Effects of multiple stressors on cyanobacteria abundance vary with lake type

Phytoplankton biomass in northern lakes reveals a complex response to global change

Global change may introduce fundamental alterations in phytoplankton biomass and community structure that can alter the productivity of northern lakes. In this study, we utilized Swedish and Finnish monitoring data from lakes that are spatially (135 lakes) and temporally (1995-2019, 110 lakes) extensive to assess how phytoplankton biomass (PB) of dominant phytoplankton groups related to changes in water temperature, pH and key nutrients [total phosphorus (TP), total nitrogen (TN), total organic carbon (TOC), iron (Fe)] along spatial (Fennoscandia) and temporal (25 years) gradients. Using a machine learning approach, we found that TP was the most important determinant of total PB and biomass of a specific species of Raphidophyceae - Gonyostomum semen - and Cyanobacteria (both typically with adverse impacts on food-webs and water quality) in spatial analyses, while Fe and pH were second in importance for G. semen and TN and pH were second and third in importance for Cyanobacteria. However, in temporal analyses, decreasing Fe and increasing pH and TOC were associated with a decrease in G. semen and an increase in Cyanobacteria. In addition, in many lakes increasing TOC seemed to have generated browning to an extent that significantly reduced PB. The identified discrepancy between the spatial and temporal results suggests that substitutions of data for space-for-time may not be adequate to characterize long-term effects of global change on phytoplankton. Further, we found that total PB exhibited contrasting temporal trends (increasing in northern- and decreasing in southern Fennoscandia), with the decline in total PB being more pronounced than the increase. Among phytoplankton, G. semen biomass showed the strongest decline, while cyanobacterial biomass showed the strongest increase over 25 years. Our findings suggest that progressing browning and changes in Fe and pH promote significant temporal changes in PB and shifts in phytoplankton community structures in northern lakes.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.

Prolonged exposure to environmental levels of microcystin-LR triggers ferroptosis in brain via the activation of Erk/MAPK signaling pathway

While existing research has illuminated the environmental dangers and neurotoxic effects of MC-LR exposure, the molecular underpinnings of brain damage from environmentally-relevant MC-LR exposure remain elusive. Employing a comprehensive approach involving RNA sequencing, histopathological examination, and biochemical analyses, we discovered genes differentially expressed and enriched in the ferroptosis pathway. This finding was associated with mitochondrial structural impairment and downregulation of Gpx4 and Slc7a11 in mice brains subjected to low-dose MC-LR over 180 days. Mirroring these findings, we noted reduced cell viability and GSH/GSSH ratio, along with an increased ROS level, in HT-22, BV-2, and bEnd.3 cells following MC-LR exposure. Intriguingly, MC-LR also amplified phospho-Erk levels in both in vivo and in vitro settings, and the effects were mitigated by treatment with PD98059, an Erk inhibitor. Taken together, our findings implicate the activation of the Erk/MAPK signaling pathway in MC-LR-induced ferroptosis, shedding valuable light on the neurotoxic mechanisms of MC-LR. These insights could guide future strategies to prevent MC-induced neurodegenerative diseases.

Controls of thermal response of temperate lakes to atmospheric warming

Atmospheric warming heats lakes, but the causes of variation among basins are poorly understood. Here, multi-decadal profiles of water temperatures, trophic state, and local climate from 345 temperate lakes are combined with data on lake geomorphology and watershed characteristics to identify controls of the relative rates of temperature change in water (WT) and air (AT) during summer. We show that differences in local climate (AT, wind speed, humidity, irradiance), land cover (forest, urban, agriculture), geomorphology (elevation, area/depth ratio), and water transparency explain >30% of the difference in rate of lake heating compared to that of the atmosphere. Importantly, the rate of lake heating slows as air warms (P < 0.001). Clear, cold, and deep lakes, especially at high elevation and in undisturbed catchments, are particularly responsive to changes in atmospheric temperature. We suggest that rates of surface water warming may decline relative to the atmosphere in a warmer future, particularly in sites already experiencing terrestrial development or eutrophication.

Changes in the abundance and diversity of bacterial and archaeal communities at different depths in a eutrophic freshwater lake in southwestern Mexico

Bacteria and archaea play a fundamental role in the biogeochemical cycles of organic matter, pollutants, and nutrients to maintain the trophic state of aquatic ecosystems. However, very little is known about the composition patterns of microbial communities in vertical distribution (water column) in freshwater lakes and their relationship with the physicochemical properties of water. "La Encantada" lake in the Lagunas de Montebello National Park (LMNP) is a site of interest due to the anthropogenic impact received and the little information about it. In this study, 3 sites were evaluated; samples were collected using 0-15 m deep water columns and analyzed using Illumina MiSeq sequencing technology based on the 16S rRNA gene. The physical parameters of pH, temperature, dissolved oxygen, electrolytic conductivity, and PO-4 were determined. The results revealed clear differences in the microbial composition of the water throughout the column; the most abundant phyla in bacterial communities were Proteobacteria (23.2%), Cyanobacteria (17.3%), and Bacteroidetes (17.2%), and for archaea were Crenarchaeota (35.9%) and Euryarchaeota (33.2%). PICRUSt metabolic inference analysis revealed that the main functional genes were related to cellular processes and biodegradation of xenobiotics, indicating an increasing trend of contaminants and residual discharges that may act as a precursor to alter microbial communities and stability of the lakes. At depths of 10 and 15 m, the microbial diversity was greater; likewise, the correlation between the physicochemical parameters and the microbial communities at the genus level showed that Chlorobaculum, Desulfomonile, and Candidatus Xiphinematobacter were favored by an increase in dissolved phosphates and by the decrease in pH and temperature. These results highlight that the microbial communities exhibit variation in their composition due to the effect of depth and physicochemical parameters, which could play a role as biological factors in the trophic states of a lake.

Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds

Cyanobacteria blooms in aquaculture ponds harm the harvesting of aquatic animals and threaten human health. Therefore, it is crucial to identify key drivers and develop methods to predict cyanobacteria blooms in aquaculture water management. In this study, we analyzed monitoring data from 331 aquaculture ponds in central China and developed two machine learning models - the least absolute shrinkage and selection operator (LASSO) regression model and the random forest (RF) model - to predict cyanobacterial abundance by identifying the key drivers. Simulation results demonstrated that both machine learning models are feasible for predicting cyanobacterial abundance in aquaculture ponds. The LASSO model (R² = 0.918, MSE = 0.354) outperformed the RF model (R² = 0.798, MSE = 0.875) in predicting cyanobacteria abundance. Farmers with well-equipped aquaculture ponds that have abundant water monitoring data can use the nine environmental variables identified by the LASSO model as an operational solution to accurately predict cyanobacteria abundance. For crude ponds with limited monitoring data, the three environmental variables identified by the RF model provide a convenient solution for useful cyanobacteria prediction. Our findings revealed that chemical oxygen demand (COD) and total organic carbon (TOC) were the two most important predictors in both models, indicating that organic carbon concentration had a close relationship with cyanobacteria growth and should be considered a key metric in water monitoring and pond management of these aquaculture ponds. We suggest that monitoring of organic carbon coupled with phosphorus reduction in feed usage can be an effective management approach for cyanobacteria prevention and to maintain a healthy ecological state in aquaculture ponds.

Nutrients and not temperature are the key drivers for cyanobacterial biomass in the Americas

Cyanobacterial blooms imperil the use of freshwater around the globe and present challenges for water management. Studies have suggested that blooms are trigged by high temperatures and nutrient concentrations. While the roles of nitrogen and phosphorus have long been debated, cyanobacterial dominance in phytoplankton has widely been associated with climate warming. However, studies at large geographical scales, covering diverse climate regions and lake depths, are still needed to clarify the drivers of cyanobacterial success. Here, we analyzed data from 464 lakes covering a 14,000 km north-south gradient in the Americas and three lake depth categories. We show that there were no clear trends in cyanobacterial biomass (as biovolume) along latitude or climate gradients, with the exception of lower biomass in polar climates. Phosphorus was the primary resource explaining cyanobacterial biomass in the Americas, while nitrogen was also significant but particularly relevant in very shallow lakes (< 3 m depth). Despite the assessed climatic gradient water temperature was only weakly related to cyanobacterial biomass, suggesting it is overemphasized in current discussions. Depth was critical for predicting cyanobacterial biomass, and shallow lakes proved more vulnerable to eutrophication. Among other variables analyzed, only pH was significantly related to cyanobacteria biomass, likely due to a biologically mediated positive feedback under high nutrient conditions. Solutions toward managing harmful cyanobacteria should thus consider lake morphometric characteristics and emphasize nutrient control, independently of temperature gradients, since local factors are more critical - and more amenable to controls - than global external forces.

