A validation study of phycocyanin sensors for monitoring cyanobacteria in cultures and field samples

Cyanobacteria are photosynthetic prokaryotes present in almost all terrestrial and aquatic environments. Under favourable conditions of high nutrient inputs, high water temperature and adequate light, they can rapidly multiply and form blooms. Cyanobacterial blooms are harmful to aquatic processes as they reduce the amount of irradiance within the water column and some have the ability to produce toxins which pose a risk to human and animal health. Cyanobacterial blooms are becoming prevalent worldwide as a result of increased anthropogenic influences. To enable accurate risk assessment of drinking water reservoirs, water bodies for recreational use and to assist in the understanding of bloom dynamics, cyanobacterial biomass must be quantified. Traditionally this was done via grab sampling, the use of microscopy and measurements of chlorophyll-a concentration using spectrophotometry. These methods do not allow samples to be collected and analysed at a frequency that provides meaningful spatial and temporal resolution. Additionally, they can be time consuming, expensive and require taxonomic expertise. Measurements of chlorophyll-a concentration can provide an estimate of overall phytoplankton biomass but cannot differentiate between eukaryotic cells and cyanobacteria. However, the fluorescent pigment phycocyanin is specific to cyanobacteria and has a unique fluorescence signature which can enable the estimation of cyanobacteria biomass. Methods to detect phycocyanin fluorescence have been integrated into handheld sensors which can be used as a proxy for estimation of cyanobacterial biomass in situ. However, robust validation of these sensors is rarely undertaken. Previous use of fluorescence sensors suggest that there may be multiple sources of interference of the optical signal. The aim of this study was to assess the performance of commercially-available phycocyanin sensors using a combination of culture-based laboratory experiments and a field investigation. The laboratory studies showed strong linear relationships between phycocyanin and chlorophyll-a fluorescence from sensors and cyanobacterial biovolume obtained by microscopy. This was observed across a range of cell concentrations for solitary (e.g. Microcystis sp.) and filamentous species (e.g. Aphanizomenon sp.). However this linear relationship was not observed with colonial and some filamentous cyanobacteria (Microcystis sp., Nodularia spumigena, and Dolichospermum sp.). Further investigation suggested that the morphology of densely aggregated species inhibited the penetration of light into the colony, resulting in an underestimation of phycocyanin. These results highlight a potential limitation for the use of phycocyanin sensors in situ as colonial and filamentous cyanobacteria are often observed. Other potential limitations for the use of phycocyanin sensors include natural variations in light intensity which can cause photobleaching; reducing fluorescence, as well as increased variability of phycocyanin fluorescence with changes in temperature. Field experiments during this study showed that extracellular phycocyanin from lysed cells could in some instances account for more than 20% of the total phycocyanin fluorescence. The field study also showed a strong linear relationship between measurements of phycocyanin fluorescence and cyanobacterial biovolume. However measurements of chlorophyll-a were poorly correlated to cyanobacterial biovolume. The results from the field experiments indicate that phycocyanin sensors may be a valuable tool to monitor cyanobacteria biomass in the field. Collectively, this data highlights the potential for the use of phycocyanin sensors to obtain high frequency data on cyanobacterial biomass, while also demonstrating limitations for their use in situ. Further study is needed to determine more definitively the effects of these potential limitations on phycocyanin measurements and which supplementary parameters could be measured in order for the data collected to be more robust and informative