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Nutrient runoff from agricultural activities in the watershed of Western Lake Erie (WLE) is a dominant driver of harmful algal blooms (HABs). While phosphorus (P) is a key factor causing these blooms and has been the focus for researchers and policymakers, the influence of nitrogen (N) on bloom dynamics has been overlooked. Total Kjeldahl N (TKN; organic N and ammonium N) has not been the focus of eutrophication research but was recently linked to bloom development in WLE. Here, monotonic and oscillatory statistical trend analyses were performed to interpret long-term (1982 to 2022) patterns of TKN in the Maumee River and were compared to algal biomass data as chlorophyll a. A predictive regression model used principal component analysis to estimate a chlorophyll-based index of HABs in WLE, and a systematic iterative process identified that TKN influences bloom dynamics along with soluble reactive phosphorus (SRP), total suspended solids (TSS) and flow. Although TKN loads exhibited a long-term decline, this decrease did not correspond to reduced HAB severity, reflecting the strong influence of flow-driven hydrologic variability on nutrient delivery and bloom response. The modeling results demonstrate that TKN, together with SRP, TSS, and flow, significantly contributes to predicting bloom magnitude. These findings highlight the need for dual-nutrient (N and P) management strategies and additional analyses of nutrient–hydrology interactions to improve HAB mitigation in WLE. 

Cyanobacteria are important primary producers, sources of secondary metabolites, and sentinels of environmental change in aquatic ecosystems – including large estuaries. Here, we newly investigated cyanobacterial diversity within the Albemarle Pamlico Sound System (APES) using (16S rRNA) gene amplicon sequencing analyses. Substantial cyanobacterial diversity including lineages lacking current isolates were recovered (46 genera, 17 potentially cyanotoxic), with oligohaline waters of the Albemarle Sound and its tributaries being notable regional hotspot for diversity. Salinity and temperature were influential drivers of cyanobacterial community composition. Picocyanobacteria (cells <3 µm in diameter) were abundant in amplicon sequence libraries (72% of cyanobacterial sequences) – especially populations withinSynechococcusSubClade 5.2. Picocyanobacteria along with picoeukaryotes were large contributors to total phytoplankton biomass comprising ~47% of chlorophyll a. Further, the picocyanobacterial generaSynechococcus,Cyanobium, andSynechocystis(55.4%, 14.8%, and 12.9% of cyanobacterial sequences, respectively) formed a core community spanning from freshwater regions (eastern AST, D949) to polyhaline environments (NRE100 downstream stations to PS5), suggesting resilience to significant salinity fluctuations and associated environmental changes. Amplicon sequence variant (ASV) and environmental data indicate the presence of several putative ecotypes, as well as distinct abundance patterns among closely related populations, highlighting substantial fitness variability among subspecies. Notably, potentially cyanotoxic genera,Synechocystis,Planktothrix,Plectonema, andDolichospermumwere the four more abundant detected in polyhaline APES regions, far beyond conspicuous freshwater sources. These findings reveal previously unrecognized potential sources of cyanotoxics in estuarine food webs and habitats, underscoring the ecological significance of cyanobacterial community dynamics across salinity gradients. 

ABSTRACT 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. IMPORTANCEOur 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. 

Abstract Terrestrial hydrological and nutrient cycles are subjected to major disturbances by agricultural operations and urbanization that profoundly influence freshwater resources. Non‐point source pollution is one of the primary causes for water quality deterioration, and thus an emerging imperative in limnology is establishing empirical models that connect watershed attributes and hydrological drivers with lake nutrient dynamics. Here, we compiled three nation‐wide nutrient, meteorological, and watershed‐landscape data sets, to develop Generalized Linear Models that predict lake phosphorus and nitrogen concentrations as a function of the surrounding watershed characteristics within various hydrological distances across 104 Chinese lakes and reservoirs. Our national‐scale investigation revealed that lake nutrient concentrations can be satisfactorily predicted by proxies of natural drivers and anthropogenic activities, reflecting the properties of the surrounding watershed. Counter to previous studies, we found that China's lake nutrient concentrations strongly depend on watershed characteristics within a hydrological distance of less than 45 km rather than the entire watershed. Furthermore, extensive human activities in watersheds not only compromise our predictive capacity, but also increase the hydrological distance that is relevant to predict lake nutrients. This national‐scale characterization can inform one of the most contentious issues in the context of China's lake management, that is, the determination of the extent of the nearshore area, where nutrient control should be prioritized. As far as we know, our study represents the first attempt to apply the concept of hydrological distance and establish statistical models that can delineate the critical spatial domain primarily responsible for the nutrient conditions along the watershed‐lake continuum. 

Harmful algal blooms can produce toxins that pose threats to aquatic ecosystems and human health. In this Review, we outline the global trends in harmful algal bloom occurrence and explore the drivers, future trajectories and potential mitigation strategies. Globally, harmful algal bloom occurrence has risen since the 1980s, including a 44% increase from the 2000s to 2010s, especially in Asia and Africa. Enhanced nutrient pollution owing to urbanization, wastewater discharge and agricultural expansion are key drivers of these increases. In contrast, changes have been less substantial in high-income regions such as North America, Europe and Oceania, where policies to mitigate nutrient pollution have stabilized bloom occurrences since the 1970s. However, since the 1990s, climate warming and legacy nutrient pollution have driven a resurgence in toxic algal blooms in some US and European lakes, highlighting the inherent challenges in mitigating harmful blooms in a warming climate. Indeed, advancing research on harmful algal bloom dynamics and projections largely depends on effectively using data from multiple sources to understand environmental interactions and enhance modelling techniques. Integrated monitoring networks across various spatiotemporal scales and data-sharing frameworks are essential for improving harmful algal bloom forecasting and mitigation.