Open access peer-reviewed chapter

An Overview of Cyanobacteria Harmful Algal Bloom (CyanoHAB) Issues in Freshwater Ecosystems

Written By

Naila-Yasmine Benayache, Tri Nguyen-Quang, Kateryna Hushchyna, Kayla McLellan, Fatima-Zohra Afri-Mehennaoui and Noureddine Bouaïcha

Submitted: 03 December 2018 Reviewed: 04 January 2019 Published: 03 April 2019

DOI: 10.5772/intechopen.84155

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This chapter will present an overview of cyanobacterial harmful algal blooms (cyanoHABs) and biotic and abiotic factors, as well as various aspects associated with these worldwide ecological bursts. The exact causes of the cyanoHABs are still not well defined, but eutrophication and climate change (temperature increase, light intensity variation, etc.) are the two assumed main factors that may promote the proliferation and expansion of cyanobacterial blooms. However, these premises need to be profoundly investigated as the optimal combination of all factors such as increased nutrient loading, physiological characteristics of cyanobacterial species, and climate effects which could lead to the blooming pattern will require robust modeling approaches to predict the phenomena. Negative issues associated with cyanoHABs are diverse including the toxic products (cyanotoxins) released by certain taxa which can damage the health of humans and animal habitats around the related watershed as well as generate a huge water quality problem for aquatic industries.


  • cyanobacteria
  • cyanotoxins
  • freshwater ecosystems
  • mathematical modeling
  • ecotoxicity

1. Introduction

Freshwater ecosystems (lakes, rivers, and reservoirs) play an important role in regulating Earth’s climate and they are of high ecological and socioeconomic value, as well as a crucial life-giving resource for humanity. However, these water bodies are fragile and anthropic pressures such as discharge of sewage, industrial pollutants, eroding soil, and deposition of effluents rich in nitrates and phosphorus cumulating by the tourist industry and urbanization have accelerated the rate and extent of their continuous eutrophication which has led to a loss of water quality and biodiversity. Worldwide, the massive proliferation of cyanobacterial harmful algal blooms (cyanoHABs) is among the major undesirable effects resulting of eutrophication [1, 2, 3, 4]. To date, environmental factors identified as contributing toward their global expansion included increased nutrient inputs via anthropogenic activities and temperatures and CO2 concentrations due to changing global climate [5, 6, 7, 8, 9, 10]. Nevertheless, these aspects need to be investigated in the future and the combination of increased nutrient loadings, physiological characteristics of cyanobacterial species, and climate change such as increase of temperature and variation of light intensity and quality will require robust modeling approaches to predict the blooming phenomena.

Since freshwater bodies around the world can be used as drinking water reservoirs or recreational areas, the blooming phenomena have gained attention as possible health hazards. The problems associated with toxic cyanobacterial blooms in these different areas are diverse, from environmental asphyxiation due to excessive consumption of oxygen, to purely esthetic problems in recreational areas when the blooms are a colorful and often smelly scum on the surface of the water [5, 11, 12]. To these common problems are added productions by certain species of cyanobacteria of various secondary metabolites with a specific toxic potential (hepatotoxins, neurotoxins, and dermotoxins) causing water quality problems for fisheries, aquaculture, farming, and sanitary hazards to human and animal health [13, 14, 15, 16, 17]. This chapter focuses on the cyanoHAB occurrence as well as on environmental factors favoring their proliferation, possible human and animal health outcomes associated with their toxins and a review of robust modeling approaches to predict the bloom pattern.


2. Involving factors in cyanoHAB pattern

2.1. Abiotic factors

Among the abiotic factors, nutrients, including inorganic nitrogen (N) and phosphorus (P), temperature, and light intensity, and hydrodynamic parameters of the water body (turbidity and residue time) have been reported as the most important factors in the proliferation of cyanobacteria [2, 4, 6]. The availability of nutrients such as N and P is essential for the growth of cyanobacteria. For example, field experiments by monitoring surveys of phytoplankton for 3 years (2012–2014) in Lake Erie (U.S.A.) showed that inter-annual differences in the duration, intensity, and toxicity of cyanobacterial blooms in this area were considered related to in-lake and tributary nutrient (N and P) concentrations [2]. This ecosystem observation is consistent with other earlier field and laboratory studies which have shown that cyanobacterial bloom occurrence and cyanobacterial species growth in cultures, respectively, have been controlled by the availability of both inorganic nitrogen and phosphorus [18, 19, 20, 21]. However, cyanobacteria taxa such as N2-fixing (diazotrophic) and non-N2 fixing (nondiazotrophic) species have a variety of mechanisms to compete for nitrogen. A strong relationship between the growth and toxin synthesis of diazotrophic and nondiazotrophic cyanobacteria and inorganic dissolved N in the medium has commonly been reported in the literature [22, 23, 24, 25]. Efforts often focus on total nitrogen (TN) and there exist important gaps in the understanding of how N speciation (NO3 and NH4+) influences cyanobacterial blooms and cyanotoxin synthesis. For example, in lakes showing symptoms of N limitation during late summer, numerous studies reported that cyanobacteria such as Microcystis, a non-N2 fixing genus, have been shown to become dominant by rapidly assimilating recycled ammonium [26, 27, 28, 29]. Indeed, laboratory and in situ studies have shown that cyanobacteria appear to out-compete other algal species for reduced N forms such as ammonium and urea [26, 27, 30]. For example, Donald et al. [31] reported that fertilization of the lake Wascana (Canada) with ammonium increased total algal abundance about 350% and cyanobacterial biomass over 500%. In a recent study examining the effects of different forms of N (nitrate, ammonium, and urea) in Lake Erie (U.S.A.), Chaffin et al. [32] have shown that the ammonium enrichment resulted in greater cyanobacterial biovolume than in the nitrate and urea enrichments. While nitrate is generally the most abundant form of nitrogen in freshwater ecosystems, it is the least preferred form of nitrogen, since its uptake by cyanobacteria requires multiple steps of intracellular reduction to ammonia [32, 33, 34]. Hence, while nitrogen (N) plays a primary role in shaping the relative abundance of cyanoHABs in a freshwater ecosystem, phosphorus (P) likely acts and interacts to influence these populations as well. The ability of cells to store phosphorus as polyphosphates [35] allows them to double several times even in phosphorus-limiting conditions [36]. Phosphorus affinities are higher in cyanobacteria compared to eukaryotic algae [37]. The concentration of phosphorus around 0.03 mg L−1 is enough for the sufficient growth of the cyanobacteria [38]. Therefore, phosphorus is commonly considered to be the limiting nutrient in freshwater ecosystems, and high concentrations of this nutrient often correlate to the occurrence of cyanobacterial blooms worldwide [2, 6, 39, 40, 41, 42]. In contrast, instead of considering the effects of N and P separately, numerous studies highlight the importance of the ratio of TN to TP (TN:TP) in determining cyanobacterial growth [42, 43]. For example, several studies in many freshwater bodies showed that when the ratio TN:TP decreased, a shift has been reported in phytoplankton assemblages toward cyanobacteria dominance [2, 12, 44].

Light intensity and quality are other important factors in phytoplankton growth. Phytoplankton can photosynthesize, using the pigments chlorophyll-a (Chl-a) and -b, therefore at a certain light intensity, depending on the species, the algae will be at maximum productivity. The pigments are also sensitive to specific wavelengths: blue and red light. Using two species as an example, Aphanizomenon flos-aquae is less light dependent than Dolichospermum flos-aquae, so in situations where there is less light, Aphanizomenon flos-aquae would be at maximum production. The cyanobacterial light harvesting mechanism is different from that of the eukaryotic algae and contains phycobiliproteins, which allows cyanobacteria to absorb light from a wide light spectrum [36]. In the fast-changing light environment, cyanobacteria have a photoadaptation mechanism, which reduces the number of harvesting mechanisms and turns the energy into the heat [36]. There is also a photoprotective mechanism that cyanobacteria use: energy dissipation mechanism [36]. They have a UV photoprotection mechanism as well: mycosporine-like amino acids (MAAs) and scytonemin that absorbs UV light and helps them to survive with high level of irradiance [45].

Water temperature leads to cyanobacterial bloom development and plays a critical role in buoyancy and assimilation of essential nutrients and synthesis of toxins [46, 47, 48]. For example, Kosten et al. [19] by examining 143 lakes along a latitudinal transect ranging from subarctic Europe to southern South America found that temperature and TN concentrations were the strongest explanatory variables for cyanobacterial biomass. This finding is also consistent with that reported by Beaulieu et al. [20] who examined the proliferation of cyanobacteria in 1147 lakes and reservoirs of different trophic status in the United States and showed that the best linear multiple regression model for predicting these events was based on TN and temperature of the lake water. Therefore, in terms of global climate change, it is obvious that the increase of water temperature will be observed, and cyanobacteria will have the prevalence in the growth rate compared to the other phytoplankton.

Turbidity is another factor that influences algal growth. The particulate matter in the water column affects light penetration and temperature of the water. An excess of sediments in the water would decrease the light penetration, which in turn may prevent large algal blooms. The sediments also aid in lowering the amount of temperature fluctuations in the water. A more consistent and possibly lower temperature would help prevent large algal blooms [49]. The higher the pH, the higher diversity of cyanobacteria can be found with a prevalence of nonfixers (Microcystis spp.), while N-fixers are more dominant at low pH [50]. The structure of the lake plays the accessory role in bloom formations. The depth of a water body, speed of flow, and presence of small coves make every water body unique and need additional attention.

