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

Rainfall seasonality shapes microbial assembly and niche characteristics in Yunnan Plateau lakes, China

Microorganisms are crucial components of freshwater ecosystems. Understanding the microbial community assembly processes and niche characteristics in freshwater ecosystems, which are poorly understood, is crucial for evaluating microbial ecological roles. The Yunnan Plateau lakes in China represent a freshwater ecosystem that is experiencing eutrophication due to anthropogenic activities. Here, variation in the assembly and niche characteristics of both prokaryotic and microeukaryotic communities was explored in Yunnan Plateau lakes across two seasons (dry season and rainy season) to determine the impacts of rainfall and environmental conditions on the microbial community and niche. The results showed that the environmental heterogeneity of the lakes decreased in the rainy season compared to the dry season. The microbial (bacterial and microeukaryotic) α-diversity significantly decreased during the rainy season. Deterministic processes were found to dominate microbial community assembly in both seasons. β-Diversity decomposition analysis revealed that microbial community compositional dissimilarities were dominated by species replacement processes. The co-occurrence networks indicated reduced species complexity for microbes and a destabilized network for prokaryotes prior to rainfall, while the opposite was found for microeukaryotes following rainfall. Microbial niche breadth decreased significantly in the rainy season. In addition, lower prokaryotic niche overlap, but greater microeukaryotic niche overlap, was observed after rainfall. Rainfall and environmental conditions significantly affected the microbial community assembly and niche characteristics. It can be concluded that rainfall and external pollutant input during the seasonal transition alter the lake environment, thereby regulating the microbial community and niche in these lakes. Our findings offer new insight into microbiota assembly and niche patterns in plateau lakes, further deepening the understanding of freshwater ecosystem functioning.

Climate change impact on sub-tropical lakes - Lake Kinneret as a case study

Climate change is anticipated to alter lake ecosystems by affecting water quality, potentially resulting in loss of ecosystem services. Subtropical lakes have high temperatures to begin with and are expected to exhibit higher temperatures all year round which might affect the thermal structure and ecological processes in a different manner than lakes in temperate zones. In this study the ecosystem response of the sub-tropical Lake Kinneret to climate change was explored using lake ecosystem models. Projection reliability was increased by using a weather generator and ensemble modelling, confronting uncertainty of both climate projections and lake models. The study included running two 1D hydrodynamic-biogeochemical models over one thousand realizations of two gradual temperature increase scenarios that span over 49 years. Our predictions show that an increase in air temperature would have subtle effects on stratification properties but may result in considerable changes to biogeochemical processes. Water temperature rise would cause a reduction in dissolved oxygen. Both of these changes would produce elevated phosphate and lowered ammonium concentrations. In turn, these changes are predicted to modify the phytoplankton community, expressed chiefly in increased cyanobacteria blooms at the expense of green phytoplankton and dinoflagellates; these changes may culminate in overall reduction of primary production. Identification of these trends would not be possible without the use of many realizations of climate scenarios. The use of ensemble modelling increased prediction reliability and highlighted elements of uncertainty. Though we use Lake Kinneret, the patterns identified most likely indicate processes that are expected in sub-tropical lakes in general.

Dynamics of algal blooms in typical low-latitude plateau lakes: Spatiotemporal patterns and driving factors

The alpine lakes distributed on the plateau are crucial for the hydrological, and biogeochemical cycle, and also serve as a guarantee for regional economic development and human survival. However, under the influence of human interference and climate fluctuations, lakes are facing problems of eutrophication and subsequent algal blooms (ABs) with acceleration, and the development and driving factors of this phenomenon need to be considered as a whole. In this study, ten lakes located on the Yunnan-Guizhou Plateau were selected as the study area to analyze the spatiotemporal distribution of ABs and possible controlling forces. The FAI (Floating Algae Index) derived from multiple MODIS products and water quality data under high-frequency monitoring were selected as the data sources for characterizing ABs. Three nutrient parameters and five meteorological variables were used to explore the driving factors affecting ABs. Various methods of trend detection and correlation analysis have been applied. The main results are as follows: (1) Dianchi Lake (in lake area) and Xingyun Lake (in area proportion) are the two lakes with the most serious ABs in the historical period; (2) ABs are mainly distributed on the shoreline and northern edge of lakes, and tend to stay away from the lake center during high-temperature periods of the day; (3) Six lakes show a decreasing trend in ABs, especially after 2018, while other lakes (including Fuxian, Chenghai, Yangzong, and Erhai) are increasing, not only in peak value but also in duration; (4) Lakes with severe ABs are all P-restricted lakes, the minimum temperature is the most sensitive meteorological factor, while the impact of precipitation against ABs has a time lag; (5) Establishing a warning system of temperature and nutrient concentration is critical in ABs adaptive strategy. This study is expected to provide scientific references for regional water management and the restoration of the eutrophic aquatic ecosystem.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

