Identifying the factors determining blooms of cyanobacteria in a set of shallow lakes

Seasonal and spatial variations of greenhouse gas (CO2, CH4 and N2O) emissions from urban ponds in Brussels

Freshwaters have been recognized as important sources of greenhouse gases (GHG) to the atmosphere. However, urban ponds have received little attention even though their number is increasing due to expanding urbanisation globally. Ponds are frequently associated to urban green spaces that provide several ecosystemic services such as cooling local climate, regulating the water cycle, and acting as small carbon sinks This study aims to identify and understand the processes producing GHGs (CO2, CH4, and N2O) in the urban ponds of the temperate European city of Brussels in Belgium. 22 relatively small ponds (0.1-4.6 ha) surrounded by contrasted landscape (strictly urban, bordered by cropland or by forest), were sampled during four seasons in 2021-2022. The mean ± standard deviation was 3,667 ± 2,904 ppm for the partial pressure of CO2 (pCO2), 2,833 ± 4,178 nmol L-1 for CH4, and 273 ± 662% for N2O saturation level (%N2O). Relationships of GHGs with oxygen and water temperature suggest that biological processes controlled pCO2, CH4 concentration and%N2O. However, pCO2 was also controlled by external inputs as indicated by the higher values of pCO2 in the smaller ponds, more subject to external inputs than larger ones. The opposite was observed for CH4 concentration that was higher in larger ponds, closer to the forest in the city periphery, and with higher macrophyte cover. N2O concentrations, as well as dissolved inorganic nitrogen, were higher closer to the city center, where atmospheric nitrogen deposition was potentially higher. The total GHG emissions from the Brussels ponds were estimated to 1kT CO2-eq per year and were equivalent to the carbon sink of urban green spaces.

How do seasonal temperature variations influence interplay between toxic and non-toxic cyanobacterial blooms? Evidence from modeling and experimental data

Summer cyanobacterial blooms exhibit a dynamic interplay between toxic and non-toxic genotypes, significantly influencing the cyanotoxin levels within a lake. The challenge lies in accurately predicting these toxin concentrations due to the significant temporal fluctuations in the proportions of toxic and non-toxic genotypes. Typically, the toxic genotypes dominate during the early and late summer periods, while the non-toxic variants prevail in mid-summer. To dissect this phenomenon, we propose a model that accounts for the competitive interaction between toxic and non-toxic genotypes, as well as seasonal temperature variations. Our numerical simulations suggest that the optimal temperature of the toxic genotypes is lower than that of the optimal temperatures of the non-toxic counterparts. This difference of optimal temperature may potentially contribute to explain the dominance of toxic genotypes at the early and late summer periods, situation often observed in the field. Experimental data from the laboratory align qualitatively with our simulation results, enabling a better understanding of complex interplays between toxic and non-toxic cyanobacteria.

Strategies for regulating the intensity of different cyanobacterial blooms: Insights from the dynamics and stability of bacterioplankton communities

The occurrence of cyanobacterial blooms is increasing in frequency and magnitude due to climate change and human activities, which poses a direct threat to drinking water security. The impacts of abiotic and biotic factors on the development of blooms have been well studied; however, control strategies for different bloom intensities have rarely been explored from the perspective of the dynamics and stability of bacterioplankton communities. Here, a network analysis was used to investigate the interactions and stability of microbial communities during different periods of R. raciborskii bloom in an inland freshwater lake. The abundance and diversity of rare taxa were significantly higher than that of abundant taxa throughout the bloom cycle. At the pre-bloom (PB) stage, microbial interactions among the different bacterial groups were weak but strongly negatively correlated, indicating low robustness and weak disturbance resistance within the community. However, community stability was better, and microbial interactions became more complicated at the high-bloom (HB) and low-bloom (LB) stages. Interestingly, rare taxa were significantly responsible for community stability and connectivity despite their low relative abundance. The Mantel test revealed that Secchi depth (SD), orthophosphate (PO43--P), and dissolved oxygen (DO) were significantly positively correlated with abundant taxa, rare taxa and PB. DO was significantly positively correlated with HB, intermediate taxa, and rare taxa, while water temperature (WT), N/P and total nitrogen (TN) were significantly positively correlated with LB, abundant taxa, intermediate taxa, and rare taxa. These findings suggest that reducing the PO43--P concentration at the PB stage may be an effective approach to preventing the development of R. raciborskii blooms, while regulating rare taxa at the HB and LB stages may be a key factor in controlling R. raciborskii blooms.

Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning-based gamma test variable selection and empirical wavelet transform

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML) models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the USA. The proposed modelling framework was based on establishing a link between five water quality variables and the concentration of CBGA. For this purpose, artificial neural network (ANN), extreme learning machine (ELM), random forest regression (RFR), and random vector functional link (RVFL) are developed. First, the four models were developed using only water quality variables. Second, based on the results of the first, a new modelling strategy was introduced based on preprocessing signal decomposition. Hence, the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) were used for decomposing the water quality variables into several subcomponents, and the obtained intrinsic mode functions (IMFs) and multiresolution analysis (MRA) components were used as new input variables for the ML models. Results of the present investigation show that (i) using single models, good predictive accuracy was obtained using the RFR model exhibiting an R and NSE values of ≈0.914 and ≈0.833 for the first station, and ≈0.944 and ≈0.884 for the second station, while the others models, i.e., ANN, RVFL, and ELM, have failed to provide a good estimation of the CBGA; (ii) the decomposition methods have contributed to a significant improvement of the individual models performances; (iii) among the thee decomposition methods, the EMD was found to be superior to the VMD and EWT; and (iv) the ANN and RFR were found to be more accurate compared to the ELM and RVFL models, exhibiting high numerical performances with R and NSE values of approximately ≈0.983, ≈0.967, and ≈0.989 and ≈0.976, respectively.

New insights into cyanobacterial blooms and the response of associated microbial communities in freshwater ecosystems

Cyanobacterial blooms are important environmental problems in aquatic ecosystems. Researchers have found that cyanobacterial blooms cannot be completely prevented by controlling and/or eliminating pollutants (nutrients). Thus, more in-depth basic research on the mechanism of cyanobacterial blooms is urgently needed. Cyanobacteria, being primordial microorganisms, provide habitats and have various forms of interactions (reciprocity and competition) with microorganisms, thus having a significant impact on themselves. However, little is known about how environmental conditions and microbial communities in both water and sediment jointly affect cyanobacterial blooms or about the co-occurrence patterns and interactions of microbial communities. We investigated changes in environmental factors and microbial communities in water and sediment during different cyanobacterial blooms and revealed their interacting effects on cyanobacteria. Cyanobacteria had greater competitive and growth advantages than other microorganisms and had antagonistic and aggressive effects on them when resources (such as nutrients) were abundant. Furthermore, microbial networks from cyanobacterial degradation periods may be more complex and stable than those from bloom periods, with more positive links among the microbial networks, suggesting that microbial community structures strengthen interconnections with each other to degrade cyanobacteria. In addition, we found that sediment-enriched cyanobacteria play a key role in cyanobacterial blooms, and sediment microorganisms promote the nutrient release, further promoting cyanobacterial blooms in the water bodies. The study contributes to further our understanding of the mechanisms for cyanobacterial blooms and microbial community structural composition, co-occurrence patterns, and responses to cyanobacteria. These results can contribute to future management strategies for controlling cyanobacterial blooms in freshwater ecosystems.

