Algal Bloom Dynamics Research Articles

Satellite-Based Detection of Algal Blooms in Large Alpine Lake Sevan: Can Satellite Data Overcome the Unavoidable Limitations in Field Observations?

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

Untangling the coupling effect of water quality and quantity on lake algal blooms in Lake Hulun from a dual perspective of remote sensing and sediment cores

Algal blooms and sediment diatoms are crucial indicators of lake water ecology, influenced by water quantity and quality. However, the coupled effects of water quality and quantity changes on algal blooms are still unclear, especially for lakes in cold and arid regions. This study assessed the long-term variations in algal blooms in Hulun Lake using a novel approach combining remote sensing and sediment core samples for diatom analysis. Two mutation points from the structural change test were identified in approximately 2000 and 2010 for algal bloom area (MBE) and sediment diatom richness, indicating asynchronous algal blooms. A structural equation model (SEM) demonstrated that water level (WL) changes were the dominant influencing factor, co-driving the variations in algal blooms and sediment diatoms in conjunction with total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). The results revealed nonlinear relationships between the lake WL, TN, Chla, and COD. The water level of 543 m emerged as a critical threshold affecting the relationship between water quality and quantity. Distinct differences in this relationship were observed when water levels were above or below this threshold. These variations became particularly pronounced during periods of high and low water levels. The results provide novel insights into the dynamics of algal blooms and can further support lake ecosystem conservation and management.

Cyanophage-encoded auxiliary metabolic genes in modulating cyanobacterial metabolism and algal bloom dynamics

Cyanophages play a pivotal role in controlling cyanobacterial populations in aquatic environments. These dsDNA viruses harbor auxiliary metabolic genes (AMGs) that modulate the key metabolic processes of their cyanobacterial hosts, such as Photosynthesis, nutrient uptake for the optimization of viral replication. Recently, pan1~pan5 and pam1~pam5 cyanophages have been isolated from the fifth largest water resource in China; Lake Chaohu. Detailed genomic analysis of these phages revealed that these isolated cyanophages especially Pan1, Pam2 and Pam3 possess unique AMGs that significantly enhance the metabolic activities of their hosts, potentially leading to the suppression of bloom formation and stabilization of the ecological dynamics of Lake Chaohu. Our findings provide concrete evidence that cyanophages encoding AMGs could serve as effective biocontrol agents against harmful algal blooms, offering a targeted approach to manage these environmental threats. The integration of cyanophage-based management therapies with traditional methods could advance the efficiency and sustainability of controlling cyanobacterial outbreaks, paving the way for novel applications in water resource management. This review emphasizes the importance and critical need for further exploration of phage-host dynamics to fully harness the potential of cyanophages in ecosystem regulation.

Potential siderophore-dependent mutualism in the harmful dinoflagellate Alexandrium pacificum (Group IV) and bacterium Photobacterium sp. TY1-4 under iron-limited conditions

Specific bacterial species induce algal blooms by producing growth-promoting substances, such as siderophores, under iron-limited conditions. However, the molecular mechanisms underlying these effects remain poorly understood. This study investigates the interactions between the harmful dinoflagellate Alexandrium pacificum (Group IV) and siderophore-producing bacteria, with a focus on iron acquisition facilitated by bacterial siderophores. During algal bloom seasons in the South Sea of Korea, Photobacterium sp. TY1-4 was isolated, which enhances A. pacificum cell density under iron-deficient conditions, TY1-4 can use the sterile exudates from A. pacificum as the sole source of carbon, suggesting a mutualistic relationship. Transcriptomic and genomic analyses revealed siderophore-mediated redox-based signaling and non-reductive pathways enhancing iron bioavailability. Photobacterium sp. TY1-4 initiates siderophore production through quorum sensing, whereas A. pacificum utilizes specific receptors and transporters for hydroxamate-type siderophores (ApFHUA and ApFHUC) to uptake iron. Three redox key iron-uptake genes were also identified in A. pacificum: membrane-bound ferroxidase ApFET3, high-affinity iron permease ApFTR1, and ferric-chelate reductases/oxidoreductases ApFRE1, with transcription levels inversely related to bioavailable iron. Increased iron bioavailability mediated by siderophores alleviates iron stress in A. pacificum, supporting its growth in iron-scarce environments. Additionally, A. pacificum co-cultured with Photobacterium sp. TY1-4 synthesized high-toxicity STXs, including GTX4, GTX2, and STX. These findings highlight the critical role of bacterial siderophores in iron binding and their potential impact on harmful algal bloom dynamics.