Cyanobacteria and cyanotoxins in the environment and water intakes: Reports, diversity of congeners, detection by mass spectrometry and their impact on health

Cyanobacteria are aquatic microorganisms of high interest for research due to the production of secondary metabolites, among which the most popular are cyanotoxins, responsible for causing severe poisoning in humans and animals through ingestion or contact with contaminated water bodies. Monitoring the number of cyanobacteria in water and concentrations of secreted cyanotoxins with the aid of sensitive and reliable methods is considered the primary action for evaluating potentially toxic blooms. There is a great diversity of methods to detect and identify these types of micro contaminants in water, differing by the degree of sophistication and information provided. Mass Spectrometry stands out for its accuracy and sensitivity in identifying toxins, making it possible to identify and characterize toxins produced by individual species of cyanobacteria, in low quantities. In this review, we seek to update some information about cyanobacterial peptides, their effects on biological systems, and the importance of the main Mass Spectrometry methods used for detection, extraction, identification and monitoring of cyanotoxins.

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.

Urbanization shifts long-term phenology and severity of phytoplankton blooms in an urban lake through different pathways

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi-source remote sensing data and integrated the Virtual-Baseline Floating macroAlgae Height (VB-FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long-term (2000-2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011-2021 to 2000-2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2 ) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change.

Microcystin concentrations and congener composition in relation to environmental variables across 440 north-temperate and boreal lakes

Understanding the environmental conditions and taxa that promote the occurrence of cyanobacterial toxins is imperative for effective management of lake ecosystems. Herein, we modeled total microcystin presence and concentrations with a broad suite of environmental predictors and cyanobacteria community data collected across 440 Canadian lakes using standardized methods. We also conducted a focused analysis targeting 14 microcystin congeners across 190 lakes, to examine how abiotic and biotic factors influence their relative proportions. Microcystins were detected in 30 % of lakes, with the highest total concentrations occurring in the most eutrophic lakes located in ecozones of central Canada. The two most commonly detected congeners were MC-LR (61 % of lakes) and MC-LA (37 % of lakes), while 11 others were detected more sporadically across waterbodies. Congener diversity peaked in central Canada where cyanobacteria biomass was highest. Using a zero-altered hurdle model, the probability of detecting microcystin was best explained by increasing Microcystis biomass, Daphnia and cyclopoid biomass, soluble reactive phosphorus, pH and wind. Microcystin concentrations increased with the biomass of Microcystis and other less dominant cyanobacteria taxa, as well as total phosphorus, cyclopoid copepod biomass, dissolved inorganic carbon and water temperature. Collectively, these models accounted for 34 % and 70 % of the variability, respectively. Based on a multiple factor analysis of microcystin congeners, cyanobacteria community data, environmental and zooplankton data, we found that the relative abundance of most congeners varied according to trophic state and were related to a combination of cyanobacteria genera biomasses and environmental variables.

