Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation

Intensification of harmful cyanobacterial blooms in a eutrophic, temperate lake caused by nitrogen, temperature, and CO2

Warmer temperatures can significantly increase the intensity of cyanobacterial harmful algal blooms (CHABs) in eutrophic freshwater ecosystems. However, few studies have examined the effects of CO2 enrichment in tandem with elevated temperature and/or nutrients on cyanobacterial taxa in freshwater ecosystems. Here, we observed changes in the biomass of cyanobacteria, nutrients, pH, and carbonate chemistry over a two-year period in a shallow, eutrophic freshwater lake and performed experiments to examine the effects and co-effects of CO2, temperature, and nutrient enrichment on cyanobacterial and N2-fixing (diazotrophic) communities assessed via high throughput sequencing of the 16S rRNA and nifH genes, respectively. During both years, there were significant CHABs (50-500 μg cyanobacterial chlorophyll-a L-1) and lake CO2 levels were undersaturated (≤300 μatm pCO2). NH4+ significantly increased the net growth rates of cyanobacteria as well as the biomass of the diazotrophic cyanobacterial order Nostocales under elevated and ambient CO2 conditions. In a fall experiment, the N2 fixation rates of Nostocales were significantly higher when populations were enriched with CO2 and P, relative to CO2-enriched populations that were not amended with P. During a summer experiment, N2 fixation rates increased significantly under N and CO2 - enriched conditions relative to N-enriched and ambient CO2 conditions. Nostocales dominated the diazotrophic communities of both experiments, achieving the highest relative abundance under CO2-enriched conditions when N was added in the first experiment and when CO2 and temperature were elevated in the second experiment, when N2 fixation rates also increased significantly. Collectively, this study indicates that N promotes cyanobacterial blooms including those formed by Dolichospermum and that the biomass and N2 fixation rates of diazotrophic cyanobacterial taxa may benefit from enhanced CO2 levels in eutrophic lakes.

Predicting and analyzing the algal population dynamics of a grass-type lake with explainable machine learning

Algal blooms, exacerbated by climate change and eutrophication, have emerged as a global concern. In this study, we introduce a novel interpretable machine learning (ML) workflow tailored for investigating the dynamics of algal populations in grass-type lakes, Liangzi lake. Utilizing seven ML methods and incorporating the covariance matrix adaptation evolution strategy (CMA-ES), we predict algal density across three distinct time periods, resulting in the construction of a total of 30 ML models. The CMA-ES-CatBoost model consistently demonstrates superior predictive accuracy and generalization capability across these periods. Through the collective validation of various interpretable tools, we identify water temperature and permanganate index as the two most critical water quality parameters (WQIs) influencing algal density in Liangzi Lake. Additionally, we quantify the independent and interactive effects of WQIs on algal density, pinpointing key thresholds and trends. Furthermore, we determine the minimum combination of WQIs that achieves near-optimal predictive performance, striking a balance between accuracy and cost-effectiveness. These findings offer a scientific and economically efficient foundation for governmental agencies to formulate strategies for water quality management and sustainable development.

One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated water quality data collection effort in Iowa lakes, this study aimed to identify factors associated with hazardous microcystin levels and develop one-week-ahead predictive classification models. Using water samples from 38 Iowa lakes collected between 2018 and 2021, feature selection was conducted considering both linear and nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, and Logistic Regression) with different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, % developed, DOC, dewpoint temperature, and ortho-P). Evaluation metrics demonstrated the strong performance of the Neural Network with oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity 0.857, LR+ 5.993, and 1/LR- 5.993), as well as the XGBoost with downsampling (ROC-AUC 0.944, accuracy 0.831, sensitivity 0.928, specificity 0.833, LR+ 5.557, and 1/LR- 11.569). This study exhibited the intricacies of modeling with limited data and class imbalances, underscoring the importance of continuous monitoring and data collection to improve predictive accuracy. Also, the methodologies employed can serve as meaningful references for researchers tackling similar challenges in diverse environments.