Key roles of carbon metabolic intensity of sediment microbes in dynamics of algal blooms in shallow freshwater lakes

Key roles of carbon metabolic intensity of sediment microbes in dynamics of algal blooms in shallow freshwater lakes

Phycosphere bacterial composition and function in colony and solitary Phaeocystis globosa strains providing novel insights into the algal blooms

Phycosphere bacteria can regulate the dynamics of different algal blooms that impact marine ecosystems. Phaeocystis globosa can alternate between solitary free-living cells and colonies and the latter morphotype is dominate during blooms. The mechanisms underlying the formation of these blooms have received much attention. High throughput sequencing results showed that the bacterial community composition differed significantly between colony and solitary strains in bacterial composition and function. It was found that the genera SM1A02 and Haliea were detected only among the colony strains and contribute to ammonium accumulation in colonies, and the genus Sulfitobacter was abundant among the colony strains that were excellent at producing DMS. In addition, the bacterial communities of the two colony strains exhibited stronger abilities for carbon and sulfur metabolism, energy metabolism, vitamin B synthesis, and signal transduction, providing inorganic and organic nutrients and facilitating tight communication with the host algae, thereby promoting growth and bloom development.

Trace Metals in Phytoplankton: Requirements, Function, and Composition in Harmful Algal Blooms

In a constantly changing environment brought about by the climate crisis and escalated anthropogenic perturbations driven by the growing population, harmful algal bloom dynamics and their impacts are expected to shift, necessitating adaptive management strategies and comprehensive research efforts. Similar to primary productivity, HABs have been thought to be driven primarily by major nutrients such as N, P, and Si. However, recent investigations on the role and importance of micronutrients as limiting factors in aquatic environments have been highlighted. This paper provides a review of metal and phytoplankton interactions, with a specific emphasis on pertinent information on the influence of trace nutrients on growth, toxin production, and other underlying mechanisms related to the dynamics of HABs. Low to near-depleted levels of essential nutrients, including Fe, Cu, Zn, Se, Mn, Co, and Mo, negatively impact cell growth and proliferation of various marine and freshwater HAB species. However, evidence shows that at elevated levels, these trace elements, along with other non-essential ones, could still cause toxic effects to certain HAB species manifested by decreased photosynthetic activities, oxidative stress, ultrastructure damage, and cyst formation. Interestingly, while elevated levels of these metals mostly result in increased toxin production, Co (i.e., yessotoxins, gymnodimine, and palytoxins) and Mn (i.e., isodomoic acid, okadaic and diol esters) enrichments revealed otherwise. In addition to toxin production, releasing dissolved organic matter (DOM), including dissolved organic carbon (DOC) and humic substances, was observed as an adaptation strategy, since these organic compounds have been proven to chelate metals in the water column, thereby reducing metal-induced toxicity. Whilst current research centers on free metal toxicity of specific essential elements such as Cu and Zn, a comprehensive account of how trace metals contribute to the growth, toxin production, and other metabolic processes under conditions reflective of in situ scenarios of HAB-prone areas would yield new perspectives on the roles of trace metals in HABs. With the growing demands of the global population for food security and sustainability, substantial pressure is exerted on the agriculture and aquaculture sector, highlighting the need for effective communication of information regarding the interactions of macro- and micronutrients with HABs to improve existing policies and practices.

Satellite Tracking Reveals the Speed Up of the Lacustrine Algal Bloom Drift in Response to Climate Change.

Satellite evidence indicates a global increase in lacustrine algal blooms. These blooms can drift with winds, resulting in significant changes of the algal biomass spatial distribution, which is crucial in bloom formation. However, the lack of long-term, large-scale observational data has limited our understanding of bloom drift. Here, we have developed a novel method to track the drift using multi-source remote sensing satellites and presented a comprehensive bloom drift data set for four typical lakes: Lake Taihu (China, 2011-2021), Lake Chaohu (China, 2011-2020), Lake Dianchi (China, 2003-2021), and Lake Erie (North America, 2003-2021). We found that blooms closer to the water surface tend to drift faster. Higher temperatures and lower wind speeds bring blooms closer to the water surface, therefore accelerating drift and increasing biomass transportation. Under ongoing climate change, algal blooms are increasingly likely to spread over larger areas and accumulate in downwind waters, thereby posing a heightened risk to water resources. Our research greatly improves the understanding of algal bloom dynamics and provides new insights into the driving factors behind the global expansion of algal blooms. Our bloom-drift-tracking methodology also paves the way for the development of high-precision algal bloom prediction models.

