Outbreaks Of Harmful Algal Blooms Research Articles

Resting Cysts of the Toxic Dinoflagellate Gymnodinium catenatum (Dinophyceae) Ubiquitously Distribute along the Entire Coast of China, with Higher Abundance in Bloom-Prone Areas

Blooms of Gymnodinium catenatum have occurred occasionally in different areas of China and caused tremendous economic loss and even threatened human health. Not only is G. catenatum an important harmful-algal-bloom (HAB)-causing species, but also the only gymnodinioid dinoflagellate known to produce paralytic shellfish poisoning toxins (PSTs). Due to the germination of resting cysts, which often initiates blooms, the distribution and abundance of cysts in sediments and particularly the confirmation of cyst beds are important information for understanding and predicting dinoflagellate blooms. In this research, 199 sediment samples were collected from China’s coastal seas, ranging from the Beidaihe in the Bohai Sea (BS) to the southernmost sample from the Nansha Islands of the South China Sea (SCS). TaqMan quantitative PCR (qPCR) assays with species-specific primers and probes were developed to specifically detect the distribution and abundance of cysts in the 199 samples. The detection revealed that G. catenatum cysts were widely present in the sediments (126 of the 199 samples), with 93.55%, 74.65%, 42.37%, and 50% of the samples detected positively from the BS, YS, ECS and SCS, respectively, and covering the vast sea area from Nansha Islands to the Beidaihe area. The single-cyst morpho-molecular identification in the samples from Beidaihe confirmed the existence of G. catenatum cysts in the BS, and the positive detections of G. catenatum cysts using the qPCR methods. While G. catenatum cysts were widely distributed in all four seas of China, the average abundance was relatively low (1.0 cyst per gram of wet sediment). Three samples from the East China Sea (ECS), however, contained G. catenatum cysts at a relatively higher level (23 cysts g−1 wet sediment) than other sea areas, suggesting a pertinence of cyst abundance to the frequent occurrences of G. catenatum blooms in the area during recent years. Collectively, for G. catenatum being such an important toxic and HAB-causing species globally, the ubiquitous distribution of its cysts along the coastal waters of China and higher abundance in the bloom-prone areas warns us of a risk that cyst beds, although currently low in abundance, may seed HABs in any and many sea areas of China at any forthcoming year, and particularly those areas with records of frequent HABs outbreaks in the past.

Salinity decline promotes growth and harmful blooms of a toxic alga by diverting carbon flow.

Global climate change intensifies the water cycle and makes freshest waters become fresher and vice-versa. But how this change impacts phytoplankton in coastal, particularly harmful algal blooms (HABs), remains poorly understood. Here, we monitored a coastal bay for a decade and found a significant correlation between salinity decline and the increase of Karenia mikimotoi blooms. To examine the physiological linkage between salinity decreases and K. mikimotoi blooms, we compare chemical, physiological and multi-omic profiles of this species in laboratory cultures under high (33) and low (25) salinities. Under low salinity, photosynthetic efficiency and capacity as well as growth rate and cellular protein content were significantly higher than that under high salinity. More strikingly, the omics data show that low salinity activated the glyoxylate shunt to bypass the decarboxylation reaction in the tricarboxylic acid cycle, hence redirecting carbon from CO2 release to biosynthesis. Furthermore, the enhanced glyoxylate cycle could promote hydrogen peroxide metabolism, consistent with the detected decrease in reactive oxygen species. These findings suggest that salinity declines can reprogram metabolism to enhance cell proliferation, thus promoting bloom formation in HAB species like K. mikimotoi, which has important ecological implications for future climate-driven salinity declines in the coastal ocean with respect to HAB outbreaks.