Spatio-temporal dynamics of phytoplankton in a diversion reservoir and the major influencing factors: taxonomic versus functional groups classification

Identifying factors affecting phytoplankton dynamics is crucial to the management of aquatic ecosystems. A lot of scholars have conducted intensive studies on phytoplankton in lake or reservoirs, but not many studies have been conducted on diversion reservoirs. To explore the seasonal and spatial variation of phytoplankton communities and their relationship with environmental factors in the context of water diversion, a case study was carried out at XiKeng (XK) reservoir in South China. In this study, month-by-month water samples and phytoplankton were collected from this reservoir from December, 2021, to July, 2022. The results showed that the phytoplankton community was characterized by significant spatial and temporal variations. There were significant differences in phytoplankton abundance and structure in the reservoirs in terms of time. The abundance of phytoplankton cells and the proportion of Cyanobacteria in the reservoir showed a trend of increasing from autumn to spring and then decreasing from spring to summer, while the functional group evolved from S1 in autumn to SN in spring and summer. The abundance of phytoplankton was influenced by the dynamic water division and the characteristics of the reservoir itself, resulting in a spatial distribution characteristic of AIII > AII > AI. Water temperature (WT) and nutrients were the key factors driving the changes in phytoplankton abundance and community structure in the reservoir. These findings will deepen our understanding of the spatial and temporal dynamics of phytoplankton community structure in diversion reservoirs and provide a basis for freshwater water ecological management strategies.

Assessing the algal population dynamics using multiple machine learning approaches: Application to Macao reservoirs

The quality of reservoir water is important to the health and wellbeing of human and animals. Eutrophication is one of the most serious problems threatening the safety of reservoir water resource. Machine learning (ML) approaches are effective tools to understand and evaluate various environmental processes of concern, such as eutrophication. However, limited studies have compared the performances of different ML models to reveal algal dynamics using time-series data of redundant variables. In this study, the water quality data from two reservoirs in Macao were analyzed by adopting various ML approaches, including stepwise multiple linear regression (LR), principal component (PC)-LR, PC-artificial neuron network (ANN) and genetic algorithm (GA)-ANN-connective weight (CW) models. The influence of water quality parameters on algal growth and proliferation in two reservoirs was systematically investigated. The GA-ANN-CW model demonstrated the best performance in reducing the size of data and interpreting the algal population dynamics data, which displayed higher R-squared, lower mean absolute percentage error and lower root mean squared error values. Moreover, the variable contribution based on ML approaches suggest that water quality parameters, such as silica, phosphorus, nitrogen, and suspended solid have a direct impact on algal metabolisms in two reservoirs' water systems. This study can expand our capacity in adopting ML models in predicting algal population dynamics based on time-series data of redundant variables.

Does temperature favour the spread of Raphidiopsis raciborskii, an invasive bloom-forming cyanobacterium, by altering cellular trade-offs?

As a tropical filamentous cyanobacterium, Raphidiopsis raciborskii has attracted much attention due to its expansion and toxin production. However, the mechanisms of its expansion to temperate regions have not been studied in detail. To address the potential strategies, the physiological and metabolomic profiles of R. raciborskii FACHB 1096 isolated from a temperate lake in China were determined and measured at different temperatures (10 °C, 15 °C, 20 °C, 25 °C, and 32 °C). The results demonstrated that temperature significantly changed cell viability, chlorophyll a content, specific growth rate, Chl a fluorescence, and filamentous shape of R. raciborskii. Low temperature decreased cell viability, specific growth rate, and photosynthetic efficiency, while the proportion of akinete and carbon fixation per unit cell were significantly increased compared with high temperature (32 °C). A constructed unimodal model indicated that filament length, cell volume, and cell length/width of R. raciborskii were significantly reduced in both high and low temperature environments. Under low-temperature conditions, R. raciborskii suffered different degrees of oxidative damage and produced corresponding antioxidant substances to resist oxidative stress, suggesting that low temperature changes the metabolic level of the cells, causing the cells to gradually switch from development to defense. Metabolomic data further confirmed that temperature change induced shifts in metabolic pathways in R. raciborskii, including starch and sucrose metabolic pathways, glutathione metabolic pathways, and the pentose phosphate pathways (PPP), as well as metabolic pathways related to the tricarboxylic acid (TCA) cycle. Our results indicated that the trade-offs of R. raciborskii cells among the growth, cell size, and metabolites can be significantly regulated by temperature, with broad implications for its global expansion in temperate waterbodies.