Trend of harmful algal bloom dynamics from GOCI observed diurnal variation of chlorophyll a off Southeast coast of China

Timely and accurate observations of harmful algal blooms dynamics help to coordinate coastal protection and reduce the damage in advance. To date, predicting changes in the spatial distribution of algal blooms has been challenging due to the lack of suitable tools. The paper proposes that the development and disappearance of algal bloom can be monitored by satellite remote sensing in a large area from the diurnal variation of chlorophyll a. In this paper, 32 pairs of observed data in 2011–2020 showed that it was most appropriate to outline the areas where the diurnal variation (the standard deviation calculated from the daily chlorophyll a) in chlorophyll a was more than 2.2 mg/m3. Among them, 30 pairs of data showed that the high chlorophyll a diurnal variation could predict the growth of the algal bloom in the next days. In these events, the median area difference between the two spatial distributions was -0.08%. When there was a high diurnal variation in chlorophyll a in the area adjacent to where algal bloom was occurred, a new algal bloom region was likely to spread in subsequent days. Continuous multiday time series showed that the diurnal variation in chlorophyll a can reflect the algal bloom’s overall growth condition.

Combined Earth observations reveal the sequence of conditions leading to a large algal bloom in Lake Geneva

Freshwater algae exhibit complex dynamics, particularly in meso-oligotrophic lakes with sudden and dramatic increases in algal biomass following long periods of low background concentration. While the fundamental prerequisites for algal blooms, namely light and nutrient availability, are well-known, their specific causation involves an intricate chain of conditions. Here we examine a recent massive Uroglena bloom in Lake Geneva (Switzerland/France). We show that a certain sequence of meteorological conditions triggered this specific algal bloom event: heavy rainfall promoting excessive organic matter and nutrients loading, followed by wind-induced coastal upwelling, and a prolonged period of warm, calm weather. The combination of satellite remote sensing, in-situ measurements, ad-hoc biogeochemical analyses, and three-dimensional modeling proved invaluable in unraveling the complex dynamics of algal blooms highlighting the substantial role of littoral-pelagic connectivities in large low-nutrient lakes. These findings underscore the advantages of state-of-the-art multidisciplinary approaches for an improved understanding of dynamic systems as a whole.

Improved data assimilation for algal bloom dynamics simulation in the Three Gorges Reservoir using particle filter

Algal blooms have been increasingly prevalent in recent years, especially in lakes and reservoirs; their accurate prediction is essential for preserving water quality. In this study, the observed chlorophyll a (chl-a) levels were assimilated into the Environmental Fluid Dynamics Code (EFDC) of algal bloom dynamics by using a particle filter (PF), and the state variables of water quality and model parameters were simultaneously updated to achieve enhanced algal bloom predictive performance. The developed data assimilation system for algal blooms was applied to Xiangxi Bay (XXB) in the Three Gorges Reservoir (TGR). The results show that the ensemble mean accuracy and reliability of the confidence intervals of the predicted state variables, including chl-a and indirectly updated phosphate (PO4), ammonium (NH4), and nitrate (NO3) levels, were considerably improved after PF assimilation. Thus, PF assimilation is an effective tool for the dynamic correction of parameters to represent their inherent variations. Increased assimilation frequency can effectively suppress the accumulation of model errors; therefore, the use of high-frequency water quality data for assimilation is recommended to ensure more accurate and reliable algal bloom prediction.