Diversity assessment of photosynthesizers: comparative analysis of pre-cultivated and natural microbiome of sediments from Cerrado biome in Maranhão, Brazil

Photosynthetic microorganisms are important components of most ecosystems and have important roles regarding biogeochemical cycles and the basis of the trophic chain. However, they sometimes are present in low abundance compared to other heterotrophic organisms. The Chapada das Mesas National Park (PNCM) is a Conservation Unit in Brazilian Cerrado biome, which is considered a hotspot for biodiversity conservation and possesses important rivers, waterfalls, and springs with economical and touristic importance. The aim of this study was to perform a comparative analysis of enriched and total microbiome of sediments to understand the impact of pre-cultivation in discovery of underrepresented groups like photosynthesizers. All sediment samples were cultivated in BG-11 medium under illumination to enrich for photosynthetic microorganisms and both the raw samples and the enriched ones were submitted to DNA extraction and sequencing of the V3-V4 hypervariable region of the 16S rRNA gene on the Ion Torrent platform. The reads were analyzed using QIIME2 software and the Phyloseq package. The enrichment allowed detection and identification of many genera of cyanobacteria in the Chapada das Mesas National Park (PNCM), which would probably not be possible without the combination of approaches. A total of 58 groups of photosynthetic microorganisms were classified in the samples from the enrichments and their relative abundance based on amplified 16S rRNA sequences were estimated, highlighting the genus Synechocystis which represented 10.10% of the abundance of the phylum Cyanobacteria and the genus Dunaliella, which represented 45.66% of the abundance of algae as the most abundant groups at the PNCM. In the enrichments, microorganisms from the phyla Proteobacteria (45.2%), Bacteroidetes (18%), and Planctomycetes (3.3%) were also identified, since there are ecological associations between the photosynthetic community and other groups of heterotrophic microorganisms. As for the functional analysis, metabolic functions associated with methanotrophy and methylotrophy, hydrocarbon degradation, phototrophy, and nitrogen fixation were predicted. The results highlight a great diversity of photosynthetic microorganisms in Cerrado and the importance of using a combination of approaches when analyzing target groups which are usually underrepresented such as cyanobacteria and microalgae.

Assessment of physico-chemical parameters of surface waters of a tropical brackish water lake in South Asia

Brackish lake systems and estuaries are unique aquatic systems that support diversified life forms and strongly influence a region's economy. Major chemical water quality parameters of India's second-largest brackish water lake, Pulicat were assessed. Physico-chemical parameters like pH, temperature, suspended solid concentrates, total dissolved solids, salinity, nitrogenous nutrients, phosphate, silicate, and chlorophyll a were analysed. The results obtained for different parameters were compared and interpreted with statistical software SPSS version 20 and images were plotted using the Arc GIS spatial analyst tool. During the summer months, the nitrogen to phosphorus ratio ranges from a minimum of 1.96 to a maximum of 16.64 (9.55 ± 4.01) while it ranges from a minimum of 7.98 to a maximum of 15.52 (12.47 ± 2) during the pre-monsoon. In the monsoon season, the nitrogen to phosphorus ratio of surface water suggests a range from a minimum of 8.64 to a maximum of 17.58 (13.87 ± 2.14). During the post-monsoon season, the nitrogen to phosphorus ratio ranges from 4.98 to 17.34 (11.77 ± 3.68). The average nitrogen to phosphorus ratios were 9.6, 12.5, 13.9 and 11.8 in summer, pre-monsoon, monsoon, and post-monsoon respectively. The nitrogen to phosphorus ratio was lower than the Redfield ratio for all the seasons. The average concentration of chlorophyll a was 14.9, 13.4, 12.8 and 11.8 in summer, pre-monsoon, monsoon, and post-monsoon respectively. As per the Pearson Correlation Coefficient, there was no significant correlation among nitrogen, phosphorus, and chlorophyll a. This suggests the influence of suspended solid concentrates, and nitrogen and phosphorus flux in the sediment-water interface might be interfering with the nutrient cycles and primary productivity.

Ecological Dynamics and Co-occurrences Among Prokaryotes and Microeukaryotes in a Diatom Bloom Process in Xiangshan Bay, China

Diatom blooms can significantly affect the succession of microbial communities, yet little is known about the assembly processes and interactions of microbial communities during autumn bloom events. In this study, we investigated the ecological effects of an autumn diatom bloom on prokaryotic communities (PCCs) and microeukaryotic communities (MECs), focusing on their assembly processes and interactions. The PCCs were largely dominated by Alphaproteobacteria, Gammaproteobacteria, Cyanobacteria, and Flavobacteria, while the MECs primarily included Diatomea, Dinoflagellata, and Chlorophyta. The succession of both PCCs and MECs was mainly driven by this diatom bloom and environmental factors, such as nitrate and silicate. Null modeling revealed that homogeneous selection had a more pronounced impact on the structure of PCCs compared with that of MECs. In particular, drift and dispersal limitation cannot be neglected in the assembly processes of MECs. Co-occurrence network analyses showed that Litorimicrobium, Cercozoa, Marine Group I (MGI), Cryptomonadales, Myrionecta, and Micromonas may affect the bloom process. In summary, these results elucidated the complex, robust interactions and obviously distinct assembly mechanisms of PCCs and MECs during a diatom bloom and extend our current comprehension of the ecological mechanisms and microbial interactions involved in an autumn diatom bloom process.

Predicting the effects of multiple global change drivers on microbial communities remains challenging

Microbial communities in many ecosystems are facing a broad range of global change drivers, such as nutrient enrichment, chemical pollution, and temperature change. These drivers can cause changes in the abundance of taxa, the composition of communities, and the properties of ecosystems. While the influence of single drivers is already described in numerous studies, the effect and predictability of multiple drivers changing simultaneously is still poorly understood. In this study, we used 240 highly replicable oxic/anoxic aquatic lab microcosms and four drivers (fertilizer, glyphosate, metal pollution, antibiotics) in all possible combinations at three different temperatures (20, 24, and 28°C) to shed light into consequences of multiple drivers on different levels of organization, ranging from species abundance to community and ecosystem parameters. We found (i) that at all levels of ecological organization, combinations of drivers can change the biological consequence and direction of effect compared to single drivers, (ii) that effects of combinations are further modified by temperature, (iii) that a larger number of drivers occurring simultaneously is often quite closely related to their effect size, and (iv) that there is little evidence that any of these effects are associated with the level of ecological organization of the state variable. These findings suggest that, at least in this experimental ecosystem approximating a stratified aquatic ecosystem, there may be relatively little scope for predicting the effects of combinations of drivers from the effects of individual drivers, or by accounting for the level of ecological organization in question, though there may be some scope for prediction based on the number of drivers that are occurring simultaneous. A priority, though also a considerable challenge, is to extend such research to consider continuous variation in the magnitude of multiple drivers acting together.

Water depth and land-use intensity indirectly determine phytoplankton functional diversity and further regulate resource use efficiency at a multi-lake scale

Biodiversity-ecosystem functioning relationships under multiple pressures have recently been the subject of broad studies. For the key primary producer in aquatic ecosystems, phytoplankton, several studies have focused on trait-based functional diversity (FD) and the related functioning (e.g., resource use efficiency, RUE), and their linkages. However, investigations of the effects of environmental factors at different levels (e.g., land use, lake morphometry, climate and nutrients) on FD and RUE are sparse. We developed a data-driven-model framework to simultaneously elucidate the effects of multiple drivers on FD (functional diversity based on dendrograms, FDc and functional richness, FRic) and RUE (of nitrogen and phosphorus) of phytoplankton based on data from 68 Yunnan-Guizhou Plateau lakes, Southwest China. We found that the concentration of total phosphorus, which is mainly affected by land-use intensity and influenced by water depth, was the primary (positive) driver of changes in both FDc and FRic, while RUE was mainly explained by phytoplankton FD (i.e., FRic). These results indicate that water depth and land-use intensity influence indirectly phytoplankton FD and further regulate RUE. Moreover, nonlinear correlations of RUE with FRic were found, which may be caused by interspecific competition and niche differentiation of the phytoplankton community related to nutrient levels. Our finding may help managers to set trade-off targets between FD and RUE in lake ecosystems except for extremely polluted ones, in which the thresholds derived from the Bayesian network, of total phosphorus, total nitrogen and land-use intensity were approximately 0.04 mg/L, 0.50 mg/L and 244 (unitless), respectively. The probability of meeting the RUE objectives was lower in shallow lakes than in deep lakes, but for FRic the opposite was observed.