Abiotic factors described above are not the only ecological factors influencing the occurrence and frequent dominance of cyanobacteria in the phytoplankton. Their widespread representation in freshwaters depends also on biotic factors such as buoyancy, allelopathic effects, and zooplankton grazing among others that will be examined in the next section.

2.2. Biotic factors

Cyanobacterial species have numerous physiological adaptations that permit them to exploit nutrients and light differentially. Some species belonging to the genera Microcystis, Anabaena (renamed Dolichospermum), Planktothrix, Aphanizomenon, and Cylindrospermopsis among others possess gas vesicles that provide them buoyancy and vertical movement through the water column and can therefore effectively dominate other pelagic planktonic algae for available sunlight and nutrients [51]. This physiological capacity confers a substantial ecological advantage to these species, as they can congregate at a dense mass in the water column of stratified lakes and move up and down in the water column to maximize photosynthesis in the surface layers where there is more photosynthetically active radiation and to take up nutrients in dark deeper layers where the concentration of nutrients is higher. In addition, the ability of these genera to fix and assimilate dissolved nitrogen gas when the external concentrations of dissolved nitrate and ammonia fall to low levels is a supplementary biotic factor that offers them an ecological advantage over other phytoplanktonic species [52, 53]. Moreover, the resistance of the larger, gas-vesicle colony-forming cyanobacteria such as Microcystis to sinking loss or consumption by grazers can provide a significant advantage when this factor operates against small, nonmotile unicellular phytoplankton [54, 55, 56]. In addition, some species of cyanobacteria produce allelopathic substances that prevent the growth of submerged vegetation and other algae [57, 58], as well as increased resistance to predation by zooplankton, reducing the diversity of grazing species, and therefore the formation of blooms [59]. Besides zooplankton grazing, fish activities, benthic fauna, bacteria relationship, and viral lyses are considered as supplementary biotic factors that control algal blooms [7].


3. Mathematical modeling: a necessary approach for studying cyanoHAB proliferation

3.1. General context

As we notice from previous parts, relationships between the bloom patterns and involved factors are highly complex, therefore appropriate prognostic techniques to forecast blooms and evaluate their spatio-temporal evolution are indispensable. However, due to the complicity and nonlinearity of the phenomenon, none of the research on predictive approaches seems accurate and none has performed well to date. Moreover, no existing research could help to identify the very important factor: thresholds of blooms under the environmental conditions. This part of the chapter will review some mainstreams of mathematical models used in the bloom prediction.

3.2. Deterministic versus probabilistic

With the development of super-powerful computers and computational techniques, many mathematical models for predicting the algal growth have been developed in recent years. There are two main families of mathematical models which are commonly used: deterministic and probabilistic (or stochastic). The deterministic approach can be chosen when the nature of problem and dataset have well determined, repeatable, and fixed outputs for the same inputs. This means they have a precise cause-effect relationship. Conversely, a stochastic approach is preferable when a system has some inherent randomness and we must estimate possible outcomes with their occurrence probability to define its behavior. Stochastic models are fundamentally built based on the randomness and uncertainty of the nature of bloom pattern, reflected through a large amount of field data. These data are indispensable for the modeler to validate and test the precision and accuracy of their models. Among the category of stochastic models, machine learning techniques provide many powerful tools to solve some relevant difficulties in predicting HAB. Machine learning techniques were developed for quantitative finance, enabling researchers to tap huge datasets. There are many publications in recent years in which diverse Supervised Machine Learning (SML) models have been applied to solve a wide range of problems, including the Artificial Neural Network (ANN) and Support Vector Machine (SVM). ANN is a SML approach widely used to predict the algal abundance [60, 61, 62, 63, 64] while SVM is used much less in algal research [65, 66, 67]. Some studies used genetic algorithms (GA) to create prediction model [68, 69]. The basic concept of these models lies in the combined effects of a set of explanatory variables (Xi) on one or some target variables (Yi), and then classification or regression decision depending on the nature of Yi. The variables Yi (outputs of the study) in most of the models target the pigment Chl-a, which stands for the growth of algal communities, biomass (algal abundance) quantity, and the number of algal cell counts.

Wei et al. [61] suggested a model to predict the timing and magnitude of four different types of algae: Microcystis, Oscillatoria, Synedra, and Phormidium. Algal blooms responded to the orthogonal combinations of water temperature, light penetration, dissolved oxygen, chemical oxygen demand, total nitrogen (TN), total phosphorus (TP), zooplankton, and pH value. This study used backpropagation ANN; data in this study were collected monthly during 15 years from 1982 to 1996 and these data were divided randomly into two sets: training dataset and testing dataset. This study also analyzed the sensitivity of the model in which pH played a key role in the blooming of algae and all four types of algae are more sensitive to TP than TN.

Another study conducted by Wilson and Recknagel [60] used feedforward ANN to predict the bloom of algae in Australia. They suggested a regression model between four inputs (phosphorus, nitrogen, underwater light, and water temperature) and one main output (biomass) was designed. In 30-day-ahead model, beside algae biomass, they added the second output chlorophyll-a and used time delay neuron network structure where inputs are one-time step (e.g., 30 days) in the past relative to output variables. Fernández et al. [70] suggested a model to predict the presence of cyanotoxins in fresh water in Spain. A group of six inputs consisting of both biological and chemical factors is used and the output is the presence of cyanotoxins (μg L−1). The most significant aspect of this model is the product of the concentration of M. aeruginosa and W. naegeliana, followed by turbidity, total phosphorus, alkalinity, and water temperature. This model used generic algorithm (GA) and multivariate adaptive regression spline (MARS) techniques in which 10-fold cross validation was used to train and validate the model. Park et al. [62] developed an early-warning model for freshwater algal bloom based on Chl-a concentration using ANN and SVM. These authors used the weekly data in 7-year period (from 2006 to 2012) to design a 7-day interval prediction model for two lakes in Korea. Five water quality parameters including Chl-a, orthophosphate as phosphorus (PO4-P), ammonium nitrogen (NH3-N), nitrate nitrogen (NO3-N), water temperature, and two meteorological data (solar radiation and wind speed) are inserted as inputs and output for ANN and SVM models.

Recently, Nelson et al. [71] used the Random Forest algorithm to characterize and quantify relationships between 10 different conditions and five dominant cyanobacteria genera. All explanatory variables were lagged by 1-month step to reflect the division rates of cyanobacteria in natural environments. Outputs are the biomass values of five different types of cyanobacteria genera.

As approaches using the probabilistic models are limited due to their complex concepts and high randomness levels, especially due to the needs of a large amount of data to validate them, which use various factors that influence cyanobacterial growth, the deterministic strategies will allow the evaluation of the risks associated with cyanobacteria in the context of “less data needed” and moreover, many physical parameters could be incorporated in coupling with biochemical factors. Various deterministic approaches [72, 73, 74, 75, 76, 77, 78] have been used in the understanding of the distribution of cyanobacteria. The Lagrangian deterministic model follows the cyanobacterial colony in the water column so that a mathematical model can be created to describe bloom density. The Kromkamp and Walsby [72] model is only used to estimate settling velocities and the Visser et al. [76] model is an improved model, which incorporates the irradiance-response curve of density change and proposed an equation that describes the rate of density change in the dark. The Lagrangian approach is used for studying movement of cells in a laboratory setting, but an Eulerian approach enables exploration of full-scale spatial distribution of cells at specific times. Bruggeman and Bolding [79] built a framework called the Fortran-based framework for aquatic biogeochemical models where the biochemical model was connected to a physical model. Then a self-contained complex biological model was combined with a hydrodynamic model by the Fortran-based framework for aquatic biogeochemical model. This model was used to calibrate physiological parameters for the phytoplankton. Recently, the work of Ndong et al. [80] has shown a sophisticated 2D Eulerian frame model to evaluate the phototactic behavior effect of cyanobacteria, as well as the effects of light and wind on the distribution of cyanobacteria and estimate coupled effects of biological and physical factors on cyanobacteria.

The new tendency of research based on the deterministic approach is using remote sensing data or satellite imagery in the detection of the spatiotemporal patterns of blooms and explains how they change under the environmental conditions. The issue with this imagery is that the movement patterns of cells in the water column may be missed. The response to light intensity, nutrient levels, and temperature also needs to be considered, which means that numerical data along with imagery are required to complete the data. Agent-based models have been used to observe the 2D and 3D transport trajectories of cyanobacteria. These models are coupled with an Eulerian model, which allows the cyanobacteria to drift in the model [81, 82, 83].

3.3. Future perspectives

The overall and common goal of all models was to attempt to explain the risks of algal/cyanobacterial blooms and to study their evolution under environmental conditions leading to the improvement or decision process used to monitor cyanobacteria. However, as previously mentioned, almost all existing models have focused on the target variables such as Chl-a concentration development, cell count numbers of taxa or genera, biomass, etc.; from them, authors could conclude about the bloom situation. There are therefore two main directions of modeling among many others that should be developed: (1) determination of biophysical threshold for blooms and (2) quantifying and modeling the toxin concentration released by toxic species.

Remote sensing data combined with machine learning algorithm are also an encouraging perspective. But one of the potential pitfalls for machine learning strategies is the extremely low signal-to-noise ratio. Machine learning algorithms will always identify a pattern, even if there is none. In other words, the algorithms can view flukes as patterns and hence are likely to identify false strategies. Every model regardless of what category it belongs to can have its weak and strong points and need a serious validation step to be universally applicable, useful, and accurate.