Effects of typhoon events on coastal hydrology, nutrients, and algal bloom dynamics: Insights from continuous observation and machine learning in semi-enclosed Zhanjiang Bay, China

Typhoons can induce variations in hydrodynamic conditions and biogeochemical processes, potentially escalating the risk of algal bloom occurrences impacting coastal ecosystems. However, the impacts of typhoons on instantaneous changes and the mechanisms behind typhoon-induced algal blooms remain poorly understood. This study utilized high-frequency in situ observation and machine learning model to track the dynamic variations in meteorological, hydrological, physicochemical, and Chlorophyll-a (Chl-a) levels through the complete Typhoon Talim landing in Zhanjiang Bay (ZJB) in July 2023. The results showed that a delayed onset of algal bloom occurring 10 days after typhoon's arrival. Subsequently, as temperatures reached a suitable range, with an ample supply of nutrients and water stability, Chl-a peaked at 121.49 μg L−1 in algal bloom period. Additionally, water temperature and air temperature decreased by 1.61 °C and 2.8 °C during the typhoon, respectively. In addition, wind speed and flow speed increased by 1.34 and 0.015 m s−1 h−1 to peak values, respectively. Moreover, the slow decline of 8.2 % in salinity suggested a substantial freshwater input, leading to an increase in nutrients. For instance, the mean DIN and DIP were 2.2 and 8.5 times higher than those of the pre-typhoon period, resulting in a decrease in DIN/DIP (closer to16) and the alleviation of P limitation. Furthermore, pH and dissolved oxygen (DO) were both low during the typhoon period and then peaked at 8.93 and 19.05 mg L−1 during the algal bloom period, respectively, but subsequently decreased, remaining lower than those of the pre-typhoon period. A preliminary learning machine model was established to predict Chl-a and exhibited good accuracy, with R2 of 0.73. This study revealed the mechanisms of eutrophication status formation and algal blooms occurrence in the coastal waters, providing insights into the effects of typhoon events on tropical coastal biogeochemistry and ecology.

The role of zooplankton in the growth of algal bloom: a mathematical study

Here a nonlinear mathematical model for an aquatic ecosystem containing both non-toxic and toxin-producing algal blooms is formulated and analyzed. The presence of harmful algae impacts the aquatic population, subsequently affecting the human population that relies on zooplankton (fish, crabs, etc.). It also deteriorates the water quality, and it is advisable not to use water from lakes containing harmful algae. Here, our focus is to understand the dynamics of aquatic ecosystems when we have both types of algae, one that is non-toxic and acts as food to zooplankton, and another that is toxin-producing algae that causes death to zooplankton. The predation of zooplankton on algae influences the growth of the algae population. First, we consider a deterministic model, and later, this model is extended to stochastic model to check whether it has any impact on the dynamics of the populations under consideration. Various equilibria of the proposed model are derived, and the local stability of these equilibria is thoroughly examined. The existence of Hopf-bifurcation is also demonstrated for a suitable set of parameters. The results of deterministic and stochastic models are compared using numerical simulation. Upon examining our results and simulation data, it is evident that modifying specific key parameters, such as the growth rate of non-toxic algae due to nutrient intake and the parameter associated with the predation of non-toxic algae by zooplankton, has a substantial impact on algal bloom dynamics. Minor adjustments to these parameters can result in an augmented presence of non-toxic algae and a simultaneous decrease in toxin-producing algae, aligning with the primary objectives set by policymakers.

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.

Three-dimensional augmentation for hyperspectral image data of water quality: An Integrated approach using machine learning and numerical models

This research introduces a comprehensive methodology to enhance hyperspectral image data (HSD) utility, specifically focusing on the three-dimensional (3-D) augmentation of Chlorophyll-a (Chl-a). This study comprises three significant steps: (1) the augmentation of limited field water quality data in terms of time interval and number of variables using neural network models, (2) the generation of 3-D data using numerical models, and (3) the extension of the hyperspectral image data into 3-D data using machine learning models. In the first phase, Multilayer Perceptron (MLP) models were developed to train water quality interactions and successfully generated high-frequency water quality data by adjusting biased measurements and predicting detailed water quality variables. In the second phase, high-frequency data generated by MLP models were applied to develop two numerical models. These numerical models successfully generated 3-D data, thereby demonstrating the effectiveness of integrating numerical modeling with neural networks. In the final phase, ten machine learning models were trained to generate 3-D Chl-a data from HSD. Notably, the Gaussian Process Regression model exhibited superior performance, effectively estimating 3-D Chl-a data with robust accuracy, as evidenced by an R-square value of 0.99. The findings align with theories of algal bloom dynamics, further validating the effectiveness of the approach. This study demonstrated the successful integrated development for HSD extension using machine learning models, numerical models, and original HSD, highlighting the potential of such integrated methodologies in advancing water quality monitoring and estimation. Notably, the approach leverages readily accessible data, allowing for the swift generation of results and bypassing time-consuming data collection processes. This research marks a significant step towards more robust, comprehensive water quality monitoring and prediction, thereby facilitating better management of aquatic ecosystems.