Temporal heterogeneity of bacterial communities and their responses to Raphidiopsis raciborskii blooms

To elucidate the interspecies connectivity between cyanobacteria and other bacteria (noncyanobacteria), microbial diversity and composition were investigated through high-throughput sequencing (HTS) in a drinking water reservoir in Chongqing city, Southwest China, during Raphidiopsis raciborskii blooms. Significant temporal changes were observed in microbial community composition during the sampling period, primarily reflected by variations in relative bacterial abundance. The modularity analysis of the network demonstrated that the bacterial community forms co-occurrence/exclusion patterns in response to variations in environmental factors. Moreover, five modules involved in the dynamic phases of the R. raciborskii bloom were categorized into the Pre-Bloom, Bloom, Post-Bloom, and Non-Bloom Groups. The reservoir was eutrophic (i.e., the average concentrations of total nitrogen (TN) and total phosphorus (TP) were 2.32 and 0.07 mg L^-1, respectively) during the investigation; however, Pearson's correlation coefficient showed that R. raciborskii was not significantly correlated with nitrogen and phosphorus. However, other environmental factors, such as water temperature, pH, and the permanganate index, were positively correlated with R. raciborskii. Importantly, Proteobacteria (α-, γ-Proteobacteria), Acidobacteria, Chloroflexi, and Firmicutes were preferentially associated with increased R. raciborskii blooms. These results suggested that the transition of R. raciborskii bloom-related microbial modules and their keystone species could be crucial in the development and collapse of R. raciborskii blooms and could provide a fundamental basis for understanding the linkage between the structure and function of the microbial community during bloom dynamics.

Natural Cyanobacteria Removers Obtained from Bio-Waste Date-Palm Leaf Stalks and Black Alder Cone-Like Flowers

The impact of urbanization and modern agricultural practice has led to accelerated eutrophication of aquatic ecosystems, which has resulted in the massive development of cyanobacteria. Very often, in response to various environmental influences, cyanobacteria produce potentially carcinogenic cyanotoxins. Long-term human exposure to cyanotoxins, through drinking water as well as recreational water (i.e., rivers or lakes), can cause serious health consequences. In order to overcome this problem, this paper presents the synthesis of completely new activated carbons and their potential application in contaminated water treatment. The synthesis and characterization of new active carbon materials obtained from waste biomass, date-palm leaf stalks (P_AC) and black alder cone-like flowers (A_AC) of reliable physical and chemical characteristics were presented in this article. The commercial activated carbon (C_AC) was also examined for the purpose of comparisons with the obtained materials. The detailed characterization of materials was carried out by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), low-temperature N2 physisorption, and Field emission scanning electron microscopy (FESEM). Preliminary analyzes of the adsorption capacities of all activated carbon materials were conducted on water samples from Aleksandrovac Lake (Southern part of Serbia), as a eutrophic lake, in order to remove Cyanobacteria from water. The results after 24 h showed removal efficiencies for P_AC, A_AC, and C_AC of 99.99%, 99.99% and 89.79%, respectively.

Modelling of threats that affect Cyano-HABs in an eutrophicated reservoir: First phase towards water security and environmental governance in watersheds