4. Dominant taxa found in cyanoHABs in freshwater

Although cyanobacterial blooms are a worldwide phenomenon, there are differences in typical genera found in temperate and tropical regions (Table 1). Microcystis was the most frequently occurring bloom genus throughout the world, while Cylindrospermopsis and Dolichospermum (Syn. Anabaena) blooms occurred in various tropical areas such as Australia, America, and Africa.

Region Dominant species References
Africa Microcystis flos-aquae, M. wesenbergii, Oscillatoria sp., Dolichospermum sp., Lingbya sp., Anabaenopsis sp. [3, 48, 84, 85]
Western Asia Planktothrix rubescens, M. aeruginosa, Nodularia spumigena, Aphanizomenon ovalisporum, P. agardhii, Synechocystis sp., Dolichospermum sp. [84, 86]
Southern Asia Dolichospermum sp., Aphanizomenon sp., Microcystis sp., Cylindrospermopsis sp., Planktothrix sp. [84, 86]
Eastern Asia Dolichospermum sp., Microcystis sp., Aphanizomenon sp., Merismopedia sp., Cylindrospermopsis sp., Nostoc sp., Planktothrix sp. [7, 84, 85]
Oceania D. planctonicum, D. circinale, Aphanizomenon tenuicaulis, C. raciborskii, A. ovalisporum, A. issatschenkoi, P. rubescens, Kamptonema formosum, M. aeruginosa, M. panniformis [48, 84, 85, 86]
South and Central America M. aeruginosa, Cylindrospermopsis sp., Dolichospermum sp., Nodularia sp., Lingbya sp. [7, 48, 84, 85]
North America M. aeruginosa, M. viridis, M. wesenbergii, Aphanizomenon schindleri, D. flos-aquae, D. planctonicum, D. circinale, D. lemmermani, D. smithii, D. viquiera, C. raciborskii, P. rubescens, Lyngbya majuscula, L. wollei, Phormidium sp., Woronichinia naegeliana [7, 87, 88, 89]
Europe Microcystis sp., Dolichospermum sp., Aphanizomenon sp., Planktothrix sp., Nodularia sp., Cylindrospermopsis sp., Phormidium sp., Anabaenopsis sp., Gloeotrichia sp. [7, 84, 86, 90]

Table 1.

Dominant cyanobacterial taxa recorded worldwide.


5. Negative outcomes from cyanoHABs

5.1. Cyanobacterial toxins and their environmental concentrations

Cyanotoxins are classified according to their mode of action into three families: neurotoxins (nervous system), hepatotoxins (liver), and dermotoxins (skin) [4, 17, 91]. Blooms formed by cyanobacteria producing hepatotoxins (microcystins and cylindrospermopsin) are more widespread than neurotoxic blooms [4, 92, 93, 94, 95] and therefore, they are considered priority for biomonitoring, especially in drinking and recreational waters.

Cyanotoxins are intracellular toxins that are released into water only during cellular senescence or death and lysis or through water treatment processes such as application of algaecide [96, 97]. Therefore, total concentrations (intracellular plus extracellular) of microcystins, the most common cyanotoxins, vary from trace to several milligrams per liter [91, 98]. For example, very high concentrations have been reported up to 8428 μg L−1 in Southwest wetlands, Australia [99], 19,500 μg L−1 in Lake Suwa, Japan [41], 23,718 μg L−1 in Dam Nhanganzwane, South Africa [100], 29,200 μg L−1 in Lake Oubeira, Algeria [101], or 36,500 μg L−1 in Lake Horowhenua, New Zealand [102]. Messineo et al. [103] reported that in several Italian lakes, concentrations of total cylindrospermopsin varied from nondetectable values up to 126 μg L−1. However, neurotoxins are less common in the freshwater ecosystems. For example, Rapala et al. [104] reported up to 1070 μg L−1 of saxitoxin in Finnish lakes. Anatoxin-a was detected in two shallow reservoirs (Konstantynów and Kraśnik) in Poland at concentrations ranging from 0.03 to 43.6 μg L−1 during a bloom of D. flos-aquae [105]. Recently, Roy-Lachapelle et al. [106] reported that the concentrations of the BMAA in 12 different lake waters in Canada ranged between 0.009 and 0.3 μg L−1.

5.2. Ecotoxicological effects of cyanotoxins

Cyanotoxins such as hepatotoxins and neurotoxins target in humans and animals the liver and nervous system, respectively, but they often have important side effects too. When present in freshwater ecosystems, they may also affect organisms at different trophic levels, especially those having identical or similar target organs, tissues, or cells.

5.2.1. Acute effects

The occurrence of cyanoHABs in aquatic ecosystems is often associated with fish mortality (Figure 1). In addition, terrestrial organisms such as livestock, dogs, and birds that are associated with these freshwater ecosystems in which cyanoHABs occur may also be at risk of cyanotoxins exposure from preying on toxic aquatic prey and/or drinking contaminated water. For example, Georges Francis was the first in 1878 to implicate cyanobacteria in the poisoning of farm animals, in Alexandrina Lake, Milang, Southern Australia [107]. Since then, a significant number of cases of animal poisonings attributable to cyanotoxins have been documented worldwide [108, 109, 110, 111]. Fish and invertebrates which are exposed over their entire life cycle to cyanotoxins are the most aquatic organisms affected, followed by birds, livestock and poultry, and dogs [111]. Acute ecotoxicity data of cyanotoxins were compiled by several studies [112, 113, 114]. The most documented cyanotoxin effects are those on microcystins due to their occurrence at high concentrations up to 28 mg L−1 and the dominance of cyanobacterial species producing them [91]. In addition, depending on their mechanism of action as potent and specific inhibitors of protein phosphatases and inducer of oxidative stress, microcystins can affect a range of invertebrate and vertebrate organisms [109, 114, 115]. Therefore, they cause changes in the trophic levels and adverse impacts on the functioning of freshwater ecosystems. This begins with the zooplankton community, which has its composition changed, especially by the mortality of certain species resulting therefore in the reduction of their diversity [59, 109, 116, 117]. For example, the copepod Diaptomus birgei was the most sensitive to microcystins with a lethal concentration (LC50) at 48 h of 0.45 to 1.0 μg mL−1 followed by the cladoceran Daphnia pulex, D. hyaline, and D. pulicaria with LC50 at 48 h of 9.6, 11.6, and 21.4 μg L−1, respectively [109].

Figure 1.

Microcystis sp. bloom associated with fish mortality (photo: N.Y. Benayache).

However, mollusks and decapods appeared to be relatively tolerant to microcystins [109, 111]. For example, the LC50 at 96 h for microcystin-LR equivalent in the decapod Kalliapseudes schubartii [118] and the crayfish Procambarus clarkia [119] is 1.58 and 0.567 mg L−1, respectively. Similarly, bivalves bioaccumulate high concentrations of microcystins without symptoms of acute toxicity [120].

Mass mortalities of fish have also been attributed to microcystins [14, 111, 121]. However, some studies suggested that most of fish mortalities can also be attributed to hypoxic conditions resulting from bloom respiration and senescence and not only to cyanotoxins [122, 123]. Like bivalves, fish appear to be less sensitive to toxin’s short-term exposure than zooplankton. For example, experimental investigations on the rainbow trout have been shown that this fish species appeared to be relatively tolerant to high concentrations of microcystin-LR and death was recorded only at 1000 μg kg−1 bw [124]. Several studies reported that the dose inducing the mortality of the half of the test population (LD50) of microcystin-LR in fish ranges from 20 to 1500 μg kg−1 body weight [125].

For the other classes of alkaloid cyanotoxins such as cylindrospermopsin and neurotoxins (anatoxins and saxitoxins), there are few or no studies that have examined their acute toxicity on aquatic organisms. For example, Ferrão-Filho et al. [126] reported that the exposure of three cladoceran species (Daphnia gessneri, D. pulex, and Moina micrura) to a saxitoxin-producer strain (T3) of Cylindrospermopsis raciborskii at cell densities of 103 and 104 cells/mL for 24 h resulted in a complete paralysis of D. pulex; however, D. gessneri was not sensitive and M. micrura was intermediate in sensitivity. Osswald et al. [127] demonstrated that when common carp Cyprinus carpio larvae were exposed to a lyophilized suspension (107 cells/mL) of a strain of Anabaena sp. producing anatoxin-a, all fish died between 24 and 29 h.

5.2.2. Subchronic and chronic effects

Aquatic organisms are continuously exposed over long periods of time or even their entire life cycle to cyanotoxins; therefore, evaluation of chronic effects of these toxins is important for an accurate environmental risk assessment. Several studies have shown that aquatic organisms that are exposed in the long term to cyanotoxins through the diet may die or display impaired feeding, immunosuppression, increased susceptibility to disease, avoidance behavior, physiological dysfunction, abnormal development, and reduced growth and reproduction [109, 125]. For example, chronic exposure of parent Daphnia magna to either microcystin-LR at 5 or 50 μg L−1, or to cyanobacterial crude extract containing the same amount of total microcystins, resulted in the decrease of the survival of offspring or cessation of eggs and reduced number of neonates and deformations of neonates such as incomplete development of the antennae [128]. Moreover, several studies have shown that when embryos and larvae of different species of fish including chub (Leuciscus cephalus), common carp (Cyprinus carpio), loach (Misgurun smizolepis), rainbow trout (Oncorhynchus mykiss) and zebrafish (Danio rerio) were immersed in solutions of 0.5–50 μg microcystins/L for up to 30 days, it resulted in interferences with hatching, developmental defects, liver damage, and/or increased mortality [129, 130, 131, 132]. In another chronic study, Ernst et al. [133] by investing the effect of a high microcystin concentration on eggs and larvae of whitefish (Coregonus lavaretus) exposed to blooms of Planktothrix sp. during winter 1998 and 2000 in a Lake Ammersee (Germany) hatchery reported malformations of eggs and disturbances of reproduction success, suggesting that the disappearance of some coregonid age groups observed in this lake may be a result of these development effects of microcystins. In a laboratory study, oral subchronic exposure of the common carp (mean body weight of 322 g) to Microcystis by feeding with bloom scum at a dose of 50 μg microcystins/kg body weight for 28 days resulted in inhibition of growth, severe damage in hepatocytes, and significant increase of some plasmatic enzyme activities such as alanine aminotransferase and aspartate aminotransferase [134].