Exposure to the mixotrophic dinoflagellate Lepidodinium sp. and its cues increase toxin production of Pseudo-nitzschia multiseries

The present study examined the defense responses of toxigenic Pseudo-nitzschia species (P. multiseries) to a mixotrophic dinoflagellate, Lepidodinium sp., and its associated cues. We evaluated their responses to different predation risks, including direct physical contact and indirect interactions facilitated by cues from Lepidodinium sp. during active feeding on heterospecific prey (Rhodonomas salina), limited feeding on conspecific prey (P. multiseries) and non-feeding (autotrophic growth in f/2 medium) states. This study is the first investigation of these trophic interactions. Our results demonstrated a significant increase in cellular domoic acid (cDA) in P. multiseries when exposed to Lepidodinium sp. and its associated cues, which was 1.38 to 2.42 times higher than the non-induced group. Notably, this increase was observed regardless of Lepidodinium sp. feeding on this toxic diatom and nutritional modes. However, the most significant increase occurred when they directly interacted. These findings suggest that P. multiseries evaluates predation risk and increases cDA production as a defensive strategy against potential grazing threats. No morphological changes were observed in P. multiseries in response to Lepidodinium sp. or its cues. P. multiseries cultured in flasks of Group L+P-P showed a decrease in growth, but Group L-P and Group L+R-P did not exhibit any decrease. These results suggest a lack of consistent trade-offs between the defense response and growth, thus an increase in cDA production may be a sustainable and efficient defense strategy for P. multiseries. Furthermore, our findings indicate that P. multiseries had no significant impact on the fitness (cell size, growth and/or grazing) of Lepidodinium sp. and R. salina, which suggests no evident toxic or allelopathic impacts on these two phytoplankton species. This study enhances our understanding of the trophic interactions between toxic diatoms and mixotrophic dinoflagellates and helps elucidate the dynamics of Harmful Algal Blooms, toxin transmission, and their impact on ecosystem health.

Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models

Highlights The monitoring of HABs can be improved using ML models for chlorophyll-a prediction. ML model selection for HABs monitoring depends on target objectives. Random forest model predicts chlorophyll-a better when the temporal dimension is not considered. The LSTM model is essential for making time-dependent chlorophyll-a predictions for HABs monitoring. Abstract. The complex dynamics of freshwater harmful algal blooms (HABs) necessitate proactive monitoring approaches to mitigate their impacts. The rapid breakthrough in computing prowess and statistical advances is triggering the development of data-driven techniques such as machine learning (ML) models, which have been shown in different fields to be instrumental in finding patterns for explaining relationships in observed data. This study assesses the ability of ML models for HABs monitoring in a lake using chlorophyll-a concentration as the index. The selected models for this study were regression tree, random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), long short-term memory (LSTM), and gated recurrent unit (GRU) models, with the last two models able to consider the temporal sequence of obtained water quality datasets. The results showed that the RF model with R2, mean absolute error (MAE), and root mean square error (RMSE) of 0.87 µgL-1, 0.97 µgL-1, and 3.53 µgL-1, respectively, outperformed the SVR, MLP, and regression tree models. LSTM model with MAE and RMSE of 2.39 µgL-1 and 3.29 µgL-1, respectively, predicted temporal dynamics of chlorophyll-a better than GRU, although with more runtime, and showed the potential for developing real-time HAB monitoring and early warning systems. The findings reveal the robustness of the chosen ML models, thereby shedding light on crucial factors that necessitate careful deliberation by researchers and policymakers in determining the most suitable approaches for monitoring HABs. Keywords: Cyanobacteria, Early warning systems, Freshwater, HABs, Machine learning models.

