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In vivo phycocyanin (PC) fluorescence allows assessing cyanobacterial abundance in an easy, fast and cost-effective way. However, the establishment of PC thresholds is necessary for its use in routine monitoring programmes and there has been no consensus regarding their definition. This work aimed:(1) to assess the potential species-specific variation in fluorometric PC content among Microcystis aeruginosa, Nostoc muscorum and Cylindrospermopsis raciborskii;(2) to propose specific PC thresholds based on World Health Organization alert levels; and (3) to evaluate the in vivo PC signal reliability to interferences from massive algal growth and mixtures of bloom-forming cyanobacteria. Strong linear relationships were recorded between PC and cell counts, biovolume and chlorophyll a. However, a significant species-specific variation in PC was observed using cell counts. Increased microalgal densities did not cause significant interference of PC signals. Also, dual mixtures of cyanobacteria revealed strong relationships between measured and expected PC content. Results suggest that fluorometric PC is a good predictor for cyanobacterial biomass but cell number is not the best parameter to define thresholds. Biovolume should be used instead. Nevertheless, speciesspecific thresholds must be considered, rather than a general cyanobacterial threshold.

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.

The third author publication name should have been Maria I. S. Nunes instead of Isabel M.S. Nunes. The corrected author name is shown above.