Anthropogenic eutrophication of shallow lakes: Is it occasional?

Understanding and managing the susceptibility of lakes to anthropogenic eutrophication has been a primary goal of limnological research for decades. To achieve United Nations' Sustainable Development Goals, scientists have attempted to understand why shallow lakes appear to be prone to eutrophication and resistant to restoration. A rich data base of 1151 lakes (each ≥ 0.5 km²) located within the Europe and the United States of America offers a rare opportunity to explore potential answers. Analysis of sites showed that lake depth integrated socio-ecological systems and reflected potential susceptibility to anthropogenic stressors, as well as lake productivity. In this study, lakes distributed in agricultural plain and densely populated lowland areas were generally shallow and subjected to intense human activities with high external nutrient inputs. In contrast, deep lakes frequently occurred in upland regions, dominated by natural landscapes with little anthropogenic nutrient input. Lake depth appeared to not only reflect external nutrient load to the lake, but also acted as an amplifier that increased shallow lake susceptibility to anthropogenic disturbance. Our findings suggest that shallow lakes are more susceptible to human forcing and their eutrophication may be not an occasional occurrence, and that societal expectations, policy goals, and management plans should reflect this observation.

A novel approach for accurate quantification of lake residence time - Lake Kinneret as a case study

Water residence time, which is affected by increasing water demands and climate change, plays a crucial role in lakes and reservoirs since it influences many natural physical and ecological processes that eventually impact the water quality of the waterbody. Thus, accurate quantification of the water residence time and its distribution is an important tool in lake management. In this study we present a novel approach for assessing the residence time in lakes and reservoirs. The approach is based on the Leslie matrix model that was originally developed for the analysis of age-structured biological population dynamics. In this approach the water in the lake is divided into different age classes each representing the time since the "parcel" of water entered the lake and provides an overall picture of the water age structure. The traditional approach for calculating residence times, which relies only on the lake volume and annual inflow or outflow volumes thereby disregarding any previous information, is very sensitive to large interannual variation. While the proposed approach produces the fraction and volume distribution curves of all age classes within the lake for each simulated timestep. Thus, in addition to mean residence time, the fraction of young water (FYW), quantifying the "young" fraction of water in the lake can be analyzed. The same is true for any other age class of water. The approach was applied to Lake Kinneret (Sea of Galilee) historical data collected over 32 years (1987-2018) and for prediction of long-term time series based on several future scenarios (inflows and outflows). It offers a more accurate quantification of the mean residence time of water in a lake and can easily be adapted to other waterbodies. Comparison of simulation results may serve as basis for determining the lake's management policy, by controlling the inflows and outflows, that will affect both the mean residence time and the fraction of "young/old" age classes of water.

Multi-decadal changes in phytoplankton biomass in northern temperate lakes as seen through the prism of landscape properties

Ecologists collectively predict that climate change will enhance phytoplankton biomass in northern lakes. Yet there are unique variations in the structures and regulating functions of lakes to make this prediction challengeable and, perhaps, inaccurate. We used archived Landsat TM/ETM+ satellite products to estimate epilimnetic chlorophyll-a (Chl-a) concentration as a proxy for phytoplankton biomass in 281 northern temperate lakes over 28 years. We explored the influence of climate (air temperature, precipitation) and landscape proxies for nutrient sources (proportion of wetlands in a contributing catchment, size of the littoral zone, potential for wind-driven sediment resuspension as estimated by the dynamic ratio) or nutrient sinks (lake volume) in a random forest model to explain heterogeneity in peak Chl-a. Lakes with higher Chl-a (median Chl-a = 2.4 μg L^-1 , n = 40) had smaller volumes (<44 × 10⁴  m³ ) and were more sensitive to increases in temperature. In contrast, lakes with lower Chl-a (median Chl-a = 0.6 μg L^-1 , n = 241) had larger volumes (≥44 × 10⁴  m³ ), contributing catchments with smaller proportions of wetlands (<4.5% of catchment area, n = 70), smaller littoral zones (<16.4 ha, n = 137), minimal wind-driven sediment resuspension (as defined by the dynamic ratio; <0.45, n = 232), and were more sensitive to increases in precipitation. Lakes with larger volumes were generally less responsive to climate factors; however, larger volume lakes with a significant proportion of wetlands and larger littoral zones behaved similarly to lakes with smaller volumes. Our finding that lakes with different landscape properties respond differently to climate factors may help predict the susceptibility of lakes to eutrophication under changing climatic conditions.

Land Use Change to Reduce Freshwater Nitrogen and Phosphorus will Be Effective Even with Projected Climate Change

Recent studies have demonstrated that projected climate change will likely enhance nitrogen (N) and phosphorus (P) loss from farms and farmland, with the potential to worsen freshwater eutrophication. Here, we investigate the relative importance of the climate and land use drivers of nutrient loss in nine study catchments in Europe and a neighboring country (Turkey), ranging in area from 50 to 12,000 km2. The aim was to quantify whether planned large-scale, land use change aimed at N and P loss reduction would be effective given projected climate change. To this end, catchment-scale biophysical models were applied within a common framework to quantify the integrated effects of projected changes in climate, land use (including wastewater inputs), N deposition, and water use on river and lake water quantity and quality for the mid-21st century. The proposed land use changes were derived from catchment stakeholder workshops, and the assessment quantified changes in mean annual N and P concentrations and loads. At most of the sites, the projected effects of climate change alone on nutrient concentrations and loads were small, whilst land use changes had a larger effect and were of sufficient magnitude that, overall, a move to more environmentally focused farming achieved a reduction in N and P concentrations and loads despite projected climate change. However, at Beyşehir lake in Turkey, increased temperatures and lower precipitation reduced water flows considerably, making climate change, rather than more intensive nutrient usage, the greatest threat to the freshwater ecosystem. Individual site responses did however vary and were dependent on the balance of diffuse and point source inputs. Simulated lake chlorophyll-a changes were not generally proportional to changes in nutrient loading. Further work is required to accurately simulate the flow and water quality extremes and determine how reductions in freshwater N and P translate into an aquatic ecosystem response.

Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections

Land use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long-term (1971-2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971-2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid-21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one-third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long-term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.

Chronic MC-LR exposure promoted Aβ and p-tau accumulation via regulating Akt/GSK-3β signal pathway

It has been reported that microcystin-leucine-arginine (MC-LR) can enter into the brain and demonstrate neurotoxicity resulting in learning and memory deficits. While, there is still a lack of clear understanding of the related molecular mechanisms. In this study, we observed β-amyloid (Aβ) accumulation and tau hyperphosphorylation (p-tau) at sites of Ser396 and Thr205 in mouse hippocampus and cortex, Alzheimer's disease (AD) like changes, after chronic exposure to MC-LR at different concentrations (1, 7.5, 15 and 30 μg/L) for 180 days. The hallmarks of AD are characterized by senile plaques and neurofibrillary tangles (NFT), with associated loss of neurons, resulting in cognitive impairment and dementia. Similarly, the production of Aβ and tau hyperphosphorylation was also detected in HT-22 cells treated with MC-LR. In addition, MC-LR promoted increased expressions of BACE1 and PS1, but reduced mRNA expressions of ADAM family members both in vivo and in vitro, promoting the Aβ production. Moreover, we identified Akt/GSK-3β signal pathway mediated the Aβ and p-tau accumulation, bringing about Alzheimer's disease-like changes. Furthermore, microglial cells were activated in those mice exposed to MC-LR. Inflammatory cytokines were also found being activated to release in vitro. In conclusion, this study could provide a clue for MC-LR-induced neurotoxicity, which gave insights into the environmental risks of Alzheimer's disease.

