Stepwise strategy for monitoring toxic cyanobacterial blooms in lentic water bodies

Abstract

Climate change has been causing the increase in frequency, severity, and duration of harmful algal blooms, which makes the establishment of water management strategies indispensable. For cyanobacteria, several methods are currently used in monitoring programs. However, these methods are time-consuming and require specialists, and results are usually not provided within an adequate timeframe for taking timely mitigation actions. This work proposes a strategy for a faster, easier, and more cost-effective monitoring of cyanobacterial blooms, using a stepwise approach based on fluorometric determination of phycocyanin at an early stage. Complementary parameters (chlorophyll a, enumeration of dominant cyanobacterial species and cyanotoxin potential and quantification) are determined when necessary, thus progressively allocating human and financial resources within the monitoring program. This strategy was applied and validated using nine lentic eutrophic freshwater bodies prone to the occurrence of cyanobacterial blooms. Samples were sequentially evaluated, and the study ended up with two samples that showed high health risks. However, according to WHO guidelines, eight of the nine samples would be classified as having "moderate risk of adverse health effects" and could lead to preventive measures that would have an important regional economic impact. Therefore, the present approach proved to be a promising alternative to increase the effectiveness and accuracy of the risk assessment process in water bodies where cyanobacterial blooms occur.

Detection of Cyanotoxin-Producing Genes in a Eutrophic Reservoir (Billings Reservoir, São Paulo, Brazil)

“…To date, few studies have evaluated the presence of several genes of clusters responsible for biosynthesis of microcystin, cylindrospermopsin and saxitoxin in environmental samples [11,[27][28][29][30][31][32][33][34][35], when compared to the majority of studies in which only one gene is used. Of these, five [28,30,[33][34][35] reported results similar to those obtained in this work, i.e., differential amplification between genes responsible for biosynthesis of a cyanotoxin.…”

“…Nevertheless, there is no consensus on which gene would be most appropriate to infer the presence of toxins [18,20,21,25,26]. Despite the full description of gene clusters responsible for the production of toxins, studies evaluating more than one gene are still not many when compared to studies evaluating one gene alone [11,[27][28][29][30][31][32][33][34][35].…”

Performance evaluation of in situ fluorometers for real-time cyanobacterial monitoring

“…The concentration of phycocyanin detected between the two strains of M. aeruginosa and the fluorometers differed by five to twenty times (Figure 3). This is most likely explained by the differences in phycocyanin production rates between different species and strains (Eriksen 2008;Whitton & Brian 2012;Mulders et al 2014;Macário et al 2017). Previous studies have shown that production rates varied between five species of Spirulina and four species of Athrospira (Eriksen 2008).…”

“…Previous research showed that normalising the fluorometer output for biovolume was able to reduce the inaccuracy better than normalising the measurements to cell density (Zamyadi et al 2012;Kasinak et al 2015). Additionally, Chl-a and cell enumeration are not indicative of the presence of toxic cyanobacterial blooms (Macário et al 2017). This is due to two factors: (1) each species has a specific phycocyanin production rate (Mulders et al 2014) and 2cell size between species can differ by several orders of magnitude (Kasinak et al 2015).…”

Harmful Cyanobacterial Blooms: Going beyond the “Green” to Monitor and Predict HCBs

“…Following this strategy, the amount of information that could be cross-linked would tremendously increase the databases with more robust information that could better feed predictive models for HCBs. Multi-tiered based approaches for monitoring HCBs have been proposed over the last years by integrating several methodologies with different purposes at different stages [32,[221][222][223][224] and most modelling approaches are already integrating field sampling data with remote sensing imagery providing models considering hydrodynamics, wind/wave, sediment transport, and biogeochemistry. However, although Chl a and PC-based models and studies are essential, they will not help per se to unravel species-specific cyanobacterial blooms dynamics.…”