Ultrasound assisted cyanotoxin extraction for nematode inhibition in soil

Root-knot nematodes are one of the plant damaging nematodes in agriculture causing a projected annual yield loss of ∼12 % (∼$160 billion) worldwide. Conventional solutions to control these plant-parasitic nematodes involve chemical nematicides. To reduce the use of harmful chemicals, microalgal extracts can be used as greener alternatives for nematode management. Microalgae produce valuable metabolites, including cyanotoxins which can aid in nematode suppression. In this study, two microalgae species, Trichormus variabilis and Nostoc punctiforme, were treated with ultrasound for intensified recovery of secondary metabolites. Ultrasound results in cell wall disruption of the microalgal species, thus resulting in enhanced release of secondary metabolites. Microalgal biomass was treated with an ultrasound probe at 50 % amplitude, 20 kHz frequency, using water as the extraction medium, for 5-30 min. The extraction efficiency was determined in terms of the total chlorophyll (Chl) content of the extract. Microscopic images of the treated cells were also investigated to gain insight into the effect of the ultrasonication time on the cell morphology. Our results suggest that ultrasonication resulted in the intensified release of secondary metabolites, as established through the total chlorophyll content of the ultrasonicated microalgal samples as well as the microscopic images of the ruptured cells. The best extraction for Trichormus variabilis was achieved with 15 min extraction time where the Total Chl content increased by 29 times (compared to the non-ultrasonicated sample), and for the Nostoc punctiforme, 30 min extraction time gave the highest metabolite recovery of 6.4 times higher than the non-ultrasonicated sample. Ultrasonicated algal extracts were then tested for their nematicidal potential against root-knot nematode, Meloidogyne hapla, in infested field soil samples. Experimental study was conducted using different concentrations of each microalga, Trichormus sp. and Nostoc sp., individually, as well as in combination. The nematode count for the treated soil was compared with that of the control (untreated soil). Ultrasonicated microalgal extracts showed 66% to 100% inhibition on root-knot nematodes in the soil samples tested.

Dominance of evaporation on lacustrine groundwater discharge to regulate lake nutrient state and algal blooms

As global threats to freshwater lakes, eutrophication and harmful algal blooms (HABs) are governed by various biogeochemical, climatological and anthropogenic processes. Groundwater is key to join these processes in regulating HABs, but the underlying mechanisms remain unclear. Here, we leveraged basin-wide field data of Lake Taihu (China's largest eutrophic lake) and global archives, and demonstrate the dominance of evaporation on lacustrine groundwater discharge (LGD) in shallow lakes. We extrapolated decadal LGD and the derived nutrient loadings and found that HABs promptly consume ubiquitous groundwater borne nutrients, leading lake water N: P ratios 2-3 months time lagged behind LGD N: P ratios. We conclude that evaporation dominated LGD is an unraveled but crucial regulator of nutrient states and HABs in shallow lakes, which advocates synergistical studies from both climatological and hydrogeological perspective when restoring lake ecosystems.

Development, Validation and Application of a Targeted LC-MS Method for Quantification of Microcystins and Nodularin: Towards a Better Characterization of Drinking Water

Cyanotoxins can be produced in surface waters by cyanobacterial blooms, mostly during summer and early autumn. Intoxications would result from consumption of water contaminated with the potent hepatotoxins, microcystins and nodularin. Therefore, the WHO has set a guideline value for drinking water quality concerning one congener of microcystin. Consequently, the design of a validated, public reference method to detect and quantify the hepatotoxins in drinking water is necessary. During this study, a method was developed to quantify cyanotoxins (eight microcystin congeners and nodularin) in water using liquid chromatography coupled with tandem mass spectrometry. Additionally, bottled and tap water samples were tested for the presence of cyanotoxins. No cyanotoxins were detected in any of the collected water samples. However, quality controls and the results of a proficiency test show the validity of the method.

A Summer of Cyanobacterial Blooms in Belgian Waterbodies: Microcystin Quantification and Molecular Characterizations

In the context of increasing occurrences of toxic cyanobacterial blooms worldwide, their monitoring in Belgium is currently performed by regional environmental agencies (in two of three regions) using different protocols and is restricted to some selected recreational ponds and lakes. Therefore, a global assessment based on the comparison of existing datasets is not possible. For this study, 79 water samples from a monitoring of five lakes in Wallonia and occasional blooms in Flanders and Brussels, including a canal, were analyzed. A Liquid Chromatography with tandem mass spectrometry (LC-MS/MS) method allowed to detect and quantify eight microcystin congeners. The mcyE gene was detected using PCR, while dominant cyanobacterial species were identified using 16S RNA amplification and direct sequencing. The cyanobacterial diversity for two water samples was characterized with amplicon sequencing. Microcystins were detected above limit of quantification (LOQ) in 68 water samples, and the World Health Organization (WHO) recommended guideline value for microcystins in recreational water (24 µg L⁻¹) was surpassed in 18 samples. The microcystin concentrations ranged from 0.11 µg L⁻¹ to 2798.81 µg L⁻¹ total microcystin. For 45 samples, the dominance of the genera Microcystis sp., Dolichospermum sp., Aphanizomenon sp., Cyanobium/Synechococcus sp., Planktothrix sp., Romeria sp., Cyanodictyon sp., and Phormidium sp. was shown. Moreover, the mcyE gene was detected in 75.71% of all the water samples.

Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

Characteristics of the Phytoplankton Community Structure and Water Quality Evaluation in Autumn in the Huaihe River (China)

As an important indicator of phytoplankton in water quality evaluation, the phytoplankton community structure is very sensitive to changes in water quality, and analyzing their community composition and function is of great significance for the ecological management and maintenance of watershed environments. To understand the environment and ecological status as well as reconstruct or restore a healthy aquatic ecosystem in the Huaihe River Basin in China, a comprehensive phytoplankton survey was conducted in the main stream and main tributaries of the Huaihe River in 2019. A total of 266 species or genera of phytoplankton were identified, mainly belonging to Bacillariophyta and Chlorophyta. The number of phytoplankton species upstream and downstream was higher than that in the middle. The results of phytoplankton biomass showed significant spatial differences in different river reaches (p < 0.05). The identified phytoplankton functional groups (FGs) were divided into 27 groups, including 16 representative functional groups (RFGs), followed by A, B, F, G, H1, J, K, L_M, L_O, M, MP, P, T, T_B, W_O and X2. The mean values of the Shannon-Wiener index and Margalef index were 2.47 and 2.50, respectively, showing that most of the water in the Huaihe River Basin was in a state of moderate nutritional status. The results of this study provided a reference for studying the composition and distribution of phytoplankton communities, nutrient status, and pollution levels in the Huaihe River Basin, as well as in other similar watersheds.

Human impacted shallow lakes in the Pampean plain are ideal hosts for cyanobacterial harmful blooms

The ecological status of Pampean shallow lakes is evidenced by Cyanobacteria Harmful Blooms impairing these nutrient enriched, turbid and polymictic water bodies spread along the Central Plains of Argentina. Under the premise that shallow lakes are sentinels of global climate and eutrophication, a 3-year research in ten lakes located across a climatic gradient explored which factors drove the dynamics of cyanobacterial assemblages frequently driving to bloom prevalence. Contrarily to what is expected, the effect of seasonal temperature on cyanobacteria was subordinated to both the light environment of the water column, which was on turn highly affected by water level conditions, and to nutrient concentrations. Monthly samplings evidenced that cyanobacterial assemblages presented a broad-scale temporal dynamics mostly reflecting inter-annual growth patterns driven by water level fluctuations. Both species composition and biovolume gradually changed across a gradient of resources and conditions and hence, the scenario in each individual lake was unique with patterns at different temporal and spatial scales. More than 35 filamentous and colonial morphospecies constituted the assemblages of Pampean lakes: nostocaleans and chroococcaleans were inversely correlated in the prevailing interannual 3-cycled patterns.

The Influence of Hydrometeorological Conditions on Changes in Littoral and Riparian Vegetation of a Meromictic Lake in the Last Half-Century

Changes in water levels in lakes play an important role in the development of their coastal zones and water trophy. The aim of this study was to assess the role of changes in hydrometeorological conditions in the development of littoral and riparian vegetation of a meromictic lake during the last half-century. The study was carried out in Lake Powidzkie, one of the largest water reservoirs located in central Poland. Water level and meteorological conditions were analyzed in the period 1961–2015. Modifications in the range of plant communities were analyzed on the basis of cartographic materials and field studies. Meteorological conditions, especially precipitation and evaporation, were found to strongly affect the lake's water retention, whilst they had less of an effect on water levels. A significant effect of the lowering of the water level in Lake Powidzkie on the development of the littoral zone, whose area more than doubled over the last half-century, from 41.5 to 118.8 ha, was noted. The most dynamic development of the littoral was observed in the last quarter of the century, in which three of several years of low-flow were recorded. The occurrence of periods with an increased amount of precipitation, after dry periods, did not contribute to the reduction of the size of the rush zone and limitation of the development of woody vegetation.