5.2.3. Ecotoxicity of cyanotoxin mixtures

Aquatic organisms are most likely subject to acute, subchronic, and chronic impacts resulting from exposure to a mixture class of cyanotoxins and not to individual toxins. Cyanobacterial species producing different toxins such as hepatotoxins, neurotoxins, and dermotoxins have been shown to coexist in blooms [91], therefore making the exposure to toxin mixtures a plausible scenario. To investigate this scenario with considering the possible synergistic toxicity of complex matrices, Esterhuizen-Londt et al. [135] tested the effects of two artificial toxin mixtures containing cyanobacterial hepatotoxins (microcystin-LR, -YR, and -RR), cyanobacterial hepatotoxins (microcystin-LR and cylindrospermopsin), and the neurotoxin β-N-methylamino-l-alanine hydrochloride, respectively, versus a crude cyanobacterial bloom extract (dominated by Microcystis aeruginosa with minor proportions of Anabaena sp. and Oscillatoria sp.) on the oxidative status of Daphnia pulex. The results showed that the cyanobacterial extract elicited higher oxidative stress response on D. pulex compared to exposure with the two artificial toxin mixtures. According to these studies, authors suggested that other unidentified compounds present in the cyanobacterial extract with synergistic effects may enhance the toxic effects. In fact, previous studies found stronger developmental effects of cyanobacterial extracts containing microcystins [136, 137] on the African clawed frog Xenopus laevis embryos and anatoxin-a [138] on common carp Cyprinus carpio larvae than their respective purified toxins.

In addition, in natural environments, cyanotoxins could interact with other anthropogenic micropollutants present in aquatic ecosystems and therefore, could attenuate or potentiate their adverse effects on aquatic organisms. For example, combined influence of microcystin-LR and a pesticide, carbaryl, was investigated on Daphnia pulicaria [139]. The results showed that the interaction between carbaryl and microcystins was highly significant and the two chemicals in a combinatorial exposure induced synergistic effects with frequent premature offspring delivery with body deformations including dented carapax or undeveloped heart. Furthermore, Cazenave et al. [140] observed less pronounced teratological effects within 24 h as well as nonsignificant increase in the activity of glutathione S-transferase (GST) in embryos of zebrafish (Danio rerio) exposed to either microcystin-LF or microcystin-RR in combination with natural organic matters compared to embryos exposed to pure toxins.

5.3. Bioaccumulation of cyanotoxins in food web and impacts on animal and human health

Zooplankton have been clearly identified as the best bioaccumulator of cyanotoxins and may transfer them to higher trophic levels in the aquatic food web [141, 142, 143, 144]. Mollusks have also been shown to accumulate high concentrations of cyanotoxins with hepatopancreas being the organ presenting the highest concentrations followed by the intestines [115, 145]. As with invertebrates, fish can also accumulate high concentrations of microcystins but on average 3.5 times lower in planktivorous fish than in zooplankton [115]. For example, the highest concentrations of microcystins were found in the liver of the planktivorous fish Osmerus eperlanus reaching up to 874 μg microcystins/g dry weight [146]. However, in another planktivorous fish such as the silver carp Hypophthalmichthys molitrix, the highest concentrations of microcystins were found in the intestines reaching up to 137 μg g−1 DW [147]. Carnivorous fish, meanwhile, accumulate less microcystins with the maximum concentration up to 51 μg g−1 DW measured, for example, in the liver of perch Perca fluviatilis [146]. Overall, carnivorous fish, as superior predators, had lower mean microcystin content than planktivorous and omnivorous fish, suggesting transfer and bioaccumulation of microcystins, however without biomagnification in the food chain. In contrast, fish may act as an efficient vector of cyanotoxins to upper trophic levels such as birds and humans. In fact, numerous bird deaths have been reported in which most deaths are associated with the consumption of toxic prey, for example, fish or mollusks that have consumed or otherwise bioaccumulated cyanobacterial toxins [109, 110, 148, 149]. For humans, Chen et al. [150] confirmed for the first time the presence of microcystins in serum samples (average 0.39 ng/ml) of fishermen at Lake Chaohu, China. According this study, daily intake by the fishermen was estimated to be in the range of 2.2–3.9 μg MC-LR equivalent, whereas the provisional World Health Organization tolerable daily intake (TDI) for daily lifetime exposure is 0.04 μg kg−1 or 2–3 μg per person. However, as has been described previously, the different species of fish accumulate microcystins mainly in the intestine and liver/hepatopancreas; this poses no risk to human health if these organs are taken from animals before consumption.


6. Conclusions

The chapter has sketched a general overview on cyanoHABs which recently become a real worrisome issue at the global scale due to their effects on water resources and animal and human health. They will cause ongoing issues as they will certainly reoccur over and over the coming years, especially under the promoting factors of climate changes and global warming effects, as much as the abuse of all watersheds due to anthropogenic actions.

Different research studies around the world have highlighted the complex relationships between cyanobacterial growth and environmental factors. The cyanoHAB dominance can result from a variety of interactions among biotic and abiotic components. The presence of toxic cyanobacteria can influence the human society at all scales, such as direct effects on drinking and recreational water resources, as well as the transfer of their toxins to higher trophic levels, resulting in fish kills and threats to all animal and human health.

Keeping our water resources clean, healthy, and safe for current and next generations becomes, therefore, a big challenge for our planet. The task of monitoring and managing cyanobacterial blooms and their negative outcomes including toxins released is a pressing concern for all. The chapter hence will serve to increase awareness of common challenges and existing capacities as well as lay the foundation for ongoing discussion and research on various subjects related to CyanoHABs that will be needed for effective management for the years to come.



NYB was granted by a fellowship from the Algerian-French PROFAS B + program. TNQ acknowledges the Natural Science and Engineering Research Council of Canada via Discovery Grant NSERC RGPIN 03796.


Conflict of interest

The authors wish to confirm that there are no known conflicts of interest associated with this chapter and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.


