Assessing the photo-physiology of cyanobacteria using active fluorescence

Abstract

Eutrophication and the impacts of climate change are responsible for the increased occurrence of harmful algae blooms. In lakes and reservoirs, cyanobacteria blooms pose particular risk to ecosystems, humans and animals due to the occurrence of toxin-producing species. Several methods exist to monitor cyanobacteria, which vary in cost, time and accuracy. Fluorescence methods developed to monitor cyanobacterial abundance already give rapid, robust and reproducible results. Discrimination between cyanobacteria and algae in fluorescence methods is based on their photosynthetic pigment content, but due to overlapping pigment absorption signatures, the interpretation of fluorescence signals is consequently not straightforward. In this thesis, a range of fluorescence markers were tested on the ability to assess and predict cyanobacteria physiology and growth. Hyperspectral laboratory experiments with algal and cyanobacteria cultures revealed that excitation wavebands centred on 445 nm and 615 nm, and emission wavebands at 660, 685 and 730 nm allow the best differentiation between cyanobacteria and algae. Broadband actinic light should be preferred to assess the relation between ambient light and photosynthesis. Based on these results, a new multispectral active fluorometer (LabSTAF) was tested to assess the physiology and growth of cyanobacteria in reservoirs exhibiting annual blooms. Fluorescence light curves obtained with a green-orange-red (GOR) and a blue (B) excitation protocol were found to follow cyanobacteria and algae physiology, respectively. The fluorescence emission ratio of photosystem I over II was also significantly correlated with the relative abundance of cyanobacteria. Excitation spectra further distinguish the presence of distinct pigment groups. Finally, the ability of the LabSTAF to determine cyanobacteria growth from photosynthetic parameters was demonstrated on natural samples brought into nutrient replete conditions. ETR (electron transport rate) and Pmax (maximum specific photosynthetic rate) were found to predict phytoplankton growth by up to 3-4 days, with excitation protocols GOR and B indicating the dominant phytoplankton group.