The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system

In recent years, there have been frequent outbreaks of harmful algal blooms (HAB) in coastal areas, which have caused serious economic losses to the local community. Therefore, accurate and rapid prediction of microalgal concentrations is necessary for early warning and countermeasures before the occurrence of HAB. This paper presents a model for predicting microalgae concentration based on unthresholded recurrence plots (UTRPs) combined with an improved broad learning system (BLS). Spectral data acquisition of algae species at different concentrations using l-induced fluorescence spectroscopy. Then, the 1D spectral data are dimensionally lifted by UTRPs transformation, recurrence plots (RPs) transformation can fully extract the internal information of 1D sequence data, and at the same time, UTRPs avoids the influence of artificially selected thresholds on the feature transformation results of traditional (RPs). Finally, a lightweight flat network BLS was used for microalgae concentration regression prediction, at the same time the BLS regularization method was improved. When comparing the two most commonly used deep learning regression models and integrated learning models, UTRPs and elastic net (UTRPs-ENBLS) achieves convincing results.

Harmful algal blooms (HAB) open issues: A review of ecological data challenges, factor analysis and prediction approaches using data-driven method

Ongoing research on the temporal and spatial distribution of algae ecological data has caused intricacies entailing incomprehensible data, model overfit, and inaccurate algal bloom prediction. Relevant scholars have integrated past historical data with machine learning (ML) and deep learning (DL) approaches to forecast the advent of harmful algal blooms (HAB) following successful data-driven techniques. As potential HAB outbreaks could be predicted through time-series forecasting (TSF) to gauge future events of interest, this research aimed to holistically review field-based complexities, influencing factors, and algal growth prediction trends and analyses with or without the time-series approach. It is deemed pivotal to examine algal growth factors for useful insights into the growth of algal blooms. Multiple open issues concerning indicator types and numbers, feature selection (FS) methods, ML and DL forms, and the time series-DL integration were duly highlighted. This algal growth prediction review corresponded to various (chronologically-sequenced) past studies with the algal ecology domain established as a reference directory. As a valuable resource for beginners to internalize the algae ecological informatics research patterns and scholars to optimize current prediction techniques, this study outlined the (i) aforementioned open issues with an end-to-end (E2E) evaluation process ranging from FS to predictive model performance and (ii) potential alternatives to bridge the literature gaps.

A photocatalytic-microbial coupling system for simultaneous removal of harmful algae and enhanced denitrification: Construction, performance and mechanism of action

Recently, harmful algal blooms (HABs) have become occurred with increasingly frequency worldwide. High nitrate content is one of the primary causes of eutrophication. Research has shown that photocatalytic materials enhance the effectiveness of microbial denitrification while removing other contaminants, despite some shortcomings. Based on this, we loaded TiO2/C3N4 heterojunctions onto weaveable, flexible carbon fibers and established a novel photocatalytically enhanced microbial denitrification system for the simultaneous removal of harmful algae and Microcystin-LR. We found that 99.35% of Microcystis aeruginosa and 95.34% of MC-LR were simultaneously and effectively removed. Compared to existing denitrification systems, the nitrate removal capacity improved by 72.33%. The denitrifying enzyme activity and electron transport system activity of microorganisms were enhanced by 3.54–3.86 times. Furthermore, the microbial community structure was optimized by the regulation of photogenerated electrons, and the relative abundance of main denitrifying bacteria increased from 50.72% to 66.45%, including Proteobacteria and Bacteroidetes. More importantly, we found that the increased secretion of extracellular polymeric substances by microorganisms may be responsible for the persistence of the reinforcing effect caused by photogenerated electrons in darkness. The higher removal of Microcystis aeruginosa and Microcystin-LR (MC-LR) achieved by the proposed system would reduce the frequency of HAB outbreaks and prevent the associated secondary pollution.