Species-Level Spatio-Temporal Dynamics of Cyanobacteria in a Hard-Water Temperate Lake in the Southern Baltics

Cyanobacteria are important primary producers in temperate freshwater ecosystems. However, studies on the seasonal and spatial distribution of cyanobacteria in deep lakes based on high-throughput DNA sequencing are still rare. In this study, we combined monthly water sampling and monitoring in 2019, amplicon sequence variants analysis (ASVs; a proxy for different species) and quantitative PCR targeting overall cyanobacteria abundance to describe the seasonal and spatial dynamics of cyanobacteria in the deep hard-water oligo-mesotrophic Lake Tiefer See, NE Germany. We observed significant seasonal variation in the cyanobacterial community composition (p < 0.05) in the epi- and metalimnion layers, but not in the hypolimnion. In winter-when the water column is mixed-picocyanobacteria (Synechococcus and Cyanobium) were dominant. With the onset of stratification in late spring, we observed potential niche specialization and coexistence among the cyanobacteria taxa driven mainly by light and nutrient dynamics. Specifically, ASVs assigned to picocyanobacteria and the genus Planktothrix were the main contributors to the formation of deep chlorophyll maxima along a light gradient. While Synechococcus and different Cyanobium ASVs were abundant in the epilimnion up to the base of the euphotic zone from spring to fall, Planktothrix mainly occurred in the metalimnetic layer below the euphotic zone where also overall cyanobacteria abundance was highest in summer. Our data revealed two potentially psychrotolerant (cold-adapted) Cyanobium species that appear to cope well under conditions of lower hypolimnetic water temperature and light as well as increasing sediment-released phosphate in the deeper waters in summer. The potential cold-adapted Cyanobium species were also dominant throughout the water column in fall and winter. Furthermore, Snowella and Microcystis-related ASVs were abundant in the water column during the onset of fall turnover. Altogether, these findings suggest previously unascertained and considerable spatiotemporal changes in the community of cyanobacteria on the species level especially within the genus Cyanobium in deep hard-water temperate lakes.

Modeling Anthropogenic and Environmental Influences on Freshwater Harmful Algal Bloom Development Detected by MERIS Over the Central United States

Human and ecological health have been threatened by the increase of cyanobacteria harmful algal blooms (cyanoHABs) in freshwater systems. Successful mitigation of this risk requires understanding the factors driving cyanoHABs at a broad scale. To inform management priorities and decisions, we employed random forest modeling to identify major cyanoHAB drivers in 369 freshwater lakes distributed across 15 upper Midwest states during the 2011 bloom season (July-October). We used Cyanobacteria Index (CI_cyano)-A remotely sensed product derived from the MEdium Resolution Imaging Spectrometer (MERIS) aboard the European Space Agency's Envisat satellite-as the response variable to obtain variable importance metrics for 75 landscape and lake physiographic predictor variables. Lakes were stratified into high and low elevation categories to further focus CI_cyano variable importance identification by anthropogenic and natural influences. "High elevation" watershed land cover (LC) was primarily forest or natural vegetation, compared with "low elevation" watersheds LC dominated by anthropogenic landscapes (e.g., agriculture and municipalities). We used the top ranked 25 Random Forest variables to create a classification and regression tree (CART) for both low and high elevation lake designations to identify variable thresholds for possible management mitigation. Mean CI_cyano was 3 times larger for "low elevation" lakes than for "high elevation" lakes, with both mean values exceeding the "High" World Health Organization recreational guidance/action level threshold for cyanobacteria (100,000 cells/mL). Agrarian-related variables were prominent across all 369 lakes and low elevation lakes. High elevation lakes showed more influence of lakeside LC than for the low elevation lakes.

Cyanobacteria and Cyanotoxins in a Changing Environment: Concepts, Controversies, Challenges

Concern is widely being published that the occurrence of toxic cyanobacteria is increasing in consequence of climate change and eutrophication, substantially threatening human health. Here, we review evidence and pertinent publications to explore in which types of waterbodies climate change is likely to exacerbate cyanobacterial blooms; whether controlling blooms and toxin concentrations requires a balanced approach of reducing not only the concentrations of phosphorus (P) but also those of nitrogen (N); how trophic and climatic changes affect health risks caused by toxic cyanobacteria. We propose the following for further discussion: (i) Climate change is likely to promote blooms in some waterbodies—not in those with low concentrations of P or N stringently limiting biomass, and more so in shallow than in stratified waterbodies. Particularly in the latter, it can work both ways—rendering conditions for cyanobacterial proliferation more favourable or less favourable. (ii) While N emissions to the environment need to be reduced for a number of reasons, controlling blooms can definitely be successful by reducing only P, provided concentrations of P can be brought down to levels sufficiently low to stringently limit biomass. Not the N:P ratio, but the absolute concentration of the limiting nutrient determines the maximum possible biomass of phytoplankton and thus of cyanobacteria. The absolute concentrations of N or P show which of the two nutrients is currently limiting biomass. N can be the nutrient of choice to reduce if achieving sufficiently low concentrations has chances of success. (iii) Where trophic and climate change cause longer, stronger and more frequent blooms, they increase risks of exposure, and health risks depend on the amount by which concentrations exceed those of current WHO cyanotoxin guideline values for the respective exposure situation. Where trophic change reduces phytoplankton biomass in the epilimnion, thus increasing transparency, cyanobacterial species composition may shift to those that reside on benthic surfaces or in the metalimnion, changing risks of exposure. We conclude that studying how environmental changes affect the genotype composition of cyanobacterial populations is a relatively new and exciting research field, holding promises for understanding the biological function of the wide range of metabolites found in cyanobacteria, of which only a small fraction is toxic to humans. Overall, management needs case-by-case assessments focusing on the impacts of environmental change on the respective waterbody, rather than generalisations.

Environmental factors associated with cyanobacterial assemblages in a mesotrophic subtropical plateau lake: A focus on bloom toxicity

Harmful algal blooms caused by cyanobacteria have been increasing in frequency worldwide. However, the main environmental drivers of this change are often difficult to identify because of the effects of the interaction between eutrophication and climate change. Recently, filamentous N₂-fixing cyanobacteria and non-diazotrophic Microcystis have been observed to be co-existing and undergoing succession in some eutrophic lakes. However, the succession patterns of dominant cyanobacteria and the factors driving this in mesotrophic lakes are not well understood. We hypothesized that the changes in cyanobacterial assemblages in mesotrophic lakes could result in a relatively high risks of toxic blooms, and that these changes are associated with the global climatic changes. We tested these hypotheses using data from the subtropical mesotrophic Lake Erhai. We found that the high spatiotemporal variability in the cyanobacterial community, and the increase in biomass were driven primarily by the growth of bloom-forming cyanobacterial taxa. Species-specific biomasses were related to a different environmental stressor; increases in dissolved organic carbon (DOC) concentrations were statistically associated with an increase of Microcystis biomass, whereas increases in surface water temperature favored higher biomass of Pseudanabaena at low transparency and high concentration of phosphorus. In addition, low nitrogen- to- phosphorus ratios were identified as potential determinants of the abundance of N₂-fixing Dolichospermum. Furthermore, changes in the concentration of DOC, total nitrogen, pH and water transparency levels were found to affect the composition of Microcystis morphotypes and genotypes mostly. This study highlights that the toxic to non-toxic Microcystis ratio might increase with the water darkening and browning (which occurs in many subtropical plateau lakes). Lake management strategies, therefore, need to consider the toxicity of cyanobacterial assemblages in mesotrophic lakes over the intensity of the cyanobacterial blooms.

Histological and chemical damage induced by microcystin-LR and microcystin-RR on land snail Helix aspersa tissues after acute exposure

Microcystins (MCs) are the most common cyanotoxins with more than 200 variants. Among these cyanotoxins, microcystin-LR (MC-LR) and microcystin-RR (MC-RR) are the most studied congeners due to their high toxicity and frequent occurrence in surface waters. MC-LR has been detected in more than 75% of natural cyanobacteria bloom, along with other toxic and less toxic congeners. Accumulation of several microcystins variants (MC-LR and MC-RR) has been confirmed in aquatic snails exposed naturally or in the laboratory to toxic blooms. Thus, this paper aims to compare the biochemical and histological impact of both toxic variants (microcystin-LR and microcystin-RR) and their mixed form on a bioindicator, the land snail Helix aspersa. During experiments, snails were gavaged with a single acute dose (0.5 μg/g) of purified MC-LR, MC-RR, or mixed MC-LR + MC-RR (0.25 + 0.25 μg/g). After 96 h of exposure, effects on the hepatopancreas, kidney, intestine and lungs were assessed by histological observations and analysis of oxidative stress biomarkers. The results show that a small dose of MCs variants can increase the non-enzymatic antioxidant glutathione (GSH), inhibit glutathione-s-transferase (GST) level and trigger a defense system by activating glutathione peroxidase (GPx), catalase (CAT) and superoxide dismutase (SOD). Microcystin-RR causes serious anomalies in the hepatopancreas and kidney than Microcystin-LR. The organ most affected is the kidney. The damage caused by MC-LR + MC-RR is greater than that caused by single variants.