  1. 1. Steffen MM, Belisle BS, Watson SB, Boyer GL, Wilhelm SW. Status, causes and controls of cyanobacterial blooms in Lake Erie. Journal of Great Lakes Research. 2014;40:215-225. DOI: 10.1016/j.jglr.2013.12.012
  2. 2. Gobler CJ, Burkholder JM, Davis TW, Harke MJ, Johengen T, Stow CA, et al. The dual role of nitrogen supply in controlling the growth and toxicity of cyanobacterial blooms. Harmful Algae. 2016;54:87-97. DOI: 10.1016/j.hal.2016.01.010
  3. 3. Ndlela LL, Oberholster PJ, Van Wyk JH, Cheng PH. An overview of cyanobacterial bloom occurrences and research in Africa over the last decade. Harmful Algae. 2016;60:11-26. DOI: 10.1016/j.hal.2016.10.001
  4. 4. Miller TR, Weirich C, Bartlett S. Cyanobacterial toxins of the laurentian great lakes, their toxicological effects, and numerical limits in drinking water. Marine Drugs. 2017;15:160. DOI: 10.3390/md15060160
  5. 5. Paerl HW, Huisman J. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environmental Microbiology Reports. 2009;1:27-37. DOI: 10.1111/j.1758-2229.2008.00004.x
  6. 6. O’Neil JM, Davis TW, Burford MA, Gobler CJ. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae. 2012;14:313-334. DOI: 10.1016/j.hal.2011.10.027
  7. 7. Paerl HW, Otten TG. Harmful cyanobacterial blooms: Causes, consequences, and controls. Microbial Ecology. 2013;65:995-1010. DOI: 10.1007/s00248-012-0159-y
  8. 8. Gehringer MM, Wannicke N. Climate change and regulation of hepatotoxin production in Cyanobacteria. FEMS Microbiology Ecology. 2014;88:1-25. DOI: 10.1111/1574-6941.12291
  9. 9. Botana LM. Toxicological perspective on climate change: Aquatic toxins. Chemical Research in Toxicology. 2016;29:619-625. DOI: 10.1021/acs.chemrestox.6b00020
  10. 10. Bullerjahn GS, McKay RM, Davis TW, Baker DB, Boyer GL, D’Anglada LV, et al. Global solutions to regional problems: Collecting global expertise to address the problem of harmful cyanobacterial blooms. A Lake Erie case study. Harmful Algae. 2016;54:223-238. DOI: 10.1016/j.hal.2016.01.003
  11. 11. Paerl HW. Nuisance phytoplankton blooms in coastal, estuarine, and inland waters1. Limnology and Oceanography. 1988;33:823-843. DOI: 10.4319/lo.1988.33.4part2.0823
  12. 12. Watson SB, McCauley E, Downing JA. Patterns in phytoplankton taxonomic composition across temperate lakes of differing nutrient status. Limnology and Oceanography. 1997;42:487-495. DOI: 10.4319/lo.1997.42.3.0487
  13. 13. Funari E, Testai E. Human health risk assessment related to cyanotoxins exposure. Critical Reviews in Toxicology. 2008;38:97-125. DOI: 10.1080/10408440701749454
  14. 14. Hilborn E, Beasley V. One health and cyanobacteria in freshwater systems: Animal illnesses and deaths are sentinel events for human health risks. Toxins. 2015;7:1374-1395. DOI: 10.3390/toxins7041374
  15. 15. Carmichael WW, Boyer GL. Health impacts from cyanobacteria harmful algae blooms: Implications for the North American Great Lakes. Harmful Algae. 2016;54:194-212. DOI: 10.1016/j.hal.2016.02.002
  16. 16. Bouaïcha N, Corbel S. Cyanobacterial toxins emerging contaminants in soils: A review of sources, fate and impacts on ecosystems, plants and animal and human health. In: Larramendy ML, Soloneski S, editors. Soil Contam. Curr. Consequences Furth. Solut. Rijeka: InTech; 2016. DOI: 10.5772/64940
  17. 17. Buratti FM, Manganelli M, Vichi S, Stefanelli M, Scardala S, Testai E, et al. Cyanotoxins: Producing organisms, occurrence, toxicity, mechanism of action and human health toxicological risk evaluation. Archives of Toxicology. 2017;91:1049-1130. DOI: 10.1007/s00204-016-1913-6
  18. 18. Dolman AM, Rücker J, Pick FR, Fastner J, Rohrlack T, Mischke U, et al. Cyanobacteria and cyanotoxins: The influence of nitrogen versus phosphorus. PLoS One. 2012;7:e38757. DOI: 10.1371/journal.pone.0038757
  19. 19. Kosten S, Huszar VLM, Bécares E, Costa LS, van Donk E, Hansson L-A, et al. Warmer climates boost cyanobacterial dominance in shallow lakes. Global Change Biology. 2012;18:118-126. DOI: 10.1111/j.1365-2486.2011.02488.x
  20. 20. Beaulieu M, Pick F, Gregory-Eaves I. Nutrients and water temperature are significant predictors of cyanobacterial biomass in a 1147 lakes data set. Limnology and Oceanography. 2013;58:1736-1746. DOI: 10.4319/lo.2013.58.5.1736
  21. 21. Yuan LL, Pollard AI, Pather S, Oliver JL, D’Anglada L. Managing microcystin: Identifying national-scale thresholds for total nitrogen and chlorophyll a. Freshwater Biology. 2014;59:1970-1981. DOI: 10.1111/fwb.12400
  22. 22. Orr PT, Jones GJ. Relationship between microcystin production and cell division rates in nitrogen-limited Microcystis aeruginosa cultures. Limnology and Oceanography. 1998;43:1604-1614. DOI: 10.4319/lo.1998.43.7.1604
  23. 23. Vezie C, Rapala J, Vaitomaa J, Seitsonen J, Sivonen K. Effect of nitrogen and phosphorus on growth of toxic and nontoxic microcystis strains and on intracellular microcystin concentrations. Microbial Ecology. 2002;43:443-454. DOI: 10.1007/s00248-001-0041-9
  24. 24. Davis TW, Harke MJ, Marcoval MA, Goleski J, Orano-Dawson C, Berry DL, et al. Effects of nitrogenous compounds and phosphorus on the growth of toxic and non-toxic strains of microcystis during cyanobacterial blooms. Aquatic Microbial Ecology. 2010;61:149-162. DOI: 10.3354/ame01445
  25. 25. Monchamp M-E, Pick FR, Beisner BE, Maranger R. Nitrogen forms influence microcystin concentration and composition via changes in cyanobacterial community structure. PLoS One. 2014;9:e85573. DOI: 10.1371/journal.pone.0085573
  26. 26. Ferber LR, Levine SN, Lini A, Livingston GP. Do cyanobacteria dominate in eutrophic lakes because they fix atmospheric nitrogen? Freshwater Biology. 2004;49:690-708. DOI: 10.1111/j.1365-2427.2004.01218.x
  27. 27. McCarthy MJ, James RT, Chen Y, East TL, Gardner WS. Nutrient ratios and phytoplankton community structure in the large, shallow, eutrophic, subtropical Lakes Okeechobee (Florida, USA) and Taihu (China). Limnology. 2009;10:215-227. DOI: 10.1007/s10201-009-0277-5
  28. 28. Chaffin JD, Bridgeman TB, Heckathorn SA, Mishra S. Assessment of microcystis growth rate potential and nutrient status across a trophic gradient in western Lake Erie. Journal of Great Lakes Research. 2011;37:92-100. DOI: 10.1016/j.jglr.2010.11.016
  29. 29. Glibert PM, Wilkerson FP, Dugdale RC, Raven JA, Dupont CL, Leavitt PR, et al. Pluses and minuses of ammonium and nitrate uptake and assimilation by phytoplankton and implications for productivity and community composition, with emphasis on nitrogen-enriched conditions. Limnology and Oceanography. 2016;61:165-197. DOI: 10.1002/lno.10203
  30. 30. Blomqvist P, Pettersson A, Hyenstrand P. Ammonium-nitrogen-Akey regulatory factor causing dominance of non-nitrogen-fixing cyanobacteria in aquatic systems. Archiv für Hydrobiologie. 1994;132:141-164
  31. 31. Donald DB, Bogard MJ, Finlay K, Bunting L, Leavitt PR. Phytoplankton-specific response to enrichment of phosphorus-rich surface waters with ammonium, nitrate, and urea. PLoS One. 2013;8:e53277. DOI: 10.1371/journal.pone.0053277
  32. 32. Chaffin JD, Davis TW, Smith DJ, Baer MM, Dick GJ. Interactions between nitrogen form, loading rate, and light intensity on Microcystis and Planktothrix growth and microcystin production. Harmful Algae. 2018;73:84-97. DOI: 10.1016/j.hal.2018.02.001
  33. 33. Zhang H, Culver DA, Boegman L. A two-dimensional ecological model of Lake Erie: Application to estimate dreissenid impacts on large lake plankton populations. Ecological Modelling. 2008;214:219-241. DOI: 10.1016/j.ecolmodel.2008.02.005
  34. 34. Harke MJ, Gobler CJ. Daily transcriptome changes reveal the role of nitrogen in controlling microcystin synthesis and nutrient transport in the toxic cyanobacterium, Microcystis aeruginosa. BMC Genomics. 2015;16:1068. DOI: 10.1186/s12864-015-2275-9
  35. 35. Markou G, Vandamme D, Muylaert K. Microalgal and cyanobacterial cultivation: The supply of nutrients. Water Research. 2014;65:186-202. DOI: 10.1016/j.watres.2014.07.025
  36. 36. Oliver RL, Hamilton DP, Brookes JD, Ganf GG. Physiology, blooms and prediction of planktonic cyanobacteria. In: Whitton BA, editor. Ecol. Cyanobacteria II Their Divers. Space Time. Dordrecht: Springer Netherlands; 2012. pp. 155-194. DOI: 10.1007/978-94-007-3855-3_6
  37. 37. Molot LA, Watson SB, Creed IF, Trick CG, McCabe SK, Verschoor MJ, et al. A novel model for cyanobacteria bloom formation: The critical role of anoxia and ferrous iron. Freshwater Biology. 2014;59:1323-1340. DOI: 10.1111/fwb.12334
  38. 38. Šejnohová L, Maršálek B. Microcystis. In: Whitton BA, editor. Ecology of Cyanobacteria II: Their Diversity in Space and Time. Netherlands, Dordrecht: Springer Science+Business Media B.V.; 2012. pp. 195-228.