Short-term Lake Erie algal bloom prediction by classification and regression models

The recent outbreaks of harmful algal blooms in the western Lake Erie Basin (WLEB) have drawn tremendous attention to bloom prediction for better control and management. Many weekly to annual bloom prediction models have been reported, but they only employ small datasets, have limited types of input features, build linear regression or probabilistic models, or require complex process-based computations. To address these limitations, we conducted a comprehensive literature review, complied a large dataset containing chlorophyll-a index (from 2002 to 2019) as the output and a novel combination of riverine (the Maumee & Detroit Rivers) and meteorological (WLEB) features as the input, and built machine learning-based classification and regression models for 10-d scale bloom predictions. By analyzing the feature importance, we identified 8 most important features for the HAB control, including nitrogen loads, time, water levels, soluble reactive phosphorus load, and solar irradiance. Here, both long- and short-term nitrogen loads were for the first time considered in HAB models for Lake Erie. Based on these features, the 2-, 3-, and 4-level random forest classification models achieved an accuracy of 89.6%, 77.0%, and 66.7%, respectively, and the regression model achieved an R2 value of 0.69. In addition, long-short term memory (LSTM) was implemented to predict temporal trends of four short-term features (N, solar irradiance, and two water levels) and achieved the Nash-Sutcliffe efficiency of 0.12–0.97. Feeding the LSTM model predictions for these features into the 2-level classification model reached an accuracy of 86.0% for predicting the HABs in 2017–2018, suggesting that we can provide short-term HAB forecasts even when the feature values are not available.

A singular value decomposition approach for detecting and delineating harmful algal blooms in the Red Sea

Harmful algal blooms (HABs) have adverse effects on marine ecosystems. An effective approach for detecting, monitoring, and eventually predicting the occurrences of such events is required. By combining a singular value decomposition (SVD) approach and satellite remote sensing observations, we propose a remote sensing algorithm to detect and delineate species-specific HABs. We implemented and tested the proposed SVD algorithm to detect HABs associated with the mixed assemblages of different phytoplankton functional type (PFT) groupings in the Red Sea. The results were validated with concurrent in-situ data from surface samples, demonstrating that the SVD-model performs remarkably well at detecting and distinguishing HAB species in the Red Sea basin. The proposed SVD-model offers a cost-effective tool for implementing an automated remote-sensing monitoring system for detecting HAB species in the basin. Such a monitoring system could be used for predicting HAB outbreaks based on near real-time measurements, essential to support aquaculture industries, desalination plants, tourism, and public health.

A combined approach detected novel species diversity and distribution of dinoflagellate cysts in the Yellow Sea, China

Resting cysts of dinoflagellates seed harmful algal blooms (HABs) and their geographic expansion, which makes it fundamentally important to obtain comprehensive inventories of dinoflagellate resting cysts in HABs-prone regions. The Yellow Sea (YS) of China has observed numerous outbreaks of dinoflagellate HABs with some novel species recorded recently indicating an underestimated HABs-causing species diversity. We report our investigation of dinoflagellate cysts of YS via an approach combining metabarcoding sequencing and single-cyst morpho-molecular identification, which identified many novel cyst species and a significant controlling effect of the Yellow Sea Cold Water Mass on cyst composition. The metabarcoding and single cyst-based sequencing detected 11 cyst species never being unambiguously reported in China, 10 never reported as cyst producers, and 3 HABs-causing species never reported from YS. Our detections of many potentially toxic or HABs-causative, particularly novel, cysts and distribution pattern provide important insights into the risks and ecology of dinoflagellate HABs.

Green algae monitoring via ground-based GNSS-R observations

Outbreaks of harmful algal blooms (HABs) exhibit high frequency, large range and damage aggravation characteristics, but existing monitoring methods, such as artificial and optical near-infrared remote sensing, cannot accommodate these characteristics. We propose a new method for monitoring green algae using Global Navigation Satellite System Reflectometry (GNSS-R) observations. The basic principle states that changes in the seawater dielectric constant and sea surface roughness due to the emergence of green algae lead to an increase in brightness temperature, which can be inverted based on the reflection time delay waveform. Shipboard reflection waveform data collected during an Enteromorpha prolifera outbreak in the Qingdao sea area were used for model development and validation of the detection and estimation performance. The results indicated that the root mean square error of GNSS-R-based inversion of the green algae density was 6.74%, indicating the potential of GNSS-R technology for rapid preliminary monitoring of green algae. Moreover, the advantages of a low cost, short return time and no climatic limitations support GNSS-R technology as a new and efficient means of green algae monitoring.

