ulva prolifera seedling can occur in most coastal waters of the southern Yellow Sea and a tendency of approaching the phosphate limitation on the growth of the floating thalli of Ulva prolifera exists. Evidence was provided to support the argument that the macroalgae thalli from aquaculture rafts, rather than that from seedlings or spores, can contribute to the original biomass of the floating green Ulva prolifera in the southern Yellow Sea. The model presented in this study can provide new insights into the interrelationship between the light and nutrient limitation, as well as into the growth mechanism of floating seaweeds. It can also provide a more accurate prediction of seaweed growth in light- and nutrient-limited environments.

The effects of turbulence on phytoplankton and implications for energy transfer with an integrated water quality-ecosystem model in a shallow lake

Turbulence has significant influences on the growth rate and community structure of phytoplankton in large shallow lakes. Phytoplankton in moving water may be influenced by turbulence and nutrient concentration gradients on a short time scale. To assess this issue, our research used an ensemble water quality and ecological model by internally coupling the three-dimensional hydrodynamic model, the Environmental Fluid Dynamics Code (EFDC), and the one-dimensional ecosystem model, PCLake. The results showed that turbulence dramatically inhibited phytoplankton growth, while nutrients had the opposite effect. In addition, turbulence was the key factor contributing to phytoplankton growth. However, the effects of turbulence on phytoplankton correlated with nutrient concentrations. For lower nutrient concentrations, phytoplankton growth was controlled by nutrients. Logistic regression models were established with the modeled chlorophyll a, total nitrogen (TN), total phosphorus (TP) and turbulent kinetic energy (K_e). The results also showed that turbulence could improve nutrient uptake by phytoplankton, especially at low nutrient levels. The effects of turbulence on phytoplankton may imply that energy transfer occurs between water turbulence and phytoplankton. Our study will provide insight into management and remediation strategies of ecosystems based on energy processes in the future.

Interactions between suspended particulate matter and algal cells contributed to the reconstruction of phytoplankton communities in turbulent waters

The effect of turbulence on phytoplankton growth has been widely studied; however, its effects with respects to suspended particulate matter (SPM) on the development of phytoplankton communities and the behavioral responses of phytoplankton to turbulence and SPM are poorly understood. Here, an approximately homogeneous turbulence simulation system (AHTS, mainly consisting of an oscillating-grid apparatus) was established to gain insight into the mechanisms underlying phytoplankton community responses in turbid, well-mixed waters. The results revealed that maintaining the turbulence dissipation rates (Ɛ) of 2.25 × 10^-3 and 1.80 × 10^-2 m²/s³ caused significant reductions in algal density, and the effects could be substantially enhanced when 500 mg/L of SPM were added before day 12. In contrast to the constant decrease of algal density for the Ɛ of 2.25 × 10^-3 m²/s³, a dramatic increase in the phytoplankton density occurred after 16 days of incubation for a Ɛ of 1.80 × 10^-2 m²/s³, irrespective of SPM. Addition of SPM in the Ɛ of 1.80 × 10^-2 m²/s³ treatments did not considerably affect the algal density profile compared to that without SPM, of which unicellular algae decreased and colonial algae dominated the phytoplankton community. On the other hand, the phytoplankton can regulate the SPM properties. During the 18 days' coincubation, extracellular polymeric substances (EPS) released from algal cells induced larger particle sizes and round surfaces of SPM, which can reduce the damage received to algal cells. Here we demonstrated that the phytoplankton communities could actively counteract the effects of turbulence + SPM and adapt the couple stress, jointly through the release of EPS, the modification of SPM surface properties and the conversion of their assemblage pattern, thereby contributing to rebalance the ecosystem. These findings highlight the strategies employed during the reconstruction of phytoplankton under the dual effects of turbulence and SPM for the first time, consequently enabling the forecasting of the dominant species of phytoplankton in turbulent waters.

