New insights towards the establishment of phycocyanin concentration thresholds considering species-specific variability of bloom-forming cyanobacteria

Abstract

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.

Alternative Methods for Analysis of Cyanobacterial Populations in Drinking Water Supplies: Fluorometric and Toxicological Applications Using Phycocyanin

“…was dominant on 7 of 10 sampling dates, while in Lake Cochichewick, Aphanizomenon was dominant on 8 of 10 sampling dates, with microcystin-producing assemblages in both lakes ( Table 2). We anticipated differences in observed microcystin levels between the two lakes, where genus-specific estimates of pigment specific toxicity [33] [34] using phycocyanin [2] could have been applied a-priori. Lacking this, we could only evaluate a posteriori whether community composition and relative abundance influenced the interpretation of our results.…”

Continuous monitoring of cyanobacterial blooms: benefits and conditions for using fluorescence probes

“…Moreover, fluorescence technology has become far more advanced in recent years with specific light-emitting diodes (LED) and optical filters, offering the opportunity to significantly improve upon existing fluorescent probe technology (HENDERSON et al, 2009). Field probes measuring PC fluorescence can then potentially used to perform spatio-temporal monitoring of cyanobacterial blooms and offer a cost effective opportunity for on-line monitoring which can enable water operators to improve cyanobacteria management during the drinking water process (BRIENT et al, 2008;ZAMYADI et al, 2012a;MACÁRIO et al, 2015). However, recent widespread development and application of in situ fluorometric probes by both scientists and water utilities have also led to recognition of major issues associated with the undertaking of these measurements, particularly around interferences (ZAMYADI et al, 2012b).…”

Chlorophyll a interference in phycocyanin and allophycocyanin spectrophotometric quantification

“…However, this is not a simple task to achieve with a spectrophotometric analysis, because the absorbance bands of these pigments (PCs and APCs, in particular) overlap with those of Chls and produce a bias in their quantitative determination even in the aqueous extracts (Myers and Kratz 1955;Sarada et al, 1999;Yacobi et al, 2015), where Chla may be present if a not mild extraction protocol is used (the efficacy of an extraction protocol is species-specific; not always mild extraction protocols are applicable, due to the great resistance a cyanobacterium cell wall can have). Chla bias might be critical in some research applications such as in vivo fluo- rescence calibration (Kasinak et al, 2015;Macário et al, 2015) and the development of a more appropriate coefficient for the semi-empirical algorithms and specific inherent optical properties for the bio-optical model to apply at the satellite images in remote sensing for cyanobacteria monitoring (Schalles and Yacobi, 2000;Simis et al, 2007;Bresciani et al, 2011;Ogashawara et al, 2013;Mishra and Mishra, 2014), as well as insight into the physiology and ecology of cyanobacterial species (e.g., complementary chromatic adaptation; Bennett and Bogorad, 1973;De Marsac and Houmard, 1988;Li et al, 2016).…”