Responses of water quality and phytoplankton assemblages to remediation projects in two hypereutrophic tributaries of Chaohu Lake

Water shortages and the presence of point and diffuse source pollution have caused a serious deterioration in water quality in two tributaries (the Tangxi River and Shiwuli River) of Chaohu Lake, China. To reduce nutrient pollution and suppress harmful algal blooms (HABs), hard engineering and ecological remediation projects were implemented. A post-project investigation from 2013 to 2016 was carried out to evaluate the outcome of the remediation projects by monitoring the seasonal and spatial variations in water quality and the phytoplankton community. In the Tangxi River, the average total phosphorus (TP) concentrations in the four seasons were below 0.5 mg L^-1, with the lowest concentration (0.29 ± 0.12 mg L^-1) found in autumn. Remediation measures including sediment dredging, riparian buffer zone creation, downstream wetland park construction, and water augmentation using reclaimed water and filtered lake water might combine to promote P source mitigation. Moreover, the percentage of bloom-forming cyanobacteria (i.e., Microcystis, Aphanizomenon, Anabaena, Oscillatoria, Phormidium and Planktothrix) in the phytoplankton assemblage and the biomass of the dominant species indicated successful HAB control. In the Shiwuli River, water quality improvements and phytoplankton responses have been observed since 2015 after the upgrading of a local wastewater treatment plant (WWTP) with effluent that was used for flow augmentation. Nevertheless, there is still room for improvement via increasing the river self-purification ability (e.g., the creation of downstream wetlands and riparian buffer zones) and promoting water augmentation according to the experience gained in the remediation projects of the Tangxi River.

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

Predicting cyanobacteria bloom occurrence in lakes and reservoirs before blooms occur

With increased global warming, cyanobacteria are blooming more frequently in lakes and reservoirs, severely damaging the health and stability of aquatic ecosystems and threatening drinking water safety and human health. There is an urgent demand for the effective prediction and prevention of cyanobacterial blooms. However, it is difficult to effectively reduce the risks and loss caused by cyanobacterial blooms because most methods are unable to successfully predict cyanobacteria blooms. Therefore, in this study, we proposed a new cyanobacterial bloom occurrence prediction method to analyze the probability and driving factors of the blooms for effective prevention and control. Dominant cyanobacterial species with bloom capabilities were initially determined using a dominant species identification model, and the principal driving factors of the dominant species were then analyzed using canonical correspondence analysis (CCA). Cyanobacterial bloom probability was calculated using a newly-developed model, after which, the probable mutation points were identified and thresholds for the principal driving factors of cyanobacterial blooms were predicted. A total of 141 phytoplankton data sets from 90 stations were collected from six large-scale hydrology, water-quality ecology, integrated field surveys in Jinan City, China in 2014-2015 and used for model application and verification. The results showed that there were six dominant cyanobacterial species in the study area, and that the principal driving factors were water temperature, pH, total phosphorus, ammonia nitrogen, chemical oxygen demand, and dissolved oxygen. The cyanobacterial blooms corresponded to a threshold water temperature range, pH, total phosphorus (TP), ammonium nitrogen level, chemical oxygen demand, and dissolved oxygen levels of 19.5-32.5 °C, 7.0-9.38, 0.13-0.22 mg L^-1, 0.38-0.63 mg L^-1, 10.5-17.5 mg L^-1, and 4.97-8.28 mg L^-1, respectively. Comparison with research results from other global regions further supported the use of these thresholds, indicating that this method could be used in habitats beyond China. We found that the probability of cyanobacterial bloom was 0.75, a critical point for prevention and control. When this critical point was exceeded, cyanobacteria could proliferate rapidly, increasing the risk of cyanobacterial blooms. Changes in driving factors need to be rapidly controlled, based on these thresholds, to prevent cyanobacterial blooms. Temporal and spatial scales were critical factors potentially affecting the selection of driving factors. This method is versatile and can help determine the risk of cyanobacterial blooms and the thresholds of the principal driving factors. It can effectively predict and help prevent cyanobacterial blooms to reduce the global probability of occurrence, protect the health and stability of water ecosystems, ensure drinking water safety, and protect human health.