How physiological and physical processes contribute to the phenology of cyanobacterial blooms in large shallow lakes: A new Euler-Lagrangian coupled model

Cyanobacterial blooms have emerged as one of the most severe ecological problems affecting large and shallow freshwater lakes. To improve our understanding of the factors that influence, and could be used to predict, surface blooms, this study developed a novel Euler-Lagrangian coupled approach combining the Eulerian model with agent-based modelling (ABM). The approach was subsequently verified based on monitoring datasets and MODIS data in a large shallow lake (Lake Taihu, China). The Eulerian model solves the Eulerian variables and physiological parameters, whereas ABM generates the complete life cycle and transport processes of cyanobacterial colonies. This model ensemble performed well in fitting historical data and predicting the dynamics of cyanobacterial biomass, bloom distribution, and area. Based on the calculated physical and physiological characteristics of surface blooms, principal component analysis (PCA) captured the major processes influencing surface bloom formation at different stages (two bloom clusters). Early bloom outbreaks were influenced by physical processes (horizontal transport and vertical turbulence-induced mixing), whereas buoyancy-controlling strategies were essential for mature bloom outbreaks. Canonical correlation analysis (CCA) revealed the combined actions of multiple environment variables on different bloom clusters. The effects of buoyancy-controlling strategies (ISP), vertical turbulence-induced mixing velocity of colony (VMT) and horizontal drift velocity of colony (HDT) were quantitatively compared using scenario simulations in the coupled model. VMT accounted for 52.9% of bloom formations and maintained blooms over long periods, thus demonstrating the importance of wind-induced turbulence in shallow lakes. In comparison, HDT and buoyancy controlling strategies influenced blooms at different stages. In conclusion, the approach developed here presents a promising tool for understanding the processes of onshore/offshore algal blooms formation and subsequent predicting.

Factors associated with blooms of cyanobacteria in a large shallow lake, China

BACKGROUND

Eutrophication of freshwater systems can result in blooms of phytoplankton, in many cases cyanobacteria. This can lead to shifts in structure and functions of phytoplankton communities adversely affecting the quality of drinking water sources, which in turn impairs public health. Relationships between structures of phytoplankton communities and concentrations of the toxicant, microcystin-leucine-arginine (MC-LR), have not been well examined in large shallow lakes. The present study investigated phytoplankton communities at seven locations from January to December of 2015 in Tai Lake, and relationships between structures and diversities of phytoplankton communities and water quality parameters, including concentrations of MC-LR and metals, were analyzed.

RESULTS

A total of 124 taxa of phytoplankton were observed, and the predominant taxa were Microcystis sp. and Dolichospermum flos-aquae of Cyanophyta and Planctonema sp. of Chlorophyta. The greatest diversities of phytoplankton communities, as indicated by species richness, Simpson, Shannon-Wiener, the Berger and Parker, and the Pielou evenness indices, were observed in spring. Furthermore, productivity of phytoplankton was significantly and negatively correlated with diversities. These results demonstrated that Simpson, Shannon-Wiener, the Berger and Parker, and the Pielou evenness indices of phytoplankton communities were significantly related to trophic status and overall primary productivity in Tai Lake. In addition, temperature of surface water, pH, permanganate index, biochemical oxygen demand, total phosphorus, arsenic, total nitrogen/total phosphorous ratio, and MC-LR were the main factors associated with structures of phytoplankton communities in Tai Lake.

CONCLUSION

The present study provided helpful information on phytoplankton community structure and diversity in Tai Lake from January to December of 2015. Our findings demonstrated that Simpson, Shannon-Wiener, the Berger and Parker, and the Pielou evenness indices could be used to assess and monitor for status and trends in water quality of Tai Lake. In addition, MC-LR was one of the main factors associated with structures of phytoplankton communities in Tai Lake. The findings may help to address important ecological questions about the impact of a changing environment on biodiversity of lake ecosystems and the control of algae bloom. Further studies are needed to explore the relationship between MC-LR and phytoplankton communities in the laboratory.