There are many species of blue-green algae (cyanobacteria), and each species can have a number of genotypes. Water utilities routinely monitor reservoirs and lagoons for the harmful toxin-producing species, and when they find a threshold number of cells, proceed to test for toxins. The problem is that not all genotypes of known toxin-producing species produce toxins. There is already a well-established quantitative PCR method that detects the genes responsible for making toxins, but the relationship between occurrence of these toxic genotypes and the amount of toxin in one water sample is not straightforward.

This project will create a modified version of the existing regulatory protocols for managing toxic blue-green algal blooms by adding the toxic gene qPCR test to the current tests for toxin and species identification. This modified protocol will then be used to assess samples for which there are already results from all three tests. The original management costs will be compared to the desktop analysis of hypothetical costs that would have been incurred if genotoxicity testing had been included in the protocol. Could gene testing reduce overall cyanobacteria management costs and confer operational benefits?

Several cyanobacteria species are well known for their potential to produce cyanotoxins. However, not all genotypes of known toxin producing species produce cyanotoxins and of these there is significant variation in the spatial and temporal dynamics of toxin production. The water industry currently relies of observational measurement of the presence of ‘potentially toxic species’, toxin gene and toxin presence to inform management of cyanobacteria blooms in water supply storages. Predictive tools and preventative management are limited by a lack of simple environmental predictors to predict toxin production events. Understanding the drivers for toxin production that inform risk management frameworks would be of great benefit to water supply managers and to inform alternate management options. These tools would enable better responses to bloom events and allowing for the establishment of pre-emptive measures to minimize cyanotoxin production by targeted manipulation of environmental drivers.

There is a need to have a clear and consistent approach to assessing the efficacy and performance of the growing number of algal bloom management technologies emerging on the market. Often technologies are offered by suppliers with the promise that they will solve algal bloom issues. However, there are a number of factors that, if poorly understood and addressed, can result in performance being unsatisfactory for water utility needs. This research will develop a customisable protocol for selection, deployment, and performance assessment of algal bloom management technologies.