Quantitative assessment of the effects of human activities on phytoplankton communities in lakes and reservoirs

Global algal blooms have been severely threatening safety of drinking water and development of socio-economy. Effective prevention and accurate control of algal blooms require a quantitative assessment of the influence of human activities and identification of priority areas. However, previous studies on the quantitative assessment of the effects of human activities on algal communities are lacking, severely hindering the effective and precise control of algal blooms. This paper proposes a quantitative assessment model to evaluate the impact intensity of human activities on phytoplankton. Applications showed that the proliferation of phytoplankton were more limited by nutrients such as total phosphorus and ammonia where waters are less influenced by human activities, yet were less limited by these nutrients where there are highly intensive human activities. The density of phytoplankton in waters increased with an increase in human activity intensity, particularly in concentrated agricultural areas, which are priority areas for the prevention and control of algal blooms. The methodologies can clearly identify key areas for algal bloom prevention and control and can provide scientific evidence for water and nutrient management throughout the world, reducing the risk of algal blooms and ensuring aquatic ecosystem health and potable water safety.

Sediment potentially controls in-lake phosphorus cycling and harmful cyanobacteria in shallow, eutrophic Utah Lake

Lakes worldwide are impacted by eutrophication and harmful algal or cyanobacteria blooms (HABs) due to excessive nutrients, including legacy P released from sediments in shallow lakes. Utah Lake (northern Utah, USA) is a shallow lake with urban development primarily on the east side of the watershed, providing an opportunity to evaluate HABs in relation to a gradient of legacy sediment P. In this study, we investigated sediment composition and P concentrations in sediment, pore water, and the water column in relation to blooms of harmful cyanobacteria species. Sediments on the east side of the lake had P concentrations up to 1710 mg/kg, corresponding to elevated P concentrations in pore water (up to 10.8 mg/L) and overlying water column (up to 1.7 mg/L). Sediment P concentrations were positively correlated with Fe2O3, CaO, and organic matter abundance, and inversely correlated with SiO2, demonstrating the importance of sediment composition for P sorption and mineral precipitation. Although the sediment contained <3% Fe2O3 by weight, approximately half of the sediment P was associated with redox-sensitive Fe oxide/hydroxide minerals that could be released to the water column under reducing conditions. Cyanobacteria cell counts indicate that blooms of Aphanizomenon flos-aquae and Dolichospermum flosaquae species tend to occur on the east side of Utah Lake, corresponding to areas with elevated P concentrations in the sediment, pore water, and water column. Our findings suggest that shallow lake eutrophication may be a function of P in legacy sediments that contribute to observed HABs in specific locations of shallow lakes.

Impact of Lake Morphology and Shallowing on the Rate of Overgrowth in Hard-Water Eutrophic Lakes

Lake disappearance is one of the results of water eutrophication which leads to lake shallowing and overgrowth, and small and shallow lakes are the most threatened with degradation. We studied the effect of lake shallowing on its overgrowth, taking into account the morphometric parameters of water bodies. The study was performed in 20 lakes located in the central west part of Poland. The degree of lake shallowing was evaluated on the basis of bathymetric maps made in the 1960s and studies carried out presently. Additionally, an analysis of littoral coverage and macrophyte growth forms was investigated. Moreover, the composition, intensity of phytoplankton blooming, and physico-chemical parameters of the lake water were analyzed. Redundancy analysis shows that the lake volume, average depth, shallowing rate, and change in volume were the parameters that most strongly correlated with the share of macrophytes in the lakes. According to the regression analysis, the share of emergent macrophytes was significantly correlated with lake shallowing. No relation was found between phytoplankton blooming and lake shallowing. Conversely, the lakes with the highest rate of shallowing were characterized by the greatest share of vegetation, which suggested that vegetation growth had a significant impact on lake shallowing.

Extraction and applications of cyanotoxins and other cyanobacterial secondary metabolites

The rapid proliferation of cyanobacteria in bodies of water has caused cyanobacterial blooms, which have become an increasing cause of concern, largely due to the presence of toxic secondary metabolites (or cyanotoxins). Cyanotoxins are the toxins produced by cyanobacteria that may be harmful to surrounding wildlife. They include hepatotoxins, neurotoxins and dermatotoxins, and are classified based on the organs they affect. There are also non-toxic secondary metabolites that include chelators and UV-absorbing compounds. This paper summarizes the optimal techniques for secondary metabolite extraction and the possible useful products that can be obtained from cyanobacteria, with additional focus given to products derived from secondary metabolites. It becomes evident that the potential for their use as biocides, chelators, biofuels, biofertilizers, pharmaceuticals, food and feed, and cosmetics has not yet been comprehensively studied or extensively implemented.

Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.