Characterization of lacustrine harmful algal blooms using multiple biomarkers: Historical processes, driving synergy, and ecological shifts

Harmful algal blooms (HABs) producing toxic metabolites are increasingly threatening environmental and human health worldwide. Unfortunately, long-term process and mechanism triggering HABs remain largely unclear due to the scarcity of temporal monitoring. Retrospective analysis of sedimentary biomarkers using up-to-date chromatography and mass spectrometry techniques provide a potential means to reconstruct the past occurrence of HABs. By combining aliphatic hydrocarbons, photosynthetic pigments, and cyanotoxins, we quantified herein century-long changes in abundance, composition, and variability of phototrophs, particularly toxigenic algal blooms, in China's third largest freshwater Lake Taihu. Our multi-proxy limnological reconstruction revealed an abrupt ecological shift in the 1980s characterized by elevated primary production, Microcystis-dominated cyanobacterial blooms, and exponential microcystin production, in response to nutrient enrichment, climate change, and trophic cascades. The empirical results from ordination analysis and generalized additive models support climate warming and eutrophication synergy through nutrient recycling and their feedback through buoyant cyanobacterial proliferation, which sustain bloom-forming potential and further promote the occurrence of increasingly-toxic cyanotoxins (e.g., microcystin-LR) in Lake Taihu. Moreover, temporal variability of the lake ecosystem quantified using variance and rate of change metrics rose continuously after state change, indicating increased ecological vulnerability and declined resilience following blooms and warming. With the persistent legacy effects of lake eutrophication, nutrient reduction efforts mitigating toxic HABs probably be overwhelmed by climate change effects, emphasizing the need for more aggressive and integrated environmental strategies.

Distinct indicators of land use and hydrology characterize different aspects of riverine phytoplankton communities

Given the many threats to freshwater biodiversity, we need to be able to resolve which of the multiple stressors present in rivers are most important in driving change. Phytoplankton are a key component of the aquatic ecosystem, their abundance, species richness and functional richness are important indicators of ecosystem health. In this study, spatial variables, physiochemical conditions, water flow alterations and land use patterns were considered as the joint stressors from a lowland rural catchment. A modeling approach combining an ecohydrological model with machine learning was applied. The results implied that land use and flow regime, rather than nutrients, were most important in explaining differences in the phytoplankton community. In particular, the percentage of water body area and medium level residential urban area were key to driving the rising phytoplankton abundance in this rural catchment. The proportion of forest and pasture area were the leading factors controlling the variations of species richness. In this case deciduous forest cover affected the species richness in a positive way, while, pasture share had a negative effect. Indicators of hydrological alteration were found to be the best predictors for the differences in functional richness. This integrated model framework was found to be suitable for analysis of complex environmental conditions in river basin management. A key message would be the significance of forest area preservation and ecohydrological restoration in maintaining both phytoplankton richness and their functional role in river ecosystems.

Autochthonous sources and drought conditions drive anomalous oxygen-consuming pollution increase in a sluice-controlled reservoir in eastern China

Freshwater reservoirs are an important type of inland waterbody. However, they can suffer from oxygen-consuming pollution, which can seriously threaten drinking water safety and negatively impact the health of aquatic ecosystems. Oxygen-consuming pollutants originate from both allochthonous and autochthonous sources, and have temporally and spatially heterogeneous drivers. Datanggang Reservoir, China, is located in a small agricultural watershed; it is controlled by multiple sluice gates. Anomalously high oxygen consumption indicators were observed in this reservoir in March 2021. Here, it was hypothesized that autochthonous sources were the primary drivers of oxygen-consuming pollution in the reservoir under drought conditions. Datasets of water quality, precipitation, primary productivity, and sediment were used to analyze water quality trends in the reservoir and inflow rivers, demonstrating the effects of allochthonous inputs and autochthonous pollution. No correlation was found between reservoir oxygen consumption indicators and allochthonous inputs; reservoir oxygen consumption indicators and chlorophyll-a concentration were significantly positively correlated (p < 0.05). Substantially lower precipitation and higher water temperature and pH (compared to historical levels) were also observed before the pollution event. Therefore, during this period the hydrological conditions, water temperature, pH, and other variables caused by short-term drought conditions may have facilitated phytoplankton growth in the reservoir. This contributed to a large increase in autochthonous oxygen-consuming pollutants, as reflected by the abnormally high indicators. Sediments contaminated with organic matter may also have been an important contributor. As the effects of environmental management and pollution control continue to emerge, exogenous pollutants imported from the land to reservoirs are currently effectively controlled. However, endogenous pollutants driven by a variety of factors, such as meteorology and hydrology, will likely become the main drivers of short-term changes in oxygen-consuming pollution in freshwater reservoirs in the foreseeable future.

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.

Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change

Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso‑ and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.