Increasingly severe cyanobacterial blooms and deep water hypoxia coincide with warming water temperatures in reservoirs

Cyanobacterial blooms are expected to intensify and become more widespread with climate change and sustained nutrient pollution, subsequently increasing threats to lentic ecosystems, water quality, and human health. However, little is known about their rates of change because long-term monitoring data are rare, except for some well-studied individual lakes, which typically are large and broadly dispersed geographically. Using monitoring data spanning 1987-2018 for 20 temperate reservoirs located in the USA, we found that cyanobacteria cell densities mostly posed low-to-moderate human health risks until 2003-2005, after which cell densities rapidly increased. Increases were greatest in reservoirs with extensive agriculture in their watersheds, but even those with mostly forested watersheds experienced increases. Since 2009, cell densities posing high human health risks have become frequent with 75% of yearly observations exceeding 100,000 cells ml^-1 , including 53% of observations from reservoirs with mostly forested watersheds. These increases coincided with progressively earlier and longer summer warming of surface waters, evidence of earlier onset of stratification, lengthening durations of deep-water hypoxia, and warming deep waters in non-stratifying reservoirs. Among years, higher cell densities in stratifying reservoirs were associated with greater summer precipitation, warmer June surface water temperatures, and higher total Kjeldahl nitrogen concentrations. These trends are evidence that expected increases in cyanobacterial blooms already are occurring as changing climate conditions in some regions increasingly favor their proliferation. Consequently, their negative effects on ecosystems, human health, and socioeconomic wellbeing could increase and expand if warming trends and nutrient pollution continue.

Expanding ecological assessment by integrating microorganisms into routine freshwater biomonitoring

Bioindication has become an indispensable part of water quality monitoring in most countries of the world, with the presence and abundance of bioindicator taxa, mostly multicellular eukaryotes, used for biotic indices. In contrast, microbes (bacteria, archaea and protists) are seldom used as bioindicators in routine assessments, although they have been recognized for their importance in environmental processes. Recently, the use of molecular methods has revealed unexpected diversity within known functional groups and novel metabolic pathways that are particularly important in energy and nutrient cycling. In various habitats, microbial communities respond to eutrophication, metals, and natural or anthropogenic organic pollutants through changes in diversity and function. In this review, we evaluated the common trends in these changes, documenting that they have value as bioindicators and can be used not only for monitoring but also for improving our understanding of the major processes in lotic and lentic environments. Current knowledge provides a solid foundation for exploiting microbial taxa, community structures and diversity, as well as functional genes, in novel monitoring programs. These microbial community measures can also be combined into biotic indices, improving the resolution of individual bioindicators. Here, we assess particular molecular approaches complemented by advanced bioinformatic analysis, as these are the most promising with respect to detailed bioindication value. We conclude that microbial community dynamics are a missing link important for our understanding of rapid changes in the structure and function of aquatic ecosystems, and should be addressed in the future environmental monitoring of freshwater ecosystems.

Potential ecological implication of Cladophora oligoclora decomposition: Characteristics of nutrient migration, transformation, and response of bacterial community structure

During decay, the sediment microenvironment and water quality are severely affected by excessive proliferation of harmful algae such as filamentous green algae (FGA). The frequency of this FGA is increased through global warming and water eutrophication. In the present study, the degradation processes of a common advantage FGA Cladophora oligoclora and its effect on nitrogen and phosphorus nutrient structure and bacterial community composition at the sediment-water interface were investigated by stable isotope labelling and high-throughput sequencing. The results showed that the decomposition process of C. oligoclona was fast, stable, and difficult to degrade. The changes in sediment δ¹⁵N values reached 66.68 ‰ on day 40, which indicated that some of the nitrogen had migrated to the sediment from C. oligoclona litter. TN and NH₄⁺-N in the overlying water rapidly increased between days 0-10, NH₄⁺-N rose to 78.21% of TN on day 40, resulting in severe pollution of ammonia in the overlying water. The nitrogen forms and contents in the sediment are mainly derived from the increasing ammonia nitrogen release. The TP and IP in the overlying water increased to the highest concentrations of 6.68±0.64, 6.59±0.79 mg·L^-1 during the decomposition process, respectively, resulting in the migration of phosphate to the sediments with increasing phosphorus content. The abundance of the main dominant bacterial communities, such as Acinetobacter (0.08%-62.48%) and Pseudomonas (0.13%-20.36%) in sediments and overlying water has changed significantly. The correlation analysis results suggested that the phosphorus was mainly related to the bacterial community in the overlying water, while the various forms of nitrogen demonstrated a high relevance with the bacterial community in the sediment. Our research results will be valuable in evaluating the potential ecological risk of FGA decomposition and provide scientific support for shallow lake management and submerged vegetation restoration.

Potential Impacts on Treated Water Quality of Recycling Dewatered Sludge Supernatant during Harmful Cyanobacterial Blooms

Cyanobacterial blooms and the associated release of cyanotoxins pose problems for many conventional water treatment plants due to their limited removal by typical unit operations. In this study, a conventional water treatment process consisting of coagulation, flocculation, sedimentation, filtration, and sludge dewatering was assessed in lab-scale experiments to measure the removal of microcystin-LR and Microcystis aeruginosa cells using liquid chromatography with mass spectrometer (LC-MS) and a hemacytometer, respectively. The overall goal was to determine the effect of recycling cyanotoxin-laden dewatered sludge supernatant on treated water quality. The lab-scale experimental system was able to maintain the effluent water quality below relevant the United States Environmental Protection Agency (US EPA) and World Health Organisation (WHO) standards for every parameter analyzed at influent concentrations of M. aeruginosa above 10⁶ cells/mL. However, substantial increases of 0.171 NTU (Nephelometric Turbidity Unit), 7 × 10⁴ cells/L, and 0.26 µg/L in turbidity, cyanobacteria cell counts, and microcystin-LR concentration were observed at the time of dewatered supernatant injection. Microcystin-LR concentrations of 1.55 µg/L and 0.25 µg/L were still observed in the dewatering process over 24 and 48 h, respectively, after the initial addition of M.aeruginosa cells, suggesting the possibility that a single cyanobacterial bloom may affect the filtered water quality long after the bloom has dissipated when sludge supernatant recycling is practiced.

Distribution of Toxic Cyanobacteria in Volcanic Lakes of the Azores Islands

Eutrophication and global climate change gather advantageous conditions for cyanobacteria proliferation leading to bloom formation and cyanotoxin production. In the Azores, eutrophication is a major concern, mainly in lakes where fertilizers and organic matter discharges have increased nutrient concentration. In this study, we focused on understanding the influence of environmental factors and lake characteristics on (i) cyanobacteria diversity and biomass and (ii) the presence of toxic strains and microcystin, saxitoxin, anatoxin-a, and cylindrospermopsin cyanotoxin-producing genes. Fifteen lakes from the Azores Archipelago were sampled seasonally, environmental variables were recorded in situ, cyanobacteria were analyzed with microscopic techniques, and cyanotoxin-producing genes were targeted through conventional PCR. Statistical analysis (DistLM) showed that lake typology-associated variables (lake’s depth, area, and altitude) were the most explanatory variables of cyanobacteria biomass and cyanotoxin-producing genes presence, although trophic variables (chlorophyll a and total phosphorus) influence species distribution in each lake type. Our main results revealed higher cyanobacteria biomass/diversity, and higher toxicity risk in lakes located at lower altitudes, associated with deep anthropogenic pressures and eutrophication scenarios. These results emphasize the need for cyanobacteria blooms control measures, mainly by decreasing anthropogenic pressures surrounding these lakes, thus decreasing eutrophication. We also highlight the potential for microcystin, saxitoxin, and anatoxin-a production in these lakes, hence the necessity to implement continuous mitigation protocols to avoid environmental and public health toxicity events.

