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

Vigilance against climate change-induced regime shifts for phosphorus restoration in shallow lake ecosystems

The dual pressure of anthropogenic activities and frequent extreme weather events has triggered a transition from macrophyte to algal dominance in shallow lakes. Phosphorus (P) is the key driver of regime shifts that can lead to a decline in the stability and resilience of lake ecosystems. However, the mechanisms underlying such regime shifts, and the effects of state transitions on internal P loading during macrophyte restoration in large shallow eutrophic lakes, remain to be fully elucidated. This study utilised long-term in situ monitoring data, across three distinct lake states (bare ground, macrophyte-dominated stage, and algae-dominated stage) to elucidate the accumulation and release mechanisms of sedimentary P during regime shifts. The findings demonstrated that the rehabilitation of submerged plants efficiently reduced internal P loading (water column P, sediment P fractions, and P flux), while the persistence of algal blooms was driven by the reductive release of Fe-P from sediments and the dissolution of Al-P from suspended particulate matter. High temperature, low dissolved oxygen, and high pH largely modulate the pathways and mechanisms of P supply during regime shifts. The combined pressures of extreme weather (heavy rainfall, strong winds, and extreme heat) and trophic cascades from fish stocking can trigger a shift from macrophytes to algae in shallow lakes. Appropriate management of the structure and biomass of aquatic animal communities (e.g., small-bodied or omnibenthivorous fish) and optimization of the food web structure can effectively improve water quality and maintain ecosystem stability. These findings enrich the theoretical understanding of regime-shift mechanisms from an ecosystem perspective and offer novel insights into P remediation in large shallow eutrophic lakes.

Climate change promotes harmful algal blooms in China's lakes and reservoirs despite significant nutrient control efforts

The increasing frequency and magnitude of harmful algal blooms (HABs) threatens the integrity of aquatic ecosystem functioning and human health worldwide. Nutrient reduction strategies have been widely used to mitigate HABs, but their efficiency in light of on-going changes in climate remains unclear. Here, we assembled an 18-year (2005-2022) national water quality dataset for 97 lakes across China. We examined the dynamics of HABs and their response to nutrient reduction under historical climate change trends using a combination of statistical and process-based modeling. The results revealed an increase in HABs despite a widespread decline in ambient nutrient levels, with 80.5 % of lakes experiencing a decline in phosphorus but 61.8 % displaying an increase in Chlorophyll a concentrations. We attributed this counterintuitive trend to climatic warming, which can hinder the mitigation of HABs until the ambient nutrients reach sufficiently low levels. The extent of HAB promotion by warming varied spatially, with a distinctly greater proliferation in China's lower-latitude lakes (<35°N), primarily due to the prevailing warmer temperatures. Notwithstanding the persistence of HABs in China's lakes, national-scale modeling suggests that nutrient loading control remains valuable in protecting our water resources, as the HAB risk would have been 32.6 % higher due to climate change. The anticipated future nutrient reduction efforts in China are expected to alleviate higher latitude lakes from frequent HAB occurrences, but lower latitude lakes will still face considerable HAB risks. Our national-scale assessment demonstrates a variant efficiency of nutrient reduction in offsetting HAB risks amid rapid climate change, and highlights the need of adaptively enhancing our mitigation strategies in response to the ever-changing ecological conditions.

Key role of macrophyte-dominated habitats over cyanobacteria-dominated habitats in stability of microbial sulfur cycling in freshwater lakes

Despite extensive studies on sulfate-reducing bacteria (SRB) and sulfur-oxidizing bacteria (SOB) in marine and deep stratified ecosystems, their spatiotemporal dynamics and ecological roles in shallow freshwater lakes remain poorly understood, especially with habitat shifts driven by eutrophication and climate change. Here, we monitored seasonal changes in sediment and porewater chemical parameters, and applied high-throughput sequencing and co-occurrence network analysis to compare SRB and SOB communities in surface sediments from cyanobacteria-dominated (ZSB) and macrophyte-dominated (XKB) habitats of Lake Taihu across four seasons. In ZSB, seasonal variations in acid volatile sulfide (AVS), dissolved sulfide (∑H2S), and Fe(II) were the primary drivers of sulfur bacterial communities. In contrast, SRB in XKB were mainly influenced by total nitrogen (TN) and total phosphorus (TP), while SOB were regulated by spatial heterogeneity, with AVS as the key driver. Spore-forming SRB such as Desulfotomaculum were distinctly enriched in ZSB, indicating an adaptive strategy to environmental fluctuations. Notably, Cupriavidus, a genus rarely linked to sulfur oxidation, was the dominant SOB genus in both habitats. Co-occurrence network analysis showed that dominant genera in ZSB acted as network hubs, indicating greater vulnerability to environmental changes in cyanobacteria-dominated habitats. Conversely, XKB displayed evenly distributed interaction networks without dominant network hubs, enhancing resilience to environmental fluctuations. These findings provide new insight into how macrophyte-dominated habitats enhance the resilience of shallow freshwater lakes to algal bloom expansion via stabilized sulfur cycling. Hydrological and hydrodynamic factors should be considered in future to better elucidate sulfur cycling in freshwater lakes.