Cyano-HABs are proliferating around the world due to anthropogenic nutrient enrichment of freshwater bodies. This study seeks to obtain a holistic vision over the various threats that affect the Cyano-HABs of Umia basin and especially of A Baxe reservoir (Galicia, NW Spain), through the method of Partial least squares path modelling (PLS-PM). The A Baxe reservoirs is a fundamental source of drinking water supply to surrounding dwellings. This study identifies and quantify the variables that increase contaminant concentration and decrease ecological integrity, as well as how this scenario evolved over various hydrologic years. In this regard, the PLS-PM equations will be robust and powerful tools to predict changes in eutrophication and ecological integrity, as response to measures implemented in the basin that can improve water quality. The dependent latent variables are "Eutrophication" (chlorophyl-a, Microcystis sp.) and "Ecological Integrity" (METI Bioindicator). The independent latent variables are "SWP", which represents surface water parameters (phosphorus, nitrogen and pH) and "Climatic Conditions" (temperature, precipitation). The PLS-PM results revealed that 51.0% of "Eutrophication" is predicted by the independent variables. The connections between latent variables are quantified through path coefficients (β). The "SWP" contributes by increasing "Eutrophication" (β = 0.235), the same occurring with the "Climatic Conditions" (β = -0.672). The variables "Eutrophication" (β = -0.217) and "SWP" (β = -0.483) lower the "Ecological Integrity". On the other hand, different trophic scenarios, adapted to the temperature increase predicted for the study area, were tested, and it was found that ecological integrity would improve by 46% if the oligotrophic state were reached. Therefore, it is recommended to prevent pollution by means of water control and governance plans, as well as corrective and preventive measures, which guarantee the water security of the river basins. Despite the complex mathematics behind the PLS-PM models, their user-friendly development and application through interactive graphical interfaces make them easily transposable to other eutrophic reservoirs, widening the readership of these studies focused on multiple-geosphere assessment of environmental impacts.

Interactive effect of water level and flushing rate on population dynamics of a harmful cyanobacterial species: Raphidiopsis raciborskii

Changes in water level and flushing rate directly affect to a large extent the biomass of harmful cyanobacteria, and drive the shift of phytoplankton composition between cyanobacteria dominance/non-dominance in eutrophic waters. Here, we gave a theoretical formula describing the combinational effect of water level and flushing rate on cyanobacterial biomass in eutrophic and well-mixed waters. We also formulated an equation predicting the water level and flushing rate at which cyanobacteria become non-dominating in such water columns. The formulae were confronted with field observations of a low-light adapted cyanobacterium in a large coastal reservoir of southern China. Our formulae demonstrate that water level and flushing rate have an interactive effect on the equilibrium biomass of low-light adapted cyanobacteria in mixed and turbid waters. The formulae were well fitted to the field observation of Raphidiopsis raciborskii population in the reservoir during four dry seasons. In agreement with the theoretical analysis, multiple regression analysis also showed that the interaction between water level and flushing rate is able to interpret the variation of R. raciborskii biomass in the water column. The two formulae are applicable for predicting the response of low-light adapted cyanobacteria to local climate change. Our findings have practical significance in designing measures against the dominance of low light-adapted cyanobacteria in reservoirs.

Occurrence of Raphidiopsis raciborskii blooms in cool waters: Synergistic effects of nitrogen availability and ecotypes with adaptation to low temperature

Raphidiopsis raciborskii is a diazotrophic and potentially toxic cyanobacterium. To date, this species has successfully invaded many regions from the tropics to sub-tropical and temperate regions, typically forming blooms at temperatures greater than 25 °C. However, there have been a few cases in which R. raciborskii blooms have occurred at low temperatures (below 15 °C), but its cause and mechanisms remain unclear. In this study, field investigations revealed that R. raciborskii blooms occurred at 10-15 °C in Lake Xihu, Yunnan, China. The biomass of R. raciborskii was found to be positively related to nitrate concentrations in this lake. Three strains of R. raciborskii, two isolated from Lake Xihu (CHAB 6611 and CHAB 6612) and one from Lushui Reservoir in central China (CHAB 3409), were used for growth experiments at 15 °C. The three strains exhibited genotypic (16S rRNA and ITS-L genes) and physiological differences in response to nitrogen concentrations at low temperature. The growth rates of strains CHAB 6611 and CHAB 6612 increased with nitrogen concentration while CHAB 3409 could not grow at 15 °C. Furthermore, the growth and phenotypic responses of CHAB 6611 and CHAB 6612 to nitrogen concentrations were different, despite the closer genetic relationship shared by these two strains. Thus, increased nitrogen concentration in water may enhance the biological availability and utilization of nitrogen by R. raciborskii, which is the external promoter, leading to improving the resistance of R. raciborskii to low temperature. The internal cause is the presence of ecotypes in R. raciborskii populations with adaptation to low temperature. With increasing global eutrophication, the distribution range of R. raciborskii as well as the scale of its blooms will increase. As such, the risk of exposure of aquatic biota and humans to cylindrospermopsin is also expected to increase.