Using Bayesian hierarchical modelling to capture cyanobacteria dynamics in Northern European lakes

Cyanobacteria blooms in lakes and reservoirs currently threaten water security and affect the ecosystem services provided by these freshwater ecosystems, such as drinking water and recreational use. Climate change is expected to further exacerbate the situation in the future because of higher temperatures, extended droughts and nutrient enrichment, due to urbanisation and intensified agriculture. Nutrients are considered critical for the deterioration of water quality in lakes and reservoirs and responsible for the widespread increase in cyanobacterial blooms. We model the response of cyanobacteria abundance to variations in lake Total Phosphorus (TP) and Total Nitrogen (TN) concentrations, using a data set from 822 Northern European lakes. We divide lakes in ten groups based on their physico-chemical characteristics, following a modified lake typology defined for the Water Framework Directive (WFD). This classification is used in a Bayesian hierarchical linear model which employs a probabilistic approach, transforming uncertainty into probability thresholds. The hierarchical model is used to calculate probabilities of cyanobacterial concentrations exceeding risk levels for human health associated with the use of lakes for recreational activities, as defined by the World Health Organization (WHO). Different TN and TP concentration combinations result in variable probabilities to exceed pre-set thresholds. Our objective is to support lake managers in estimating acceptable nutrient concentrations and allow them to identify actions that would achieve compliance of cyanobacterial abundance risk levels with a given confidence level.

Organic nitrogen steadily increasing in Norwegian rivers draining to the Skagerrak coast

Declining atmospheric nitrogen (N) deposition, through reduction in the direct input of inorganic N, may result in less inorganic N being leached from soils to freshwaters (dissolved inorganic N = DIN). Declining sulphur deposition, through reducing the ionic strength in soil water, increases the solubility and mobility of organic soil compounds and may result in increased leaching of organically bound N to freshwaters (total organic N = TON). It is unknown to which extent these two independents and opposing trends, i.e. DIN decline versus TON increase, may affect the nutrient balance (load, stoichiometry) of river water draining into coastal zones. By combining long-term atmospheric and riverine monitoring data of the five major Norwegian rivers draining to the Skagerrak coast, we show that over the past 27 years (1990-2017) river water nutrient composition, and specifically N stoichiometry has been steadily shifting from inorganic to organic fractions, with correlations to changes in human pressures (air pollution), but especially climate (precipitation, temperature, discharge). This shift in nutrient quality may have large consequences on the nutrient cycling in both freshwater and coastal ecosystems and illustrates the complex interactions of multiple stressors (here: N deposition, S deposition, and climate change) on aquatic ecosystems.

A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

Cyanobacteria harmful blooms (CyanoHABs) in lakes and reservoirs represent a major risk for water authorities globally due to their toxicity and economic impacts. Anticipating bloom occurrence and understanding the main drivers of CyanoHABs are needed to optimize water resources management. An extensive review of the application of CyanoHABs forecasting and predictive models was performed, and a summary of the current state of knowledge, limitations and research opportunities on this topic is provided through analysis of case studies. Two modelling approaches were used to achieve CyanoHABs anticipation; process-based (PB) and data-driven (DD) models. The objective of the model was a determining factor for the choice of modelling approach. PB models were more frequently used to predict future scenarios whereas DD models were employed for short-term forecasts. Each modelling approach presented multiple variations that may be applied for more specific, targeted purposes. Most models reviewed were site-specific. The monitoring methodologies, including data frequency, uncertainty and precision, were identified as a major limitation to improve model performance. A lack of standardization of both model output and performance metrics was observed. CyanoHAB modelling is an interdisciplinary topic and communication between disciplines should be improved to facilitate model comparisons. These shortcomings can hinder the adoption of modelling tools by practitioners. We suggest that water managers should focus on generalising models for lakes with similar characteristics and where possible use high frequency monitoring for model development and validation.

Global changes may be promoting a rise in select cyanobacteria in nutrient-poor northern lakes

The interacting effects of global changes-including increased temperature, altered precipitation, reduced acidification and increased dissolved organic matter loads to lakes-are anticipated to create favourable environmental conditions for cyanobacteria in northern lakes. However, responses of cyanobacteria to these global changes are complex, if not contradictory. We hypothesized that absolute and relative biovolumes of cyanobacteria (both total and specific genera) are increasing in Swedish nutrient-poor lakes and that these increases are associated with global changes. We tested these hypotheses using data from 28 nutrient-poor Swedish lakes over 16 years (1998-2013). Increases in cyanobacteria relative biovolume were identified in 21% of the study sites, primarily in the southeastern region of Sweden, and were composed mostly of increases from three specific genera: Merismopedia, Chroococcus and Dolichospermum. Taxon-specific changes were related to different environmental stressors; that is, increased surface water temperature favoured higher Merismopedia relative biovolume in low pH lakes with high nitrogen to phosphorus ratios, whereas acidification recovery was statistically related to increased relative biovolumes of Chroococcus and Dolichospermum. In addition, enhanced dissolved organic matter loads were identified as potential determinants of Chroococcus suppression and Dolichospermum promotion. Our findings highlight that specific genera of cyanobacteria benefit from different environmental changes. Our ability to predict the risk of cyanobacteria prevalence requires consideration of the environmental condition of a lake and the sensitivities of the cyanobacteria genera within the lake. Regional patterns may emerge due to spatial autocorrelations within and among lake history, rates and direction of environmental change and the niche space occupied by specific cyanobacteria.

Predicting cyanobacteria occurrence using climatological and environmental controls

The occurrence of algal bloom results in deterioration of water quality, undesirable sights, tastes and odors, and the possibility of infections to humans and fatalities to livestock, wildlife and pets. Earlier studies have identified a range of factors including water temperature, flow, and nutrient concentrations that could affect cyanobacterial proliferation. Lack of enough data, independence in data across multiple sampling time steps, as well as the presence of more than one causative factors, each with different levels of influence on the response, has resulted in limited progress in the development of generalized prediction frameworks for cyanobacteria. In this study, a prediction model for cyanobacteria occurrences was developed using only three dominant environmental variables; water temperature, velocity and phosphorus concentration. These environmental variables were selected due to not only direct or joint contribution to algal bloom but also the ease of their availability either through direct measurements or as modelled responses in the river location of interest. In order to apply bacterial growth dynamic to the model, weight functions which quantify the importance assigned to the three variables depending on the cell number at the preceding time, were formulated. An extensive dataset spanning from 2013 to 2018 at 16 representative locations across the four major rivers in South Korea was used to develop and validate the model. Through cross-validation, this model was shown to have more than 75% forecasting accuracy despite the use of a relatively simple predictive algorithm. As the developed model makes use of commonly available environmental variables, it can easily be extended to locations across the country where very limited or no prior information about cyanobacteria bloom is available.

Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes

Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms; random forest clearly outperforms the other methods with very promising results.

Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

The frequency of toxin-producing cyanobacterial blooms has increased in recent decades due to nutrient enrichment and climate change. Because Microcystis blooms are related to different environmental conditions, identifying potential nutrient control targets can facilitate water quality managers to reduce the likelihood of microcystins (MCs) risk. However, complex biotic interactions and field data limitations have constrained our understanding of the nutrient-microcystin relationship. This study develops a Bayesian modelling framework with intracellular and extracellular MCs that characterize the relationships between different environmental and biological factors. This model was fit to the across-lake dataset including three bloom-plagued lakes in China and estimated the putative thresholds of total nitrogen (TN) and total phosphorus (TP). The lake-specific nutrient thresholds were estimated using Bayesian updating process. Our results suggested dual N and P reduction in controlling cyanotoxin risks. The total Microcystis biomass can be substantially suppressed by achieving the putative thresholds of TP (0.10 mg/L) in Lakes Taihu and Chaohu, but a stricter TP target (0.05 mg/L) in Dianchi Lake. To maintain MCs concentrations below 1.0 μg/L, the estimated TN threshold in three lakes was 1.8 mg/L, but the effect can be counteracted by the increase of temperature. Overall, the present approach provides an efficient way to integrate empirical knowledge into the data-driven model and is helpful for the management of water resources.

How Do Eutrophication and Temperature Interact to Shape the Community Structures of Phytoplankton and Fish in Lakes?