Inorganic Nitrogen Uptake Characteristics of Three Typical Bloom-Forming Algae in the East China Sea

Inorganic nitrogen (N) is an important element for eutrophication and harmful algal bloom (HAB) formation. However, the roles of inorganic N in HAB outbreaks are still unclear. Here, we compared the affinities and abilities for inorganic N uptake and assimilation among three typical bloom-forming algae in the East China Sea (ECS), Skeletonema costatum, Prorocentrum donghaiense and Alexandrium pacificum by investigating the uptake and enzymatic (nitrate reductase (NR) and glutamine synthetase (GS) kinetics for nitrate and ammonia. The Ks of nitrate and ammonium in S. costatum was lower than those in P. donghaiense and A. pacificum. The NR activity of S. costatum and P. donghaiense exhibited a positive relationship with the nitrate concentration, and NR activity of S. costatum was nearly 4-fold higher than that of P. donghaiense at high nitrate concentration. However, the NR activity of A. pacificum could not be detected. The GS activity of three species decreased with the increase of ammonium concentrations, and the highest GS activity was detected in A. pacificum. S. costatum presented the highest affinity for nitrate and ammonium, followed by P. donghaiense and A. pacificum. Moreover, P. donghaiense exhibited the highest affinity for intracellular ammonium. Our results characterized the differences in inorganic nitrogen uptake among the three typical bloom-forming algae, which may contribute to the formation of blooms in the coastal waters of the ECS.

A novel algicidal bacteria isolated from native snail lived in Taihu Lake against algal blooms: identification, degradation kinetic, and algicidal mechanism.

Harmful algal blooms (HABs) impacted negatively the water ecosystem, and produced toxic microcystins that poses toxic effect on liver, nervous, and genital system. The introduction of useful and adaptive algae-degrading microbes or bio-augmentation can be regarded as an efficient way to inhibit the outbreak of HABs. The purpose of this study is to evaluate the application potential of algicidal bacteria named XMC, which is isolated from native snails. Response surface methodology (RSM) experiments showed that self-characteristic and various external conditions affected the actual algae inhibition ability of XMC. In particular, actual algicidal efficiency was strongly depend on the temperature and growth stage of XMC, and the maximum algicidal rate could reach 93.95% within 7days. The degradation curve of Microcystis aeruginosa was compliant with the first-order kinetic model, which could be used to predict the degradation effect of Microcystis aeruginosa in engineering applications. The analysis results of algae dissolution products showed that algicidal bacteria XMC had both direct and indirect algicidal capacity. In addition, XMC had strong algicidal ability and greater environmental adaptability, and its algae dissolution products were environmentally friendly. All results indicated that XMC had the potential to be used in the bio-degradation of cyanobacteria bloom.

Comparative Study of Algal Responses and Adaptation Capability to Ultraviolet Radiation with Different Nutrient Regimes.

Frequent outbreaks of harmful algal blooms (HABs) represent one of the most serious outcomes of eutrophication, and light radiation plays a critical role in the succession of species. Therefore, a better understanding of the impact of light radiation is essential for mitigating HABs. In this study, Chlorella pyrenoidosa and non-toxic and toxic Microcystis aeruginosa were mono-cultured and co-cultured to explore algal responses under different nutrient regimes. Comparisons were made according to photosynthetically active radiation (PAR), UV-B radiation exerted oxidative stresses, and negative effects on the photosynthesis and growth of three species under normal growth conditions, and algal adaptive responses included extracellular polymeric substance (EPS) production, the regulation of superoxide dismutase (SOD) activity, photosynthetic pigments synthesis, etc. Three species had strain-specific responses to UV-B radiation and toxic M. aeruginosa was more tolerant and showed a higher adaptation capability to UV-B in the mono-cultures, including the lower sensitivity and better self-repair efficiency. In addition to stable μmax in PAR ad UV-B treatments, higher EPS production and enhanced production of photosynthetic pigments under UV-B radiation, toxic M. aeruginosa showed a better recovery of its photosynthetic efficiency. Nutrient enrichment alleviated the negative effects of UV-B radiation on three species, and the growth of toxic M. aeruginosa was comparable between PAR and UV-B treatment. In the co-cultures with nutrient enrichment, M. aeruginosa gradually outcompeted C. pyrenoidosa in the PAR treatment and UV-B treatment enhanced the growth advantages of M. aeruginosa, when toxic M. aeruginosa showed a greater competitiveness. Overall, our study indicated the adaptation of typical algal species to ambient UV-B radiation and the stronger competitive ability of toxic M. aeruginosa in the UV-radiated waters with severer eutrophication.