Elevated co2 has Differential Effects on Five Species of Microalgae from a Subtropical Freshwater Lake: Possible Implications for Phytoplankton Species Composition

Rising atmospheric CO₂ concentrations are predicted to have a significant impact on global phytoplankton populations. Of particular interest in freshwater systems are those species that produce toxins or impact water quality, though evidence for how these species, and many others, will respond is limited. This study investigated the effects of elevated CO₂ (1,000 ppm) relative to current atmospheric CO₂ partial pressures (400 ppm), on growth, cell size, carbon acquisition, and photophysiology of five freshwater phytoplankton species including a toxic cyanophyte, Raphidiopsis raciborskii, from Lake Wivenhoe, Australia. Effects of elevated CO₂ on growth rate varied between species; notably growth rate was considerably higher for Staurastrum sp. and significantly lower for Stichococcus sp. with a trend to lower growth rate for R. raciborskii. Surface area to volume ratio was significantly lower with elevated CO₂ , for all species except Cyclotella sp. Timing of maximum cell concentrations of those genera studied in monoculture occurred in the lake in order of CO₂ affinity when free CO₂ concentrations dropped below air equilibrium. The results presented here suggest that as atmospheric levels of CO₂ rise, R. raciborskii may become less of a problem to water quality, while some species of chlorophytes may become more dominant. This has implications for stakeholders of many freshwater systems.

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

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

Concentrations of cylindrospermopsin toxin in water and tilapia fish of tropical fishponds in Egypt, and assessing their potential risk to human health

Unlike microcystin, cylindrospermospin (CYN) concentrations in fishpond water and their accumulation in fish tissues have been largely unexplored. This study determined CYN levels in water and tilapia fish organs from three tropical fishponds in southern Egypt. Water and fish samples were collected monthly from fishponds for 12 months (Oct 2012 to Sep 2013). The results revealed that six CYN-producing species of cyanobacteria dominated phytoplankton populations and formed blooms in these fishponds during warm months. Among these species, Anabaena affinis, Planktothrix agardhii, Cylindrospermopsis catemaco, and C. philippinensis were assigned as CYN producers for the first time in the present study. The highest cell densities of CYN-producing species in fishponds were recorded in August and September 2013, correlating with high temperature, pH and nutrient concentrations. Dissolved CYN was found in fishpond waters at levels (0.3-2.76 μg L^-1) very close to those of particulate CYN (0.4-2.37 μg L^-1). CYN was also estimated in tilapia fish organs at levels up to 417 ng g^-1 in the intestines, 1500 ng g^-1 in the livers, and 280 ng g^-1in edible muscles. Compared to the recommended guideline (0.03 μg kg^-1 day^-1), the estimated daily intake (EDI) of CYN in our samples of edible muscles exceeded this limit by a factor of 1.3-14 during summer and autumn. This might represent a risk to human health upon consumption of such contaminated fish muscles. Therefore, fishponds worldwide should be monitored for the presence toxic cyanobacteria to protect humans from their potent toxins.