  39. 39. Xu H, Paerl HW, Qin B, Zhu G, Hall NS, Wu Y. Determining Critical nutrient thresholds needed to control harmful cyanobacterial blooms in Eutrophic Lake Taihu, China. Environmental Science & Technology. 2015;49:1051-1059. DOI: 10.1021/es503744q
  40. 40. Huang L, Fang H, He G, Jiang H, Wang C. Effects of internal loading on phosphorus distribution in the Taihu Lake driven by wind waves and lake currents. Environmental Pollution. 2016;219:760-773. DOI: 10.1016/j.envpol.2016.07.049
  41. 41. Harke MJ, Davis TW, Watson SB, Gobler CJ. Nutrient-controlled niche differentiation of western lake erie cyanobacterial populations revealed via metatranscriptomic surveys. Environmental Science & Technology. 2016;50:604-615. DOI: 10.1021/acs.est.5b03931
  42. 42. Li J, Hansson L-A, Persson K. Nutrient control to prevent the occurrence of cyanobacterial blooms in a eutrophic lake in Southern Sweden, used for drinking water supply. Water. 2018;10:919. DOI: 10.3390/w10070919
  43. 43. Jeppesen E, Kronvang B, Meerhoff M, Søndergaard M, Hansen KM, Andersen HE, et al. Climate change effects on runoff, catchment phosphorus loading and lake ecological state, and potential adaptations. Journal of Environmental Quality. 2009;38:1930-1941. DOI: 10.2134/jeq2008.0113
  44. 44. Downing JA, McCauley E. The nitrogen:phosphorus relationship in lakes. Limnology and Oceanography. 1992;37:936-945. DOI: 10.4319/lo.1992.37.5.0936
  45. 45. Paerl HW, Paul VJ. Climate change: Links to global expansion of harmful cyanobacteria. Water Research. 2012;46:1349-1363. DOI: 10.1016/j.watres.2011.08.002
  46. 46. Davis TW, Berry DL, Boyer GL, Gobler CJ. The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae. 2009;8:715-725. DOI: 10.1016/j.hal.2009.02.004
  47. 47. Moss B. Allied attack: Climate change and eutrophication. Inland Waters. 2011;1:101-105. DOI: 10.5268/IW-1.2.359
  48. 48. Mowe MAD, Mitrovic SM, Lim RP, Furey A, Yeo DCJ. Tropical cyanobacterial blooms: A review of prevalence, problem taxa, toxins and influencing environmental factors. Journal of Limnology. 2015;74:205-224. DOI: 10.4081/jlimnol.2014.1005
  49. 49. Wang W-C. Effect of turbidity on algal. Growth. Circular 121. State of Illinois, Department of Registration and Education. 1974. pp. 1-12. ISWS-74-CIR121
  50. 50. Whitton BA, Potts M. Introduction to the Cyanobacteria. In: Whitton BA, editor. Ecol. Cyanobacteria II Their Divers. Space Time. Dordrecht: Springer Netherlands; 2012. pp. 1-13. DOI: 10.1007/978-94-007-3855-3_1
  51. 51. Oliver RL, Walsby AE. Direct evidence for the role of light mediated gas vesicle collapse in the buoyancy regulation of Anabaena flos-aquae (cyanobacteria). Limnology and Oceanography. 1984;29:879-886
  52. 52. Dokulil MT, Teubner K. Cyanobacterial dominance in lakes. Hydrobiologia. 2000;438:1-12. DOI: 10.1023/A:1004155810302
  53. 53. Paerl HW, Gardner WS, Havens KE, Joyner AR, McCarthy MJ, Newell SE, et al. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae. 2016;54:213-222. DOI: 10.1016/j.hal.2015.09.009
  54. 54. Fulton RS, Paerl HW. Effects of the blue-green alga Microcystis aeruginosa on zooplankton competitive relations. Oecologia. 1988;76:383-389. DOI: 10.1007/BF00377033
  55. 55. Lampert W. Laboratory studies on zooplankton-cyanobacteria interactions. New Zealand Journal of Marine and Freshwater Research. 1987;21:483-490. DOI: 10.1080/00288330.1987.9516244
  56. 56. Wilson AE, Sarnelle O, Tillmanns AR. Effects of cyanobacterial toxicity and morphology on the population growth of freshwater zooplankton: Meta-analyses of laboratory experiments. Limnology and Oceanography. 2006;51:1915-1924. DOI: 10.4319/lo.2006.51.4.1915
  57. 57. Figueiredo MAT, Nowak RD, Wright SJ. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing. 2007;1:586-597. DOI: 10.1109/JSTSP.2007.910281
  58. 58. Antunes JT, Leão PN, Vasconcelos VM. Influence of biotic and abiotic factors on the allelopathic activity of the cyanobacterium Cylindrospermopsis raciborskii strain LEGE 99043. Microbial Ecology. 2012;64:584-592. DOI: 10.1007/s00248-012-0061-7
  59. 59. Azevêdo DJS, Barbosa JEL, Porto DE, Gomes WIA, Molozzi J. Biotic or abiotic factors: Which has greater influence in determining the structure of rotifers in semi-arid reservoirs? Acta Limnologica Brasiliensia. 2015;27:60-77. DOI: 10.1590/S2179-975X2914
  60. 60. Wilson H, Recknagel F. Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes. Ecological Modelling. 2001;146(1-3):69-84
  61. 61. Wei B, Sugiura N, Maekawa T. Use of artificial neural network in the prediction of algal blooms. Water Research. 2001;35(8):2022-2028
  62. 62. Park Y, Cho KH, Park J, Cha SM, Kim JH. Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea. Science of the Total Environment. 2015;502:31-41
  63. 63. Lu F, Chen Z, Liu W, Shao H. Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin. Ecological Engineering. 2016;95:422-429
  64. 64. Tian W, Liao Z, Zhang J. An optimization of artificial neural network model for predicting chlorophyll dynamics. Ecological Modelling. 2017;364:42-52
  65. 65. Liu J, Zhang Y, Qian X. Modeling chlorophyll-a in Taihu Lake with machine learning models. In: Proceedings of 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE). Beijing, China; 2009. pp. 1-6
  66. 66. Liu Z, Wang X, Cui L, Lian X, Xu J. Research on water bloom prediction based on least squares support vector machine. In: WRI World Congress on Computer Science and Information Engineering. Vol. 5. Los Angeles, California, USA: IEEE; 2009. pp. 764-768
  67. 67. Xie Z, Lou I, Ung WK, Mok KM. Freshwater algal bloom prediction by support vector machine in Macau storage reservoirs. Mathematical Problems in Engineering. 2012;2012:1-12. Article ID: 397473.
  68. 68. Bobbin J, Recknagel F. Inducing explanatory rules for the prediction of algal blooms by genetic algorithms. Environment International. 2001;27(2-3):237-242
  69. 69. Sivapragasam C, Muttil N, Muthukumar S, Arun VM. Prediction of algal blooms using genetic programming. Marine Pollution Bulletin. 2010;60(10):1849-1855
  70. 70. Fernández JA, Muñiz CD, Nieto PG, de Cos Juez FJ, Lasheras FS, Roqueñí MN. Forecasting the cyanotoxins presence in fresh waters: A new model based on genetic algorithms combined with the MARS technique. Ecological Engineering. 2013;53:68-78
  71. 71. Nelson NG, Muñoz-Carpena R, Phlips EJ, Kaplan D, Sucsy P, Hendrickson J. Revealing biotic and abiotic controls of harmful algal blooms in a shallow subtropical lake through statistical machine learning. Environmental Science & Technology. 2018;52(6):3527-3535
  72. 72. Kromkamp J, Walsby AE. A computer model of buoyancy and vertical migration in cyanobacteria. Journal of Plankton Research. 1990;12:161-183. DOI: 10.1093/plankt/12.1.161
  73. 73. Webster IT. Effect of wind on the distribution of phytoplankton cells in lakes. Limnology and Oceanography. 1990;35:989-1001. DOI: 10.4319/lo.1990.35.5.0989
  74. 74. Verhagen JHG. Modeling phytoplankton patchiness under the influence of wind-driven currents inlakes. Limnology and Oceanography. 1994;39:1551-1565. DOI: 10.4319/lo.1994.39.7.1551
  75. 75. Webster IT, Hutchinson PA. Effect of wind on the distribution of phytoplankton cells in lakes revisited. Limnology and Oceanography. 1994;39:365-373. DOI: 10.4319/lo.1994.39.2.0365
  76. 76. Visser PM, Passarge J, Mur LR. Modelling vertical migration of the cyanobacterium Microcystis. Hydrobiologia. 1997;349:99-109. DOI: 10.1023/A:1003001713560
  77. 77. Wallace BB, Hamilton DP. Simulation of water-bloom formation in the cyanobacterium Microcystis aeruginosa. Journal of Plankton Research. 2000;22:1127-1138. DOI: 10.1093/plankt/22.6.1127
  78. 78. Porat R, Teltsch B, Perelman A, Dubinsky Z. Diel buoyancy changes by the cyanobacterium Aphanizomenon ovalisporum from a shallow reservoir. Journal of Plankton Research. 2001;23:753-763. DOI: 10.1093/plankt/23.7.753
  79. 79. Bruggeman J, Bolding K. A general framework for aquatic biogeochemical models. Environmental Modelling & Software. 2014;61:249-265. DOI: 10.1016/j.envsoft.2014.04.002
  80. 80. Ndong M, Bird D, Nguyen Quang T, Kahawita R, Hamilton D, de Boutray ML, et al. A novel Eulerian approach for modelling cyanobacteria movement: Thin layer formation and recurrent risk to drinking water intakes. Water Research. 2017;127:191-203. DOI: 10.1016/j.watres.2017.10.021
  81. 81. Feng T, Wang C, Wang P, Qian J, Wang X. How physiological and physical processes contribute to the phenology of cyanobacterial blooms in large shallow lakes: A new Euler-Lagrangian coupled model. Water Research. 2018;140:34-43. DOI: 10.1016/j.watres.2018.04.018