Freshwater ecosystems are among the systems most threatened and impacted by anthropogenic activities, but there is still a lack of knowledge on how this multi-pressure environment impacts aquatic communities in situ. In Europe, nutrient enrichment and temperature increase due to global change were identified as the two main pressures on lakes. Therefore, we investigated how the interaction of these two pressures impacts the community structure of the two extreme components of lake food webs: phytoplankton and fish. We modelled the relationship between community components (abundance, composition, size) and environmental conditions, including these two pressures. Different patterns of response were highlighted. Four metrics responded to only one pressure and one metric to the additive effect of the two pressures. Two fish metrics (average body-size and biomass ratio between perch and roach) were impacted by the interaction of temperature and eutrophication, revealing that the effect of one pressure was dependent on the magnitude of the second pressure. From a management point of view, it appears necessary to consider the type and strength of the interactions between pressures when assessing the sensitivity of communities, otherwise their vulnerability (especially to global change) could be poorly estimated.

Microcystin-leucine-arginine induced neurotoxicity by initiating mitochondrial fission in hippocampal neurons

Microcystin-leucine-arginine (MC-LR) can cross the blood-brain barrier (BBB) and demonstrate potent acute hippocampal neurotoxicity. Chronic exposure to MC-LR has been confirmed to cause learning and memory deficits in mice, but the potential molecular mechanism of MC-LR-caused neurotoxicity is still unclear. In this research, we observed that MC-LR induced oxidative stress, mitochondrial fission and apoptosis in HT-22 hippocampal neurons. Moreover, further studies identified that MC-LR induced mitochondrial fragmentation via activating Dynamin-related protein 1 (Drp1) and Mitochondrial fission factor (Mff), contributing to apoptosis of hippocampal neuronal cells. The observed effects were associated with increased intracellular Ca²⁺ and reduced activity of protein phosphatases 2A (PP2A) as results of MC-LR exposure in hippocampal neuron cells. Ca²⁺ activates CaMK II and Akt to enhance phosphorylation of Drp1 at Ser616 residue. Inhibition of PP2A activity increased AMPK activity to mediate phosphorylation of Mff. Our data proved that MC-LR can cause mitochondrial fragmentation in hippocampal neurons, which provides novel perception to explore the underlying molecular mechanism associated with MC-LR-induced neurotoxicity and Alzheimer's disease-like changes.

Blooming cyanobacteria alter water flea reproduction via exudates of estrogen analogues

Cyanobacteria blooms are increasing globally, with further increases predicted in association with climate change. Recently, some cyanobacteria species have been identified as a source of estrogenic effects in aquatic animals. To explore possible estrogenic effects of Microcystis aeruginosa (an often-dominant cyanobacteria species) on zooplankton, we examined effects of cyanobacteria exudates (MaE, 2 × 10⁴ and 4 × 10⁵ cells/ml) on reproduction in Daphnia magna. We analyzed physiological, biochemical and molecular characteristics of exposed Daphnia via both chronic and acute exposures. MaE at both low and high cell density enhanced egg number (15.4% and 23.3%, respectively) and reproduction (37.7% and 52.4%, respectively) in D. magna similar to 10 μg/L estradiol exposure. In addition, both MaE of low and high cell densities increased population growth rate (15.8% and 19.6%, respectively) and reproductive potential (60% and 83%, respectively) of D. magna. These exudates promoted D. magna reproduction by stimulating 17β-hydroxysteroid-dehydrogenase (17β-HSD) activity and production of ecdysone and juvenile hormone, and by enhancing vitellogenin biosynthesis via up-regulating expression of Vtg1 and Vtg2. However, increased expression (6.6 times higher than controls) of a detoxification gene (CYP360A8) indicated that MaE might also induce toxicity in D. magna. Reproductive interference of zooplankton by blooming cyanobacteria might negatively affect foodwebs because MaE-induced zooplankton population increase would enhance grazing and reduce abundance of edible algae, thereby adding to the list of known disruptive properties of cyanobacterial blooms.

Response of cyanobacteria and phytoplankton abundance to warming, extreme rainfall events and nutrient enrichment

Cyanobacterial blooms are an increasing threat to water quality and global water security caused by the nutrient enrichment of freshwaters. There is also a broad consensus that blooms are increasing with global warming, but the impacts of other concomitant environmental changes, such as an increase in extreme rainfall events, may affect this response. One of the potential effects of high rainfall events on phytoplankton communities is greater loss of biomass through hydraulic flushing. Here we used a shallow lake mesocosm experiment to test the combined effects of: warming (ambient vs. +4°C increase), high rainfall (flushing) events (no events vs. seasonal events) and nutrient loading (eutrophic vs. hypertrophic) on total phytoplankton chlorophyll-a and cyanobacterial abundance and composition. Our hypotheses were that: (a) total phytoplankton and cyanobacterial abundance would be higher in heated mesocosms; (b) the stimulatory effects of warming on cyanobacterial abundance would be enhanced in higher nutrient mesocosms, resulting in a synergistic interaction; (c) the recovery of biomass from flushing induced losses would be quicker in heated and nutrient-enriched treatments, and during the growing season. The results supported the first and, in part, the third hypotheses: total phytoplankton and cyanobacterial abundance increased in heated mesocosms with an increase in common bloom-forming taxa-Microcystis spp. and Dolichospermum spp. Recovery from flushing was slowest in the winter, but unaffected by warming or higher nutrient loading. Contrary to the second hypothesis, an antagonistic interaction between warming and nutrient enrichment was detected for both cyanobacteria and chlorophyll-a demonstrating that ecological surprises can occur, dependent on the environmental context. While this study highlights the clear need to mitigate against global warming, oversimplification of global change effects on cyanobacteria should be avoided; stressor gradients and seasonal effects should be considered as important factors shaping the response.

Estimation of Kd(PAR) in inland waters across China in relation to the light absorption of optically active components

The comprehensive analysis of the relationships between the attenuation of photosynthetic active radiation (K_d(PAR)) and light absorption is an imperative requirement to retrieve K_d(PAR) from remote sensing data for aquatic environments. The spatial distributions of the K_d(PAR) and light absorption of optically active components (a_OACs) were routinely estimated in China lakes and reservoirs. Spatial K_d(PAR) was relatively dependent on the inorganic particles (average relative contribution of 57.95%). The a_OACs could explain 70-87% of K_d(PAR) variations. A linear model is used to predict K_d(PAR), as a function of light absorption coefficient of phytoplankton (a_phy), colored dissolved organic matter (a_CDOM), and inorganic particles (a_NAP): K_d(PAR) = 0.41 + 0.57 × a_CDOM + 0.96 × a_NAP + 0.57 × a_phy (R² = 0.87, n = 741, p < 0.001). In the lakes with low TSM concentration and non-eutrophic lakes with high TSM, a_CDOM was the most powerful predicting factor on K_d(PAR). In eutrophic lakes with high TSM, a_NAP had the most significant impact on K_d(PAR). This study allowed K_d(PAR) to be predicted from a_OACs values in the inland waters.

The Influence of Climate Change on the Restoration Trajectory of a Nutrient-Rich Deep Lake

Nutrient reduction in impacted lowland freshwater systems is ecologically and culturally important. Gaining a greater insight into how lakes respond to lowering nutrient loads and how climate-driven physical limnology affects present and future cycling of available nutrients is important for ecosystem resource management. This study examines the nutrient decline in a hypereutrophic freshwater lake (Rostherne Mere, Cheshire, UK) 25 years after sewage effluent diversion, a uniquely long-term analysis of a recovering nutrient-rich deep lake. Using nutrient, phytoplankton, climate and catchment hydrological monitoring, the contemporary lake system is compared to previous studies from 1990 to 2002. Nutrient change since point source load diversion showed annual average and maximum phosphorus (P) concentrations decreased significantly for the first 10 years (1992: ~ 600 µg P L−1; 2002: ~ 200 µg P L−1), but have since stabilised due to a substantial legacy sediment P internal load. Dissolved inorganic nitrogen (DIN) concentrations have not substantially changed since diversion, resulting in the alteration of the DIN/SRP ratio from a system characterised by N limitation (N:P ~ 5), to one predominantly P limited (N:P > 20). Nutrient changes over this time are shown to drive ecological change, especially in the cyanobacterial and algal communities. Furthermore, very high-resolution monitoring of lake inflow and outflow (every 5 min during 2016) shows that water residence time at this lake is significantly shorter than previously estimated (~ 0.8 years compared to previous estimates of ~ 1.6–2.4 years). Together with long-term data demonstrating that the stratification period at Rostherne Mere has increased by 40 days over the last ~ 50 years (due to later autumnal mixing), we show that a rapid rate of epilimnetic flushing together with a long stratification period substantially reduces the available epilimnetic P during the summer cyanobacterial bloom. This is of growing importance for many such lakes, given widespread climate-driven lengthening of stratification and a national trend of decreasing summer rainfall (decreasing seasonal flushing) but more intense summer storm events (resulting in short-term flushing events).