Early warning of limit-exceeding concentrations of cyanobacteria and cyanotoxins in drinking water reservoirs by inferential modelling

An early warning scheme is proposed that runs ensembles of inferential models for predicting the cyanobacterial population dynamics and cyanotoxin concentrations in drinking water reservoirs on a diel basis driven by in situ sonde water quality data. When the 10- to 30-day-ahead predicted concentrations of cyanobacteria cells or cyanotoxins exceed pre-defined limit values, an early warning automatically activates an action plan considering in-lake control, e.g. intermittent mixing and ad hoc water treatment in water works, respectively. Case studies of the sub-tropical Lake Wivenhoe (Australia) and the Mediterranean Vaal Reservoir (South Africa) demonstrate that ensembles of inferential models developed by the hybrid evolutionary algorithm HEA are capable of up to 30days forecasts of cyanobacteria and cyanotoxins using data collected in situ. The resulting models for Dolicospermum circinale displayed validity for up to 10days ahead, whilst concentrations of Cylindrospermopsis raciborskii and microcystins were successfully predicted up to 30days ahead. Implementing the proposed scheme for drinking water reservoirs enhances current water quality monitoring practices by solely utilising in situ monitoring data, in addition to cyanobacteria and cyanotoxin measurements. Access to routinely measured cyanotoxin data allows for development of models that predict explicitly cyanotoxin concentrations that avoid to inadvertently model and predict non-toxic cyanobacterial strains.

Differences in cyanobacterial strain responses to light and temperature reflect species plasticity

Microcystis aeruginosa and Cylindrospermopsis raciborskii are two cyanobacterial species that dominate freshwaters globally. Multiple strains of each species with different physiology occur, however, many studies have focused only on one or two strains, limiting our understanding of both strain variation and characterisation of the species. Therefore, in this study we examined the variation in growth and morphology of multiple isolates of both species, isolated from two adjacent Australian reservoirs. Four M. aeruginosa strains (=isolates) (one colony-forming, three single-celled morphology) and eight C. raciborskii isolates (five with straight trichomes, three with coiled trichomes) were cultured individually in a factorial designed experiment with four light intensities (L: 10, 30, 50 and 100μmol photons m^-2s^-1) and two temperatures (T: 20 and 28°C). The specific growth rate (μ), cell volume, and final cell concentration was measured. The light attenuation coefficient (k_j), a measure of self-shading, was calculated. The results showed that the intraspecific variation was greater than the interspecific variation. The μ of all isolates of M. aeruginosa and C. raciborskii ranged from 0.16 to 0.55d^-1 and 0.15 to 0.70d^-1, respectively. However, at a specific light and temperature the mean μ of all M. aeruginosa isolates and C. raciborskii isolates were similar. At the species level, M. aeruginosa had higher growth rates at higher light intensity but lower temperature (L100T20), while straight C. raciborskii had higher growth rates at lower light intensity but higher temperature (L50T28), and coiled C. raciborskii had higher growth rates at higher light intensity and higher temperature (L100T28). The final cell concentrations of M. aeruginosa were higher than C. raciborskii. However, C. raciborskii isolates had greater variation in μ, k_j and cell volume than M. aeruginosa. k_j varied with light and temperature, and decreased with surface-to-volume ratio within each species. k_j was lower for M. aeruginosa compared to C. raciborskii as expected based on cell size, but interestingly, C. raciborskii coiled isolates had lower k_j than the straight isolates suggesting lower effect of self-shading. This study highlights the extent of strain variation to environmental conditions and to species variability.

A novel single-parameter approach for forecasting algal blooms

Harmful algal blooms frequently occur globally, and forecasting could constitute an essential proactive strategy for bloom control. To decrease the cost of aquatic environmental monitoring and increase the accuracy of bloom forecasting, a novel single-parameter approach combining wavelet analysis with artificial neural networks (WNN) was developed and verified based on daily online monitoring datasets of algal density in the Siling Reservoir, China and Lake Winnebago, U.S.A. Firstly, a detailed modeling process was illustrated using the forecasting of cyanobacterial cell density in the Chinese reservoir as an example. Three WNN models occupying various prediction time intervals were optimized through model training using an early stopped training approach. All models performed well in fitting historical data and predicting the dynamics of cyanobacterial cell density, with the best model predicting cyanobacteria density one-day ahead (r = 0.986 and mean absolute error = 0.103 × 10⁴ cells mL^-1). Secondly, the potential of this novel approach was further confirmed by the precise predictions of algal biomass dynamics measured as chl a in both study sites, demonstrating its high performance in forecasting algal blooms, including cyanobacteria as well as other blooming species. Thirdly, the WNN model was compared to current algal forecasting methods (i.e. artificial neural networks, autoregressive integrated moving average model), and was found to be more accurate. In addition, the application of this novel single-parameter approach is cost effective as it requires only a buoy-mounted fluorescent probe, which is merely a fraction (∼15%) of the cost of a typical auto-monitoring system. As such, the newly developed approach presents a promising and cost-effective tool for the future prediction and management of harmful algal blooms.

