Nutrient reduction mitigated the expansion of cyanobacterial blooms caused by climate change in Lake Taihu according to Bayesian network models

Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.

Potential gap in understanding cyanoHABs: Light-dependent morphological variations in colonial cyanobacterium Microcystis

Colony formation is a crucial characteristic of Microcystis, a cyanobacterium known for causing cyanobacterial harmful algal blooms (cyanoHABs). It has been observed that as Microcystis colonies grow larger, they often become less densely packed, which correlates with a decrease in light penetration. The objective of this study was to investigate the effects of light limitation on the morphological variations in Microcystis, particularly in relation to the crowded cellular environment. The results indicated that when there was sufficient light (transmittance = 100 %) to support a growth rate of 0.11±0.01 day-1, a significant increase in colony size was found, from 466±15 μm to 1030±111 μm. However, under light limitation (transmittance = 50 % - 1 %) where the growth rate was lower than 0, there was no significant improvement in colony size. Microcystis in the light limitation groups exhibited a loose cell arrangement and even the presence of holes or pores within the colony, confirming the negative correlation between colony size and cell arrangement. This pattern is driven by regional differences in growth within the colony, as internal cells have a significantly lower frequency of division compared to peripheral cells, due to intra-colony self-shading (ICSS). The research demonstrates that Microcystis can adjust its cell arrangement to avoid excessive self-shading, which has implications for predicting and controlling cyanoHABs. These findings also contribute to the understanding of cyanobacterial variations and can potentially inform future research on the diverse phycosphere.

High-frequency data significantly enhances the prediction ability of point and interval estimation

Accurate prediction of dissolved oxygen (DO) dynamics is crucial for understanding the influence of environmental factors on the stability of aquatic ecosystem. However, limited research has been conducted to determine the optimal frequency of water quality monitoring that ensures continuous assessment of water health while minimizing costs. To address these challenges, the present study developed a hybrid stochastic hydrological model (i.e., ARIMA-GARCH hybrid model) and machine learning (ML) models. The objective of this study is to identify the best-performing model and establish the optimal monitoring frequency. Results revealed that high-frequency DO monitoring data exhibit greater variability compared to low-frequency data. Moreover, the ARIMA-GARCH model demonstrates promising potential in predicting DO concentrations for low-frequency monitoring data, surpassing ML models in performance. Furthermore, increasing the monitoring frequency significantly improves the prediction accuracy of models, regardless of whether point (with lower R2 values of 0.64 and 0.51 for daily detection than these of every 15 min (0.96 and 0.99) at CHQ and LHT, respectively) or interval predictions (with RIW higher values of 2.00 and 1.55 for daily detection higher than these of 0.02 and 0.16 in every 15 min at CHQ and LHT, respectively) are considered. Additionally, a 4 hourly monitoring frequency was found to be optimal for water quality assessment using each model. These findings identify the superior performing of the ARIMA-GARCH model and highlight the crucial role of monitoring frequency in enhancing DO prediction and improving model performance.

MODIS Terra and Aqua images bring non-negligible effects to phytoplankton blooms derived from satellites in eutrophic lakes

Phytoplankton-induced lake eutrophication has drawn ongoing interest on a global scale. One of the most popular remote sensing satellite data for observing long-term dynamic changes in phytoplankton is Moderate-resolution Imaging Spectroradiometer (MODIS). However, it is worth noting that MODIS provides two images with different transit times: Terra (local time, about 10:30 am) and Aqua (local time, about 1:30 pm), which may result in a considerable bias in monitoring phytoplankton bloom areas due to the rapid migration of phytoplankton under wind or hydrodynamic conditions. To analyze this quantitatively, we selected MODIS Terra and Aqua images to generate datasets of phytoplankton bloom areas in Lake Taihu from 2003 to 2022. The results showed that Terra more frequently detected larger ranges of phytoplankton blooms than Aqua, whether on daily, monthly, or annual scales. In addition, long-term trend changes, seasonal characteristics, and abrupt years also varied with different transit times. Terra detected mutation years earlier, while Aqua displayed more pronounced seasonal characteristics. There were also differences in sensitivity to climate factors, with Terra being more responsive to temperature and wind speed on monthly and annual scales, while Aqua was more sensitive to nutrient and meteorological factors. These conclusions have also been further confirmed in Lake Chaohu, Lake Dianchi, and Lake Hulun. In conclusion, our findings strongly advocate for a linear relationship to fit Terra to Aqua results to mitigate long-term monitoring errors of phytoplankton blooms in inland lakes (R2 = 0.70, RMSE = 101.56). It is advised to utilize satellite data with transit times between 10 am and 1 pm to track phytoplankton bloom changes and to consider the diverse applications resulting from the transit times of Terra and Aqua.

Irreversible community difference between bacterioplankton generalists and specialists in response to lake dredging

Understanding response of bacterioplankton community responsible for maintaining ecological functions of aquatic ecosystems to environmental disturbance is an important subject. However, it remains largely unclear how bacterioplankton generalists and specialists respond to dredging disturbance. Illumina MiSeq sequencing and statistical analyses were used to evaluate landscape patterns, evolutionary potentials, environmental adaptability, and community assembly processes of generalists and specialists in response to dredging in eutrophic Lake Nanhu. The Proteobacteria and Actinobacteria dominated bacterioplankton communities of generalists and specialists, and abundances of Proteobacteria decreased and Actinobacteria increased after dredging. The generalists displayed higher phylogenetic distance, richness difference, speciation rate, extinction rate, and diversification rate as well as stronger environmental adaptation than that of specialists. In contrast, the specialists rather than generalists showed higher community diversity, taxonomic distance, and species replacement as well as closer phylogenetic clustering. Stochastic processes dominated community assemblies of generalists and specialists, and stochasticity exhibited a larger effect on community assembly of generalists rather than specialists. Our results emphasized that lake dredging could change landscape patterns of bacterioplankton generalists and specialists, whereas the short-term dredging conducted within one year was unable to reverse community difference between generalists and specialists. Our findings extend our understanding of how bacterioplankton generalists and specialists responding to dredging disturbance, and these findings might in turn call on long-term dredging for better ecological restoration of eutrophic lakes.