Periodically asymmetric responses of deep chlorophyll maximum to light and thermocline in a clear monomictic lake: Insights from monthly and diel scale observations

Deep chlorophyll maximum (DCM), a chlorophyll peak in the water column, has important implications for biogeochemical cycles, energy flow and water surface algal blooms in deep lakes. However, how an observed periodically asymmetric DCM response to environmental variables remains unclear, limiting our in-depth understanding and effective eco-environmental management of deep lakes. Based on both monthly field investigations in 2021 and diel continuous observations in 2021-2023 in clear, monomictic Lake Fuxian, Southwest China, the temporal dynamics and drivers of DCM were examined and periodic features of DCM were found, with a formation period (FP, February-July) and a weakening period (WP, August-December). On the monthly scale, although DCM dynamics were partly attributed to thermocline structures, the role of light penetration depths varied with period. In the FP, the influence of light on DCM was direct, i.e., increased depth and thickness but decreased magnitude. Differently, the influence of light mainly occurred by affecting thermocline structures in the WP, where water quality was another important driver. On the diel scale, light was a major reason for a thicker and lower (magnitude) DCM during day than at night, and the response of DCM to environmental factors between the FP and WP differed also more during day. This periodically asymmetric response of daytime DCM not only being caused by light but possibly also related to other physical factors such as lake surface water temperature, wind speed and precipitation. Bayesian network modelling suggested that water darkening and stratification intensification may promote a shallower, thinner and larger (magnitude) DCM in both FP and WP, but achieving such changes in DCM requires different light and thermocline thresholds. Our findings provide new information valuable for modelling DCM and for predicting the related surface algal blooms in deep lakes under climate change and eutrophication.

Drinking water safety improvement and future challenge of lakes and reservoirs

To meet the Sustainable Development Goal (SDG) target 6.1, China has undertaken significant initiatives to address the uneven distribution of water resources and to enhance water quality. Since 2000, China has invested heavily in the water infrastructure of numerous reservoirs, with a total storage capacity increase of 4.704 × 1011 m3 (an increase of 90.8%). These reservoirs have significantly enhanced the available freshwater resources for drinking water. Concurrently, efforts to improve water quality in lakes and reservoirs, facilitated by nationwide water quality monitoring, have been successful. As a result, an increasing lakes and reservoirs are designated as centralized drinking water sources (CDWSs) in China. Among the 3441 CDWSs across all provinces, 40.8% are sourced from lakes and reservoirs, 32.6% from rivers, and 26.6% from groundwater in 2023. Notably, from 2016 to 2023, the percentage of lakes and reservoirs categorized as CDWSs has increased consistently across all 29 provinces. This progress has enabled 561.4 million urban residents to access improved drinking water sources in 2022, compared to 303.4 million in 2004. Our findings underscore the pivotal role of water infrastructure construction and water quality improvement jointly promoting lakes and reservoirs as vital drinking water sources. Nevertheless, the nationwide occurrence of algal blooms has surged by 113.7% from the 2000s to the 2010s , which is a considerable challenge to drinking water safety. Fortunately, algal blooms have been markedly alleviated in past four years. However, it is still crucial to acknowledge that lakes and reservoirs face the challenges of algal blooms, and associated toxic microcystin and odor compounds.

Potential health risk assessment of cyanobacteria across global lakes

Cyanobacterial blooms pose environmental and health risks due to their production of toxic secondary metabolites. While current methods for assessing these risks have focused primarily on bloom frequency and intensity, the lack of comprehensive and comparable data on cyanotoxins makes it challenging to rigorously evaluate these health risks. In this study, we examined 750 metagenomic data sets collected from 103 lakes worldwide. Our analysis unveiled the diverse distributions of cyanobacterial communities and the genes responsible for cyanotoxin production across the globe. Our approach involved the integration of cyanobacterial biomass, the biosynthetic potential of cyanotoxin, and the potential effects of these toxins to establish potential cyanobacterial health risks. Our findings revealed that nearly half of the lakes assessed posed medium to high health risks associated with cyanobacteria. The regions of greatest concern were East Asia and South Asia, particularly in developing countries experiencing rapid industrialization and urbanization. Using machine learning techniques, we mapped potential cyanobacterial health risks in lakes worldwide. The model results revealed a positive correlation between potential cyanobacterial health risks and factors such as temperature, N2O emissions, and the human influence index. These findings underscore the influence of these variables on the proliferation of cyanobacterial blooms and associated risks. By introducing a novel quantitative method for monitoring potential cyanobacterial health risks on a global scale, our study contributes to the assessment and management of one of the most pressing threats to both aquatic ecosystems and human health.