Molecular separation of two long taxonomically debated cyanobacterial genera Cylindrospermopsis and Raphidiopsis (Nostocales) based on the ITS-L phylogeny

The cyanobacterial genera Raphidiopsis and Cylindrospermopsis morphologically resembled each other and are difficult to distinguish, especially when heterocytes do not form in Cylindrospermopsis species. The phylogeny based on multiple gene loci sequences in previous studies cannot divide the strains of these two genera into separate clusters. In the present study, four gene loci 16S rRNA, ITS-L, cpcBA-IGS and rpoC1, sequenced from many Chinese strains (44 Cylindrospermopsis strains and 16 Raphidiopsis strains), were analyzed to infer the phylogenetic relationship between the two genera. According to the 16S rRNA, cpcBA-IGS and rpoC1 gene phylogeny, Cylindrospermopsis and Raphidiopsis strains are intermixed and divide into two or three groups. By contrast, the ITS-L analysis implied that they respectively form two separate and unique clusters. A fragment of 7bp (RAGAAACT) in ITS-L was conserved for Raphidiopsis strains, which could be distinguished from Cylindrospermopsis strains. A pair of primers targeting the ITS-L fragment specific to Raphidiopsis strains were designed and further verified.

Understanding the winning strategies used by the bloom-forming cyanobacterium Cylindrospermopsis raciborskii

The cyanobacterium Cylindrospermopsis raciborskii is a widespread species increasingly being recorded in freshwater systems around the world. It is of particular concern because strains in some geographic areas are capable of producing toxins with implications for human and animal health. Studies of this species have increased rapidly in the last two decades, especially in the southern hemisphere where toxic strains are prevalent. A clearer picture is emerging of the strategies adopted by this species to bloom and out-compete other species. This species has a high level of flexibility with respect to light and nutrients, with higher temperatures and carbon dioxide also promoting growth. There are two types of toxins produced by C. raciborskii: cylindrospermopsins (CYNs) and saxitoxins (STXs). The toxins CYNs are constitutively produced irrespective of environmental conditions and the ecological or physiological role is unclear, while STXs appear to serve as protection against high salinity and/or water hardness. It is also apparent that strains of this species can vary substantially in their physiological responses to environmental conditions, including CYNs production, and this may explain discrepancies in findings from studies in different geographical areas. The combination of a flexible strategy with respect to environmental conditions, and variability in strain response makes it a challenging species to manage. Our ability to improve bloom prediction will rely on a more detailed understanding of the complex physiology of this species.

Cylindrospermopsis raciborskii: review of the distribution, phylogeography, and ecophysiology of a global invasive species

Cylindrospermopsis raciborskii is a cyanobacterial species extensively studied for its toxicity, bloom formation and invasiveness potential, which have consequences to public and environmental health. Its current geographical distribution, spanning different climates, suggests that C. raciborskii has acquired the status of a cosmopolitan species. From phylogeography studies, a tropical origin for this species seems convincing, with different conjectural routes of expansion toward temperate climates. This expansion may be a result of the species physiological plasticity, or of the existence of different ecotypes with distinct environmental requirements. In particular, C. raciborskii is known to tolerate wide temperature and light regimes and presents diverse nutritional strategies. This cyanobacterium is also thought to have benefited from climate change conditions, regarding its invasiveness into temperate climates. Other factors, recently put forward, such as allelopathy, may also be important to its expansion. The effect of C. raciborskii in the invaded communities is still mostly unknown but may strongly disturb species diversity at different trophic levels. In this review we present an up-to-date account of the distribution, phylogeography, ecophysiology, as well some preliminary reports of the impact of C. raciborskii in different organisms.