  82. 82. Li Y, Zhang Q, Ye R, Yao J, Tan Z. 3D hydrodynamic investigation of thermal regime in a large river-lake-floodplain system (Poyang Lake, China). Journal of Hydrology. 2018;567:86-101. DOI: 10.1016/j.jhydrol.2018.10.007
  83. 83. Pyo J, Ligaray M, Kwon Y, Ahn M-H, Kim K, Lee H, et al. High-spatial resolution monitoring of phycocyanin and chlorophyll-a using airborne hyperspectral imagery. Remote Sensing. 2018;10:1180. DOI: 10.3390/rs10081180
  84. 84. Codd GA, Azevedo SMFO, Bagchi SN, Burch MD, Carmichael WW, Harding WR, et al. CYANONET a global network for cyanobacterial bloom and toxin risk management. Initial situation assessment and recommendations. IHP-VI Technical Document in Hydrology. 2005;76. UNESCO Working Series SC-2005/WS/55
  85. 85. Blaha L, Babica P, Marsalek B. Toxins produced in cyanobacterial water blooms—Toxicity and risks. Interdisciplinary Toxicology. 2009;2(2):36-41. DOI: 10.2478/v10102-009-0006-2
  86. 86. Meriluo J, Blaha L, Bojadzija G, Bormans M, Brient L, Codd GA, et al. Toxic cyanobacteria and cyanotoxins in European waters—Recent progress achieved through the CYANOCOST. Action and challenges for further research. Advances in Oceanography and Limnology. 2017;8(1):161-178. DOI: 10.4081/aiol.2017.6429AR
  87. 87. Schanz F, Allen ED, Gorham PR. Bioassay of the seasonal ability of water from a eutrophic Alberta lake to promote selective growth of strains of Anabaena flos-aquaeand other blue-green algae. Canadian Journal of Botany. 1979;57(21):2443-2451. DOI: 10.1139/b79-288
  88. 88. Pick FR. Blooming algae: A Canadian perspective on the rise of toxic cyanobacteria. Canadian Journal of Fisheries and Aquatic Sciences. 2016;73:1149-1158
  89. 89. Nguyen-Quang T, Fraser H, Hushchyna K, McLellan K, Sabir Q. Continuation of the systematic study (2nd year – 2017) of toxic algal blooms in McLaughlin and Irishtown Reservoirs to add to the water quality program of the City of Moncton—New Brunswick. Technical Report submitted to Government of New Brunswick, Environmental Trust Fund, Environmental and Local Government, Canada. 2018
  90. 90. Svirčev Z, Simeunović J, Subakov-Simić G, Krstić S, Vidović M. Freshwater cyanobacterial blooms and cyanotoxin production in serbia in the past 25 years. Geographica Panonica. 2007;11:32-38
  91. 91. Corbel S, Mougin C, Bouaïcha N. Cyanobacterial toxins: Modes of actions, fate in aquatic and soil ecosystems, phytotoxicity and bioaccumulation in agricultural crops. Chemosphere. 2014;96:1-15. DOI: 10.1016/j.chemosphere.2013.07.056
  92. 92. Merel S, Walker D, Chicana R, Snyder S, Baurès E, Thomas O. State of knowledge and concerns on cyanobacterial blooms and cyanotoxins. Environment International. 2013;59:303-327. DOI: 10.1016/j.envint.2013.06.013
  93. 93. Antunes JT, Leão PN, Vasconcelos VM. Cylindrospermopsis raciborskii: Review of the distribution, phylogeography, and ecophysiology of a global invasive species. Frontiers in Microbiology. 2015;6:1-12. DOI: 10.3389/fmicb.2015.00473
  94. 94. Burford MA, Beardall J, Willis A, Orr PT, Magalhaes VF, Rangel LM, et al. Understanding the winning strategies used by the bloom-forming cyanobacterium Cylindrospermopsis raciborskii. Harmful Algae. 2016;54:44-53. DOI: 10.1016/j.hal.2015.10.012
  95. 95. Preece EP, Hardy FJ, Moore BC, Bryan M. A review of microcystin detections in Estuarine and Marine waters: Environmental implications and human health risk. Harmful Algae. 2017;61:31-45. DOI: 10.1016/j.hal.2016.11.006
  96. 96. Sivonen K, Jones G-J. Cyanobacterial toxins. In: Chorus I, Bartram J, editors. Toxiccyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring and Management. London and New York: World Health Organization, Taylor and Francis; 1999. pp. 41-111
  97. 97. Saker ML, Griffiths DJ. The effect of temperature on growth and cylindrospermopsin content of seven isolates of Cylindrospermopsis raciborskii (Nostocales, Cyanophyceae) from water bodies in northern Australia. Phycologia. 2000;39:349-354. DOI: 10.2216/i0031-8884-39-4-349.1
  98. 98. Pham T-L, Utsumi M. An overview of the accumulation of microcystins in aquatic ecosystems. Journal of Environmental Management. 2018;213:520-529. DOI: 10.1016/j.jenvman.2018.01.077
  99. 99. Kemp A, John J. Microcystins associated with Microcystis dominated blooms in the Southwest wetlands, Western Australia. Environmental Toxicology. 2006;21:125-130. DOI: 10.1002/tox.20164
  100. 100. Oberholster PJ, Myburgh JG, Govender D, Bengis R, Botha AM. Identification of toxigenic Microcystis strains after incidents of wild animal mortalities in the Kruger National Park, South Africa. Ecotoxicology and Environmental Safety. 2009;72:1177-1182
  101. 101. Nasri A-B, Bouaıcha N, Fastner J. First report of a Microcystin-containing bloom of the cyanobacteria Microcystis spp. in Lake Oubeira, Eastern Algeria. Archives of Environmental Contamination and Toxicology. 2004;46:197-202. DOI: 10.1007/s00244-003-2283-7
  102. 102. Wood SA, Holland PT, Stirling DJ, Briggs LR, Sprosen J, Ruck JG, et al. Survey of cyanotoxins in New Zealand water bodies between 2001 and 2004. New Zealand Journal of Marine and Freshwater Research. 2006;40:585-597. DOI: 10.1080/00288330.2006.9517447
  103. 103. Messineo V, Bogialli S, Melchiorre S, Sechi N, Lugliè A, Casiddu P, et al. Cyanobacterial toxins in Italian freshwaters. Limnologica. 2009;39:95-106. DOI: 10.1016/j.limno.2008.09.001
  104. 104. Rapala J, Berg KA, Lyra C, Niemi RM, Manz W, et al. Paucibacter toxinivorans gen. nov., sp. nov., a bacterium that degrades cyclic cyanobacterial hepatotoxins microcystins and nodularin. International Journal of Systematic and Evolutionary Microbiology. 2005;55:1563-1568. DOI: 10.1099/ijs.0.63599-0
  105. 105. Pawlik-skowronska B, Toporowska M. Blooms of toxin-producing Cyanobacteria—A real threat in small dam reservoirs at the beginning of their operation. Oceanological and Hydrobiological Studies. 2011;40:30-37. DOI: 10.2478/s13545-011-0038-z
  106. 106. Roy-Lachapelle A, Solliec M, Sauvé S. Determination of BMAA and three alkaloid cyanotoxins in lake water using dansyl chloride derivatization and high-resolution mass spectrometry. Analytical and Bioanalytical Chemistry. 2015;407:5487-5501. DOI: 10.1007/s00216-015-8722-2
  107. 107. Francis G. Poisonous Australian lake. Nature. 1878;18:11
  108. 108. Duy TN, Lam PKS, Shaw GR, Connell DW. Toxicology and risk assessment of freshwater cyanobacterial (Blue-Green Algal) toxins in water. In: Ware GW, editor. Rev. Environ. Contam. Toxicol. Contin. Residue Rev., New York, NY: Springer New York; 2000. pp. 113-185. DOI: 10.1007/978-1-4757-6429-1_3
  109. 109. Landsberg JH. The effects of harmful algal blooms on aquatic organisms. Reviews in Fisheries Science. 2002;10:113-390. DOI: 10.1080/20026491051695
  110. 110. Stewart I, Seawright AA, Shaw GR. Cyanobacterial poisoning in livestock, wild mammals and birds—An overview. In: Hudnell HK, editor. Cyanobacterial Harmful Algal Blooms State Sci. Res. Needs, New York, NY: Springer New York; 2008. pp. 613-637. DOI: 10.1007/978-0-387-75865-7_28
  111. 111. Wood R. Acute animal and human poisonings from cyanotoxin exposure—A review of the literature. Environment International. 2016;91:276-282. DOI: 10.1016/j.envint.2016.02.026
  112. 112. Wiegand C, Pflugmacher S. Ecotoxicological effects of selected cyanobacterial secondary metabolites a short review. Toxicology and Applied Pharmacology. 2005;203:201-218. DOI: 10.1016/j.taap.2004.11.002
  113. 113. Zanchett G, Oliveira-Filho E. Cyanobacteria and cyanotoxins: From impacts on aquatic ecosystems and human health to anticarcinogenic effects. Toxins. 2013;5:1896-1917. DOI: 10.3390/toxins5101896
  114. 114. Bownik A. Harmful algae: Effects of cyanobacterial cyclic peptides on aquatic invertebrates—A short review. Toxicon. 2016;124:26-35. DOI: 10.1016/j.toxicon.2016.10.017
  115. 115. Ferrão-Filho AS, Kozlowsky-Suzuki B. Cyanotoxins: Bioaccumulation and effects on aquatic animals. Marine Drugs. 2011;9:2729-2772. DOI: 10.3390/md9122729
  116. 116. Vasconcelos JF, Barbosa JEL, Lira W, Azevedo SMFO. Microcystin bioaccumulation can cause potential mutagenic effects in farm fish. Egyptian Journal of Aquatic Research. 2013;39:185-192. DOI: 10.1016/j.ejar.2013.11.002
  117. 117. Vilar MCP, de Araújo-Castro CMV, Moura ADN. Acute toxicity of Microcystis spp. (Cyanobacteria) bloom on Moina minuta (Cladocera) in a tropical reservoir, Northeastern Brazil. Ecotoxicology and Environmental Contamination. 2014;9:93-98. DOI: 10.5132/eec.2014.01.012