Ecological niches of bloom-forming cyanobacteria in brackish Baltic Sea coastal waters

Cyanobacterial blooms occur regularly during summer and fall in various freshwater and brackish environments worldwide causing harm to aquatic flora, fauna, and water quality in natural ecosystems as well as in aquaculture. They also interfere with industrial fisheries, tourism, and human health. Meanwhile, up to date limited knowledge exists about these detrimental phenomena in brackish waters, and patterns of their expansion across the freshwater-to-marine continuum remain unclear. In this study, we analyzed long-term (1972–2016) dynamics of cyanobacterial harmful algal blooms (cyanoHABs) in the brackish southern Baltic Sea coastal waters, refined the cyanoHAB criteria, and for the first time measured ecological niches of the dominant bloom-forming cyanobacteria. The ecological niches of selected cyanoHAB species were determined considering a set of abiotic parameters as niche dimensions: water temperature, salinity, and concentrations of basic nutrients (PO43−, SiO44−, dissolved inorganic nitrogen – DIN, total phosphorus – TP, total nitrogen – TN, and TN/TP ratio), with special emphasis on salinity. We hypothesized that in highly variable brackish environments, the cyanobacterial species with broader ecological niches occur and bloom more often compared to those with narrower niches. For testing this hypothesis, we evaluated the effects of ecological niche width on the occurrence of the dominant cyanoHAB species and frequency of their blooms. The results revealed a pronounced statistically significant (p < 0.05) positive correlation between salinity-niche width and frequency of cyanoHABs. Moreover, the occurrence of cyanoHAB species with wider DIN and PO43− concentration-niches was substantially higher than that of the species with narrower nutrient-concentration niches. The effect of temperature-niche width on the occurrence of cyanoHAB species, on the contrary, was negligible and statistically insignificant. The latter result concurs with the fact that most of these cyanobacteria are highly thermophilic: on average, they bloom at temperatures above 21 °C, and their realized temperature niches in the southern Baltic coastal waters are relatively narrow.

Green algae outcompete cyanobacteria in a shallow lake, Longhu Lake

Abstract Green algae are natural competitors of cyanobacteria, but we still do not know why green algae have a competitive advantage in shallow lakes. In this study, we used qPCR to quantify and monitor green algae and cyanobacteria in Longhu Lake. Our results showed that green algae were dominant in Longhu Lake, accounting for 71.80–80.31%. The temporal and spatial dynamics of green algal blooms were consistent with that of total organic nitrogen (TON), indicating that organic nitrogen may be the key trigger of green algal blooms. Nitrogen and phosphorus were excessive, and the peak of ammonia nitrogen occurred during the blooms, implying that ammonia nitrogen may be one of the important factors stimulating green algal blooms. Spearman correlation analysis and RDA analysis showed that green algae and cyanobacteria were positively correlated with water temperature, TON, and ammonia nitrogen, indicating that they have similar favorable growth conditions in Longhu Lake. Our results indicated that the combined effects of elevated water temperature, excessive nitrogen and phosphorus, non-stratification, and short water retention time could favor the competitive dominance of green algae in Longhu Lake. The findings here improve our understanding of the competition between green algae and cyanobacteria in shallow lakes.

Horizontal and vertical migration of cyanobacterial blooms in two eutrophic lakes observed from the GOCI satellite

Under the variations of natural conditions (temperature, wind speed, light, et al.) and self-regulation of buoyancy, cyanobacterial blooms can change rapidly in a short time. The Geostationary Ocean Color Imager (GOCI) can provide hourly monitoring of the dynamics of algal blooms (eight times per day), and has potential in observing the horizontal and vertical movement of cyanobacterial blooms. Based on the fractional floating algae cover (FAC), the diurnal dynamics and migration of floating algal blooms were evaluated, and the horizontal and vertical migration speed of phytoplankton was estimated from the proposed algorithm in two eutrophic lakes, Lake Taihu and Lake Chaohu in China. The locations, number, and area of algal bloom patches showed the hotspots and horizontal movement of bloom patches. The spatial and seasonal variations of the vertical velocities indicated that both the rising and sinking speed were higher in summer and autumn than those in spring and winter. The factors affecting diurnal horizontal and vertical migrations of phytoplankton were analyzed. Diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), and temperature had significant positive relationships with FAC in the morning. Wind speed contributed 18.3 and 15.1% to the horizontal movement speed in Lake Taihu and Lake Chaohu, respectively. The rising speed was more related to DNI and DHI in Lake Taihu and Lake Chaohu with contribution of 18.1 and 16.6%. The horizontal and vertical movement of algae provide important information for understanding phytoplankton dynamics and the prediction and warning of algal blooms in lake management.