Advances in multiplex molecular detection technologies for harmful algae.

As the eutrophication of natural water bodies becomes more and more serious, the frequency of outbreaks of harmful algal blooms (HABs) mainly formed by harmful algae also increases. HABs have become a global ecological problem that poses a serious threat to human health and food safety. Therefore, it is extremely important to establish methods that can rapidly detect harmful algal species for early warning of HABs. The traditional morphology-based identification method is inefficient and inaccurate. In recent years, the rapid development of molecular biology techniques has provided new ideas for the detection of harmful algae and has become a research hotspot. The current molecular detection methods for harmful algal species mainly include fluorescence in situ hybridization, sandwich hybridization, and quantitative PCR (qPCR), but all of these methods can only detect single harmful algal species at a time. The establishment of methods for the simultaneous detection of multiple harmful algal species has become a new trend in the development of molecular detection technology because various harmful algal species may coexist in the natural water environment. The established molecular techniques for multiple detections of harmful algae mainly include gene chip, multiplex PCR, multiplex qPCR, massively parallel sequencing, antibody chip, and multiple isothermal amplification. This review mainly focuses on the principles, advantages and disadvantages, application progress, and application prospects of these multiple detection technologies, aiming at providing effective references not only for the fisheries but also for economic activities, environment, and human health.

Characteristics of Harmful Algal Species in the Coastal Waters of China from 1990 to 2017.

Harmful algal blooms (HABs) have occurred frequently in coastal waters of China, imposing negative effects on the marine ecological environment. A dataset of HABs and terrestrial runoff was collected and analyzed in this study, and factors responsible for HABs were further explored. Frequency and expansion of HABs peaked between 2001 and 2007, and although they have declined slightly since then, they have remained quite high. Frequency and accumulative area of HABs peaked in 2004–2005, and most occurred from April to August during these years. HABs occurred frequently in the Changjiang (Yangtze River) estuary, and Prorocentrum donghaiense, Noctiluca scientillans, Karenia mikimotoi, and Skeletonema costatum were the main algal species. The increases of eutrophication, the abnormal sea surface temperature caused by climate and ocean currents, and the species invasion caused by the discharge of ballast water may be important factors for the long-term outbreak of HABs in the Chinese coastal waters. These findings provide a better understanding of HABs in China, which will be helpful to further prevention and control.

Antibacterial activity of γFe2 O3 /TiO2 nanoparticles on toxic cyanobacteria from a lake in Southern Illinois.

Frequent outbreaks of harmful algal blooms (HABs) have brought adverse impacts on human health, economic viability, and recreational activities in many communities in the United States. Cyanobacteria (or blue-green algae) blooms are the most common type of HABs in surface water. Current bactericides for controlling the blooms are disadvantageous due to the recycling difficulty. In this study, an innovative magnetic nanomaterial-γFe2 O3 /TiO2 nanoparticle-was used to inactivate toxic cyanobacteria species found in a lake in Southern Illinois that frequently experienced HABs. Cyanotoxin genes of mcy, nda, cyr, and sxt were used for targeting microcystin-, nodularin-, cylindrospermopsin-, and saxitoxin-producing cyanobacteria, respectively, by quantitative polymerase chain reaction (PCR) method. It was found that the concentration of chlorophyll a presents a strong correlation (R2 = 0.6024) with the gene copy obtained from 16S rRNA targeted for all cyanobacteria, but not with that from individual toxigenic cyanobacteria. The inactivation efficiencies of the nanomaterials under visible light were as high as 5-log and 1-log for cyanobacteria species containing mcyE/ndaF and sxtA genes, respectively, an improvement over the treatment under darkness. These nanomaterials can be recycled by their magnetic properties for reuse. Communities susceptible to HAB outbreaks are expected to benefit from the developed method for mitigating the blooms. PRACTITIONER POINTS: Lab-made γFe2 O3 /TiO2 nanoparticles can be used to inactivate microcystin/nodularin- and saxitoxin-producing cyanobacteria species. qPCR method can be used for targeting toxic cyanobacteria; Chl a cannot be used as a standalone indicator for HABs. Better inactivation efficiency under visible light indicated possible application of the technology under sunlight for HAB mitigation from surface water.