IMPORTANCE

Our research introduces a novel and comprehensive approach to potential cyanobacterial health risk assessment, offering insights into risk from a toxicity perspective. The distinct geographical variations in cyanobacterial communities coupled with the intricate interplay of environmental factors underscore the complexity of managing cyanobacterial blooms at a global scale. Our systematic and targeted cyanobacterial surveillance enables a worldwide assessment of cyanobacteria-based potential health risks, providing an early warning system.

Metagenomic analysis unveiled the response of microbial community and antimicrobial resistome in natural water body to duck farm sewage

Sewages from duck farms are often recognized as a major source of antimicrobial resistance and pathogenic bacteria discharged to natural water bodies, but few studies depicted the dynamic changes in resistome and microbial communities in the rivers under immense exposure of sewage discharge. In this study, we investigated the ecological and environmental risks of duck sewages to the rivers that geographically near to the duck farms with short-distance (<1 km) using 16S rRNA amplicon and metagenomic sequencing. The results showed that a total of 20 ARG types were identified with abundances ranged from 0.61 to 1.33 cpc. Of note, the genes modulate resistances against aminoglycoside, bacitracin and beta-lactam were the most abundant ARGs. Limnohabitans, Fluviibacter and Cyanobium were the top 3 predominant genera in the microbial community. The alpha diversity of overall microbial community decrease while the abundance of pathogen increase during the input of sewage within 200 m. Sul1 and bacA were the dominant ARGs brought from duck farm sewage. The community variations of ARGs and microbiome were primarily driven by pH and temperature. Total phosphorus was significantly correlated to alpha diversity and top 30 ARGs subtype. Stochastic processes was the dominated microbial assembly pattern and did not be altered by sewage. We also highlighted the ecological risk caused by blaGES which possibly could be mitigated by Cyanobacteria, and the natural water body can purify partial ARGs as well as microbiome from duck farms sewage. These findings expanded our knowledge regarding the ecological risks by wastes from the livestock farm, and underscoring the necessity to monitor ARGs in farm-surrounding water bodies.

Temporal shifts in the phytoplankton network in a large eutrophic shallow freshwater lake subjected to major environmental changes due to human interventions

Phytoplankton communities are crucial components of aquatic ecosystems, and since they are highly interactive, they always form complex networks. Yet, our understanding of how interactive phytoplankton networks vary through time under changing environmental conditions is limited. Using a 29-year (339 months) long-term dataset on Lake Taihu, China, we constructed a temporal network comprising monthly sub-networks using "extended Local Similarity Analysis" and assessed how eutrophication, climate change, and restoration efforts influenced the temporal dynamics of network complexity and stability. The network architecture of phytoplankton showed strong dynamic changes with varying environments. Our results revealed cascading effects of eutrophication and climate change on phytoplankton network stability via changes in network complexity. The network stability of phytoplankton increased with average degree, modularity, and nestedness and decreased with connectance. Eutrophication (increasing nitrogen) stabilized the phytoplankton network, mainly by increasing its average degree, while climate change, i.e., warming and decreasing wind speed enhanced its stability by increasing the cohesion of phytoplankton communities directly and by decreasing the connectance of network indirectly. A remarkable shift and a major decrease in the temporal dynamics of phytoplankton network complexity (average degree, nestedness) and stability (robustness, persistence) were detected after 2007 when numerous eutrophication mitigation efforts (not all successful) were implemented, leading to simplified phytoplankton networks and reduced stability. Our findings provide new insights into the organization of phytoplankton networks under eutrophication (or re-oligotrophication) and climate change in subtropical shallow lakes.

Increased extinction probability and altered physiological characteristics in pirimicarb-tolerant Daphnia magna

We evaluated the physiological characteristics of chemical-tolerant cladocerans. Over the course of 26 generations (F25), Daphnia magna was continuously exposed to pirimicarb (carbamate) solutions (0, 3.8, 7.5, and 15 µg/L) in sub-lethal or lethal levels. The 48 h EC50 values (29.2-29.9 µg/L) for 7.5 and 15 µg/L exposure groups were found to be nearly two times higher than that in the control (17.2 µg/L). Subsequently, we investigated whether the extinction probability changed when the chemical-tolerant daphnids were fed two different types of food, Chlorella vulgaris and Synechococcus leopoliensis. Furthermore, we ascertained how chemical tolerance influences respiration and depuration rates. The 48 h EC50 value was positively related to the extinction probability when the daphnids were fed S. leopoliensis. Because the measured lipid content of S. leopoliensis was three times lower than that of C. vulgaris, the tolerant daphnids struggled under nutrient-poor conditions. Respiration rates across all pirimicarb treatment groups were higher than those in the control group, suggesting that they may produce large amounts of energy through respiration to maintain the chemical tolerance. Since the pirimicarb depuration rate for 7.5 µg/L exposure groups was higher than that in the control, the altered metabolic/excretion rate may be one factor for acquiring chemical tolerance. These altered physiological characteristics are crucial parameters for evaluating the mechanisms of chemical tolerance and associated fitness costs.

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.