Xuemei Liu1, Liwen Chen2, Guangxin Zhang3, Jingjie Zhang4, Yao Wu2, Hanyu Ju1. 1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. Changchun 130102, China; University of the Chinese Academy of Sciences, Beijing 100049, China. 2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. Changchun 130102, China. 3. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. Changchun 130102, China. Electronic address: zhgx@iga.ac.cn. 4. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences. Changchun 130102, China; Environmental Research Institute, National University of Singapore, Kent Ridge 117576, Singapore; Shenzhen Municipal Engineering Lab of Environmental IoT Technologies, Southern University of Science and Technology, Shenzhen, 518055, China. Electronic address: lakejz@gmail.com.
Abstract
Understanding the limiting factors of phytoplankton growth and competition is crucial for the restoration of aquatic ecosystems. However, the role and synergistic effect of co-varying environmental conditions, such as nutrients and light on the succession of phytoplankton community remains unclear. In this study, a hydrodynamic-ecological modeling approach was developed to explore phytoplankton growth and succession under co-varying environmental conditions (nutrients, total suspended solids (TSS) and variable N:P ratios) in a large shallow lake called Lake Chagan, in Northeast China. A phytoplankton bloom model was nested in the ecological modeling approach. In contrast to the traditonal ecological modeling, competition between phytoplankton species in our study was modeled at both the species/functional group and phenotype levels. Six phytoplankton functional groups, namely diatoms, green algae, Anabaena, Microcystis, Aphanizomenon and Oscillatoria and each of them with three limitation types (i.e., light-limitation, nitrogen-limitation and phosphorus-limitation) were included in the bloom model. Our results demonstrated that the average biomass proportion of the three limitation types (light-limitation, nitrogen-limitation and phosphorus-limitation) in the six phytoplankton function groups accounted for approximately 50%, 37% and 23% of the total phytoplankton biomass, respectively. TSS suppressed the growth of diatoms and green algae, but favored the dominance of cyanobacteria in Lake Chagan, especially in the turbid water phase (TSS ≥ 60 mg/L). In addition, it was reported that the potential of either N-fixing or non-N-fixing cyanobacterial blooming along the gradients of N:P ratios could exist under the influence of the co-environmental factors in the lake. The proportion of non-N-fixing cyanobacteria (i.e., Microcystis and Oscillatoria) exceeded the proportion of N-fixing cyanobacteria (i.e., Anabaena and Aphanizomenon) when the N:P ratios exceeded 20. Non-N-fixing cyanobacteria would become dominant at higher TSS concentrations and lower light intensities in the turbid water. N-fixing cyanobacteria favored lower N:P ratios and higher light intensities in the clearwater phase (where TSS ≤ 60 mg/L). To sustain a good ecological status in the lake, our results suggest that nutrient and TSS levels in the lake should be maintained at or below the thresholds (TN ≤ 1.5 mg/L; TP ≤ 0.1 mg/L; N:P ratios between 15 and 20; and TSS ≤ 60 mg/L). These findings can help improve water quality management practices to restore aquatic ecosystems.
Understanding the limiting factors of phytoplankton growth and competition is crucial for the restoration of aquatic ecosystems. However, the role and synergistic effect of co-varying environmental conditions, such as nutrients and light on the succession of phytoplankton community remains unclear. In this study, a hydrodynamic-ecological modeling approach was developed to explore phytoplankton growth and succession under co-varying environmental conditions (nutrients, total suspended solids (TSS) and variable N:P ratios) in a large shallow lake called Lake Chagan, in Northeast China. A phytoplankton bloom model was nested in the ecological modeling apn>proach. In contrast to the traditonal ecological modeling, compn>etition between phytoplankton spn>ecies in our study was modeled at both the spn>ecies/functional groupn> and phenotype levels. Six phytoplankton functional groupn>s, namely diatoms, n>an class="Species">green algae, Anabaena, Microcystis, Aphanizomenon and Oscillatoria and each of them with three limitation types (i.e., light-limitation, nitrogen-limitation and phosphorus-limitation) were included in the bloom model. Our results demonstrated that the average biomass proportion of the three limitation types (light-limitation, nitrogen-limitation and phosphorus-limitation) in the six phytoplankton function groups accounted for approximately 50%, 37% and 23% of the total phytoplankton biomass, respectively. TSS suppressed the growth of diatoms and green algae, but favored the dominance of cyanobacteria in Lake Chagan, especially in the turbid water phase (TSS ≥ 60 mg/L). In addition, it was reported that the potential of either N-fixing or non-N-fixing cyanobacterial blooming along the gradients of N:P ratios could exist under the influence of the co-environmental factors in the lake. The proportion of non-N-fixing cyanobacteria (i.e., Microcystis and Oscillatoria) exceeded the proportion of N-fixing cyanobacteria (i.e., Anabaena and Aphanizomenon) when the N:P ratios exceeded 20. Non-N-fixing cyanobacteria would become dominant at higher TSS concentrations and lower light intensities in the turbid water. N-fixing cyanobacteria favored lower N:P ratios and higher light intensities in the clearwater phase (where TSS ≤ 60 mg/L). To sustain a good ecological status in the lake, our results suggest that nutrient and TSS levels in the lake should be maintained at or below the thresholds (TN ≤ 1.5 mg/L; TP ≤ 0.1 mg/L; N:P ratios between 15 and 20; and TSS ≤ 60 mg/L). These findings can help improve water quality management practices to restore aquatic ecosystems.