Transcriptional Responses of Flavin-Containing Monooxygenase Genes in Scallops Exposed to PST-Producing Dinoflagellates Implying Their Involvements in Detoxification

Flavin-containing monooxygenase (FMO) is one of the most prominent xenobiotic metabolic enzymes. It can catalyze the conversion of heteroatom-containing chemicals to polar, readily excretable metabolites and is considered an efficient detoxification system for xenobiotics. Bivalves can accumulate paralytic shellfish toxins (PSTs) produced by dinoflagellates, especially during outbreaks of harmful algal blooms. Exploring FMO genes in bivalves may contribute to a better understanding of the adaptation of these species and the mechanisms of PSTs bioavailability. Therefore, through genome screening, we examined the expansion of FMO genes in two scallops (Patinopecten yessoensis and Chlamys farreri) and found a new subfamily (FMO_like). Our expression analyses revealed that, in both scallops, members of the FMO_N-oxide and FMO_like subfamilies were mainly expressed from the D-stage larvae to juveniles, whereas the FMO_GS-OX subfamily genes were mainly expressed at and prior to the trochophore stage. In adult organs, higher expressions of FMOs were observed in the kidney and hepatopancreas than in other organs. After exposure to PST-producing algae, expression changes in FMOs occurred in hepatopancreas and kidney of both scallops, with more members being up-regulated in hepatopancreas than in kidney for Alexandrium catenella exposure, while more up-regulated FMOs were found in kidney than in hepatopancreas of C. farreri exposed to A. minutum. Our findings suggest the adaptive functional diversity of scallop FMO genes in coping with the toxicity of PST-producing algae.

Effect of Modified Clay on the Growth Dynamics and Physio—Biochemical Response of Newly Hatched Larvae of the Marine Medaka (Oryzias melastigma)

An outbreak of harmful algal blooms (HABs) often leads to the death of fish and other marine organisms and causes serious losses to human economic activities. Modified clay (MC) technology is an effective way to control HABs. Although the material and preparation process are based on the premise of green and safety, the potential eco-environmental impacts of MC on non-target organisms should still have to receive attention before field applications can occur. Therefore, the effects of one commonly used modified clay, polyaluminum chloride-modified clay (PAC-MC), on the survival, growth, and oxidative stress of the marine medaka (Oryzias melastigma) were studied. The toxicity test results showed that the 96-h median lethal concentration (96-h LC50) of PAC-MC for newly hatched medaka larvae was 5.204 g/L, which was much higher than the concentration used on site (4–10 t/km2). Within the concentration range of PAC-MC used in this experiment (≤2 g/L), the morphology, heart rate, growth, and aluminum content of larvae did not change with the increase in the modified clay concentration. Low concentrations of PAC-MC (≤0.5 g/L) did not significantly affect catalase (CAT) activity, superoxide dismutase (SOD) activity, peroxidase (POD) activity, and the content of malondialdehyde (MDA), but higher concentrations of PAC-MC (such as 2 g/L) caused oxidative damage to the larvae and increased the antioxidant enzyme activity of the larvae. The present study revealed that under an effective dosage for treating harmful algal blooms on site, PAC-MC had no adverse effects on the survival, growth, oxidative stress, and aluminum content of the newly hatched marine medaka, which provides a scientific basis for the field application of modified clay.