Predicting Lake Quality for the Next Generation: Impacts of Catchment Management and Climatic Factors in a Probabilistic Model Framework

Lake ecosystems across the world are under combined pressures of eutrophication and climate change, which increase the risk of harmful cyanobacteria blooms, reduced ecological status, and degraded ecosystem services. In Europe, the third cycle of river basin management plans (2021–2027) according to the Water Framework Directive must take into account the potential impacts of climate change on water quality, including effects on relevant biological indicators. Here, we applied a Bayesian network as a meta-model for linking future climate and land-use scenarios for the time horizon 2050–2070, via process-based catchment and lake models, to cyanobacteria abundance and ecological status of a eutrophic lake. Building upon previous applications of the model, a new version was developed to include relevant climatic variables such as wind speed. Explorative scenarios showed that the combination of low wind and high temperature gave the most synergistic effects on cyanobacteria under high levels of eutrophication (Chl-a concentration). Considering the management target of good ecological status, however, the climate-related promotion of cyanobacteria blooms contributed most to degrading the ecological status at intermediate levels of eutrophication. Future developments of this model will aim to strengthen the link between climate variables and ecological responses, to make the model also useful for seasonal forecasting.

Learning and memory deficits and alzheimer's disease-like changes in mice after chronic exposure to microcystin-LR

Previous studies have demonstrated that toxins produced by toxic cyanobacterial blooms are hazardous materials. Although microcystin-LR (MC-LR) has been revealed to inflict damage to the brain, the mechanisms underlying its neurotoxicity as a result of chronic exposure to MC-LR are not fully described. In this study, the mice were exposed to MC-LR dissolved in drinking water at doses of 1, 7.5, 15, or 30 μg/L for 180 days. MC-LR accumulated mostly in the mouse hippocampus (55 ng/g dry weight) followed by cortex (28 ng/g dry weight) after exposure to MC-LR at 30 μg/L. MC-LR exposure at this concentration induced dysfunction of learning and memory, accompanied with apoptosis of neuronal cells (with 10% reduction of the neurons in the CA1 region and 15% in the CA2 region), reduction of spine density, accumulation of β-amyloid plaques 1-42 (Aβ1-42), and enhanced phosphorylation of tau (p-tau) in the brain, which is characteristic of Alzheimer's disease (AD). These data indicate that MC-LR may induce AD-like pathology. Following prolonged exposure, MC-LR significantly upregulated the ratio of proBDNF to BDNF by downregulating the tPA levels, thereby activating downstream signaling pathways to improve the expression of p-JNK, and c-Jun while to inhibit the expression of p-Creb and p-PKC. This study uncovered new molecular mechanisms that account for neurotoxicity after chronic exposure to MC-LR, which has wide-ranging implications for public health.

The future depends on what we do today - Projecting Europe's surface water quality into three different future scenarios

There are infinite possible future scenarios reflecting the impacts of anthropogenic multiple stress on our planet. These impacts include changes in climate and land cover, to which aquatic ecosystems are especially vulnerable. To assess plausible developments of the future state of European surface waters, we considered two climate scenarios and three storylines describing land use, management and anthropogenic development ('Consensus', 'Techno' and 'Fragmented', which in terms of environmental protection represent best-, intermediate- and worst-case, respectively). Three lake and four river basins were selected, representing a spectrum of European conditions through a range of different human impacts and climatic, geographical and biological characteristics. Using process-based and empirical models, freshwater total nitrogen, total phosphorus and chlorophyll-a concentrations were projected for 2030 and 2060. Under current conditions, the water bodies mostly fail good ecological status. In future predictions for the Techno and Fragmented World, concentrations further increased, while concentrations generally declined for the Consensus World. Furthermore, impacts were more severe for rivers than for lakes. Main pressures identified were nutrient inputs from agriculture, land use change, inadequately managed water abstractions and climate change effects. While the basins in the Continental and Atlantic regions were primarily affected by land use changes, in the Mediterranean/Anatolian the main driver was climate change. The Boreal basins showed combined impacts of land use and climate change and clearly reflected the climate-induced future trend of agricultural activities shifting northward. The storylines showed positive effects on ecological status by classical mitigation measures in the Consensus World (e.g. riparian shading), technical improvements in the Techno World (e.g. increasing wastewater treatment efficiency) and agricultural extensification in the Fragmented World. Results emphasize the need for implementing targeted measures to reduce anthropogenic impacts and the importance of having differing levels of ambition for improving the future status of water bodies depending on the societal future to be expected.

Response of the eukaryotic plankton community to the cyanobacterial biomass cycle over 6 years in two subtropical reservoirs

Although it is widely recognized that cyanobacterial blooms have substantial influence on the plankton community in general, their correlations with the whole community of eukaryotic plankton at longer time scales remain largely unknown. Here, we investigated the temporal dynamics of eukaryotic plankton communities in two subtropical reservoirs over a 6-year period (2010–2015) following one cyanobacterial biomass cycle—the cyanobacterial bloom (middle 2010), cyanobacteria decrease (late 2010–early 2011), non-bloom (2011–2014), cyanobacteria increase, and second bloom (late 2014–2015). The eukaryotic community succession that strongly correlated with this cyanobacterial biomass cycle was divided into four periods, and each period had distinct characteristics in cyanobacterial biomass and environments in both reservoirs. Integrated co-occurrence networks of eukaryotic plankton based on the whole study period revealed that the cyanobacterial biomass had remarkably high network centralities, and the eukaryotic OTUs that had stronger correlations with the cyanobacterial biomass exhibited higher centralities. The integrated networks were also modularly responded to different eukaryotic succession periods, and therefore correlated with the cyanobacterial biomass cycle. Moreover, sub-networks based on the different eukaryotic succession periods indicated that the eukaryotic co-occurrence patterns were not constant but varied largely associating with the cyanobacterial biomass. Based on these long-term observations, our results reveal that the cyanobacterial biomass cycle created distinct niches between persistent bloom, non-bloom, decrease and increase of cyanobacteria, and therefore associated with distinct eukaryotic plankton patterns. Our results have important implications for understanding how complex aquatic plankton communities respond to cyanobacterial blooms under the changing environments.

Protecting and restoring Europe's waters: An analysis of the future development needs of the Water Framework Directive

The Water Framework Directive (WFD) is a pioneering piece of legislation that aims to protect and enhance aquatic ecosystems and promote sustainable water use across Europe. There is growing concern that the objective of good status, or higher, in all EU waters by 2027 is a long way from being achieved in many countries. Through questionnaire analysis of almost 100 experts, we provide recommendations to enhance WFD monitoring and assessment systems, improve programmes of measures and further integrate with other sectoral policies. Our analysis highlights that there is great potential to enhance assessment schemes through strategic design of monitoring networks and innovation, such as earth observation. New diagnostic tools that use existing WFD monitoring data, but incorporate novel statistical and trait-based approaches could be used more widely to diagnose the cause of deterioration under conditions of multiple pressures and deliver a hierarchy of solutions for more evidence-driven decisions in river basin management. There is also a growing recognition that measures undertaken in river basin management should deliver multiple benefits across sectors, such as reduced flood risk, and there needs to be robust demonstration studies that evaluate these. Continued efforts in 'mainstreaming' water policy into other policy sectors is clearly needed to deliver wider success with WFD goals, particularly with agricultural policy. Other key policy areas where a need for stronger integration with water policy was recognised included urban planning (waste water treatment), flooding, climate and energy (hydropower). Having a deadline for attaining the policy objective of good status is important, but even more essential is to have a permanent framework for river basin management that addresses the delays in implementation of measures. This requires a long-term perspective, far beyond the current deadline of 2027.