  118. 118. Montagnolli W, Zamboni A, Luvizotto-Santos R, Yunes JS. Acute effects of Microcystis aeruginosa from the Patos Lagoon Estuary, Southern Brazil, on the Microcrustacean Kalliapseudes schubartii (Crustacea: Tanaidacea). Archives of Environmental Contamination and Toxicology. 2004;46:463-469. DOI: 10.1007/s00244-003-2304-6
  119. 119. An Z, Sun L, Wang P. Acute toxicity and accumulation of microcystin-leucine-arginine in the crayfish Procambarus clarkii (Girard, 1852). Crustaceana. 2015;88:397-404. DOI: 10.1163/15685403-00003424
  120. 120. Amorim Á, Vasconcelos V. Dynamics of microcystins in the mussel Mytilus galloprovincialis. Toxicon. 1999;37:1041-1052. DOI: 10.1016/S0041-0101(98)00231-1
  121. 121. de Oliveira Azavedo SMF, Carmouze J-P. Une mortalité de poissons dans une lagune tropicale (Brésil) durant une période de dominance de Cyanophyceae: Coïncidence ou consequence? Revue d'Hydrobiologie Tropicale. 1994;27:265-272
  122. 122. Jewel MA, Affan MA, Khan S. Fish mortality due to cyanobacterial bloom in an aquaculture pond in Bangladesh. Pakistan Journal of Biological Sciences. 2003;6:1046-1050
  123. 123. Ibelings BW, Havens KE. Cyanobacterial toxins: A qualitative meta–analysis of concentrations, dosage and effects in freshwater, estuarine and marine biota. In: Hudnell HK, editor. Cyanobacterial Harmful Algal Blooms State Sci. Res. Needs, New York, NY: Springer New York; 2008. pp. 675-732. DOI: 10.1007/978-0-387-75865-7_32
  124. 124. Kotak BG, Zurawell RW, Prepas EE, Holmes CF. Microcystin-LR concentration in aquatic food web compartments from lakes of varying trophic status. Canadian Journal of Fisheries and Aquatic Sciences. 1996;53:1974-1985. DOI: 10.1139/f96-135
  125. 125. Malbrouck C, Kestemont P. Effects of microcystins on fish. Environmental Toxicology and Chemistry. 2006;25:72. DOI: 10.1897/05-029R.1
  126. 126. Ferrão-Filho AdS, da Costa SM, Ribeiro MGL, Azevedo SMFO. Effects of a saxitoxin-producer strain of Cylindrospermopsis raciborskii (cyanobacteria) on the swimming movements of cladocerans. Environmental Toxicology. 2008;23:161-168. DOI: 10.1002/tox.20320
  127. 127. Osswald J, Rellán S, Gago A, Vasconcelos V. Toxicology and detection methods of the alkaloid neurotoxin produced by cyanobacteria, anatoxin-a. Environment International. 2007;33:1070-1089. DOI: 10.1016/j.envint.2007.06.003
  128. 128. Dao TS, Do-Hong L-C, Wiegand C. Chronic effects of cyanobacterial toxins on Daphnia magna and their offspring. Toxicon. 2010;55:1244-1254. DOI: 10.1016/j.toxicon.2010.01.014
  129. 129. Oberemm A, Fastner J, Steinberg CEW. Effects of microcystin-LR and cyanobacterial crude extracts on embryo-larval development of zebrafish (Danio rerio). Water Research. 1997;31:2918-2921. DOI: 10.1016/S0043-1354(97)00120-6
  130. 130. Oberemm A, Becker J, Codd GA, Steinberg C. Effects of cyanobacterial toxins and aqueous crude extracts of cyanobacteria on the development of fish and amphibians. Environmental Toxicology. 1999;14:77-88. DOI: 10.1002/(SICI)1522-7278(199902)14:1<77::AID-TOX11>3.0.CO;2-F
  131. 131. Liu Y, Song L, Li X, Liu T. The toxic effects of microcystin-LR on embryo-larval and juvenile development of loach, Misgurunsmizolepis gunthe. Toxicon. 2002;40:395-399. DOI: 10.1016/S0041-0101(01)00173-8
  132. 132. Palikova M, Navratil S, Marsalek B, Blaha L. Toxicity of crude extract ofcyanobacteria for embryos and larvae of carp (Cyprinus carpio L.). Acta Veterinaria Brunensis. 2003;72(3):437-443
  133. 133. Ernst B, Hitzfeld B, Dietrich D. Presence of Planktothrix sp. and cyanobacterial toxins in Lake Ammersee, Germany and their impact on whitefish (Coregonus lavaretus L.). Environmental Toxicology. 2001;16:483-488. DOI: 10.1002/tox.10006
  134. 134. Li X-Y, Chung I-K, Kim J-I, Lee J-A. Subchronic oral toxicity of microcystin in common carp (Cyprinus carpio L.) exposed to Microcystis under laboratory conditions. Toxicon. 2004;44:821-827. DOI: 10.1016/j.toxicon.2004.06.010
  135. 135. Esterhuizen-Londt M, von Schnehen M, Kühn S, Pflugmacher S. Oxidative stress responses in the animal model, Daphnia pulex exposed to a natural bloom extract versus artificial cyanotoxin mixtures. Aquatic Toxicology. 2016;179:151-157. DOI: 10.1016/j.aquatox.2016.09.003
  136. 136. Dvorakova D, Dvorakova K, Blaha L, Marsalek B, Knotkova Z. Effects of cyanobacterial biomass and purified microcystins on malformations in Xenopus laevis: Teratogenesis assay (FETAX). Environmental Toxicology. 2002;17:547-555
  137. 137. Burýšková B, Hilscherová K, Babica P, Vršková D, Maršálek B, Bláha L. Toxicity of complex cyanobacterial samples and their fractions in Xenopus laevis embryos and the role of microcystins. Aquatic Toxicology. 2006;80:346-354. DOI: 10.1016/j.aquatox.2006.10.001
  138. 138. Osswald J, Carvalho AP, Claro J, Vasconcelos V. Effects of cyanobacterial extracts containing anatoxin-a and of pure anatoxin-a on early developmental stages of carp. Ecotoxicology and Environmental Safety. 2009;72:473-478. DOI: 10.1016/j.ecoenv.2008.05.011
  139. 139. Cerbin S, Kraak MHS, de Voogt P, Visser PM, Van Donk E. Combined and single effects of pesticide carbaryl and toxic Microcystis aeruginosa on the life history of Daphnia pulicaria. Hydrobiologia. 2010;643:129-138. DOI: 10.1007/s10750-010-0130-1
  140. 140. Cazenave J, Bistoni M d l Á, Zwirnmann E, Wunderlin DA, Wiegand C. Attenuating effects of natural organic matter on microcystin toxicity in zebra fish (Danio rerio) embryos—Benefits and costs of microcystin detoxication. Environmental Toxicology. 2006;21:22-32. DOI: 10.1002/tox.20151
  141. 141. Magalhães VF, Marinho MM, Domingos P, Oliveira AC, Costa SM, Azevedo LO, et al. Microcystins (cyanobacteria hepatotoxins) bioaccumulation in fish and crustaceans from Sepetiba Bay (Brasil, RJ). Toxicon. 2003;42:289-295. DOI: 10.1016/S0041-0101(03)00144-2
  142. 142. Smith JL, Haney JF. Foodweb transfer, accumulation, and depuration of microcystins, a cyanobacterial toxin, in pumpkinseed sunfish (Lepomis gibbosus). Toxicon. 2006;48:580-589. DOI: 10.1016/j.toxicon.2006.07.009
  143. 143. Papadimitriou T, Kagalou I, Stalikas C, Pilidis G, Leonardos ID. Assessment of microcystin distribution and biomagnification in tissues of aquatic food web compartments from a shallow lake and evaluation of potential risks to public health. Ecotoxicology. 2012;21:1155-1166. DOI: 10.1007/s10646-012-0870-y
  144. 144. Liu L-P, Su X-M, Chen T-Y, Li K, Zhan J, Egna H, et al. Evidence of rapid transfer and bioaccumulation of Microcystin-LR poses potential risk to freshwater prawn Macrobrachiumro senbergii (de Man). Aquaculture Research. 2016;47:3088-3097. DOI: 10.1111/are.12759
  145. 145. Prepas EE, Kotak BG, Campbell LM, Evans JC, Hrudey SE, Holmes CF. Accumulation and elimination of cyanobacterial hepatotoxins by the freshwater clam Anodontagrandis simpsoniana. Canadian Journal of Fisheries and Aquatic Sciences. 1997;54:41-46. DOI: 10.1139/f96-261
  146. 146. Ibelings BW, Bruning K, de Jonge J, Wolfstein K, Pires LMD, Postma J, et al. Distribution of microcystins in a lake foodweb: No evidence for biomagnification. Microbial Ecology. 2005;49:487-500. DOI: 10.1007/s00248-004-0014-x
  147. 147. Xie L, Xie P, Guo L, Li L, Miyabara Y, Park H-D. Organ distribution and bioaccumulation of microcystins in freshwater fish at different trophic levels from the eutrophic Lake Chaohu, China. Environmental Toxicology. 2005;20:293-300. DOI: 10.1002/tox.20120
  148. 148. Matsunaga H, Harada K-I, Senma M, Ito Y, Yasuda N, Ushida S, et al. Possible cause of unnatural mass death of wild birds in a pond in Nishinomiya, Japan: Sudden appearance of toxic cyanobacteria. Natural Toxins. 1999;7:81-84. DOI: 10.1002/(SICI)1522-7189(199903/04)7:2<81::AID-NT44>3.0.CO;2-O
  149. 149. Chen J, Xie P, Li L, Xu J. First identification of the hepatotoxic microcystins in the serum of a chronically exposed human population together with indication of hepatocellular damage. Toxicological Sciences. 2009;108:81-89. DOI: 10.1093/toxsci/kfp009
  150. 150. Chen J, Zhang D, Xie P, Wang Q, Ma Z. Simultaneous determination of microcystin contaminations in various vertebrates (fish, turtle, duck and water bird) from a large eutrophic Chinese lake, Lake Taihu, with toxic Microcystis blooms. Science of the Total Environment. 2009;407:3317-3322. DOI: 10.1016/j.scitotenv.2009b.02.005

Written By

Naila-Yasmine Benayache, Tri Nguyen-Quang, Kateryna Hushchyna, Kayla McLellan, Fatima-Zohra Afri-Mehennaoui and Noureddine Bouaïcha

Submitted: 03 December 2018 Reviewed: 04 January 2019 Published: 03 April 2019