Predicting coastal algal blooms with environmental factors by machine learning methods

Harmful algal blooms are a major type of marine disaster that endangers the marine ecological environment and human health. Since the algal bloom is a complex nonlinear process with time characteristics, traditional statistical methods often cannot provide good predictions. In this paper, we propose a method based on machine learning with the aim to predict the occurrence of algal blooms by environmental parameters. The features related to algal bloom growth have been experimented for achieving a good prediction of algal concentrations by a combination strategy. We validate the prediction performance on two real datasets from two locations in US and China, i.e., Scripps Pier, California and Sishili Bay, Shandong, respectively. The models and feature subsets have been selected to complete the missing data and predict the phytoplankton concentration. The results demonstrate the efficiency of our method in the short-term prediction of concentrations by selecting appropriate features. The comparison studies prove the advantage of our developed machine learning method. The importance of every features for the prediction performance reveals crucial factors for the outbreak of harmful algal blooms.

Common fate of sister lakes in Hulunbuir Grassland: Long-term harmful algal bloom crisis from multi-source remote sensing insights

With the increasing impact of drought and anthropogenic c activities, middle and high latitude lakes are increasingly facing exacerbated eutrophication. The sister lakes Hulun Lake and Buir Lake, the former located in the Hulunbuir Grassland in northeast China and the latter shared by China and Mongolia, have been exposed to outbreaks of harmful algal blooms (HABs) of varying degrees. In this study, multi-source remote sensing technologies were utilized to explore the essential factors causing HAB occurrences in the Hulun Basin. First, a simplified adjusted floating algae index (AFAI) algorithm was applied to invert the HAB sequence from 1984 to 2019. The results show that large-scale HABs were detected in both lakes on or close to the same days in 1984, 1999, and 2013. From a long-term perspective, the HAB events of Hulun Lake are more serious and intractable than those of Buir Lake in terms of outbreak frequency, duration, and scale. Furthermore, the effects of meteorological and hydrological conditions were evaluated quantitatively. In situ sampling of the two lakes highlights the limiting effect of nutrients on the algae flooding in Buir Lake with perennial outflow. In contrast, Hulun Lake turned into an internal flow lake without outlets from 2000 to 2012 and the decreasing water volume stimulated the accumulation of total nitrogen (TN) and phosphorus (TP), with averages of 2.36 and 0.21 mg/L, respectively. Analysis of the comprehensive driving factors demonstrates that the level of exogenous nutrients caused a difference in the severity of the HAB event between the sister lakes, which are largely determined by drought and eco-unfriendly anthropogenic activities. Our work emphasizes the pivotal role of substantial increases of nutrient loads and stricter regulations are necessary to curb further deterioration of this situation.

Isolation and Identification of Two Algae-Lysing Bacteria against Microcystis aeruginosa

Algae blooms present an environmental problem worldwide. In response to the outbreak of harmful algal blooms in cyanobacteria, the role of biological control has drawn wide attention, particularly for algicidal bacteria. The mechanism underlying algicidal activity was determined in our study. Algae-lysing bacteria used were separated from water and sediment collected from the Fenhe scenic spot of Taiyuan. Genetic and molecular identification was conducted by polymerase chain reaction amplification based on 16S rDNA gene. These bacterial strains were identified as Raoultella planticola and Aeromonas sp. The algae-lysing characteristics were evaluated on Microcystis aeruginosa. For the two algicidal bacteria, the high inoculation ratio (>8%) of bacteria strains contributed to the lytic effect. M. aeruginosa could be completely removed by these strains at different cell ages. However, the time used decreased with an increase in cell age. The removal rate was increased while M. aeruginosa was in the lag and logarithmic phases. The earlier bacteria strains could be inoculated, the sooner all algae could be removed. Both algicidal substances were protein, which could destroy the photosynthetic systems and break the cell of M. aeruginosa. The algicidal bacteria strain has important theoretical and practical significance for economic and feasible algae removal and provides good germplasm resources and technical support for the control of cyanobacterial bloom.