OU Harmful Algal Bloom Survey
What are Harmful Algal Blooms (HABs)?
HABs are simply overgrowths of algae in the environment. Some consist of population booms of algae that produce toxins, while others do not. Toxic HABs are dangerous for animal (fish, birds, shellfish) and human consumption, leading to higher treatment costs and economic impacts on water-using industry. HABs also can create dead zones in the water by consuming large amounts of dissolved oxygen and decomposing. This study focuses on toxic algal blooms containing cyanobacteria (or 'blue-green algae'), of which the most famous is Microcystis aeruginosa (photo below).
Algae need high water concentrations of nutrients such as nitrogen and phosphorus, sunlight and warmer temperatures to lead to HAB formation. HAB formation, but increasing human impacts on the environment are contributing to a higher frequency of HAB occurrence. Nutrient and pollution runoff due to residential lawn maintenance and agriculture can build up and facilitate HAB formation.
The Oakland University HAB Survey
Dr. David Szlag and Dr. Thomas Raffel at Oakland University are conducting a MI-Department of Environmental Quality supported project to develop stronger predictive tools for monitoring HABs, caused by toxin-producing cyanobacteria. We will conduct a large-scale survey of Michigan lakes to determine why some lakes are at higher risk for HAB formation. We are also collaborating with research groups at Wayne State University (Dr. Judy Westrick), Lake Superior State University (Benjamin Southwell), and Northern Kentucky University (Dr. Michael Waters and Dr. Miriam Steinitz-Kannan).
Harmful algal blooms (HABs) of toxin producing cyanobacteria are a problem for drinking water and recreational water use, resulting in new monitoring challenges. Current protocols may fail to detect HABs early enough for effective regulatory responses. Alternative approaches based on emerging technologies and HAB detection by citizen scientists could help to address these challenges (Figure 1). First, we propose to develop HAB hazard maps using existing land-use data and cyanotoxin reports and then continually update these maps by (1) measuring cyanobacteria and toxins using a combination of qPCR (DNA detection), ELISA (antibodies), LC/MS/MS (mass spectrometry), and SPATT samplers (resin that sequesters environmental toxins) and (2) testing for statistical associations with likely environmental drivers. Second, we propose to develop, implement, and validate an approach to early HAB detection by citizen scientists. Volunteers will report potential HABs using a smartphone app (HAB-App) that uses photographs of water samples to estimate cyanobacteria abundance. HAB reports will be validated using Lugol-preserved samples and targeted cyanotoxin analysis.
2017 Research Aims
- Spatial Survey: We will survey approximately 30 lakes across Michigan to test which variables best predict HAB formation. Our team will collect samples once every month from July to October 2017. Our sites include both those on inland lakes and the Great Lakes.
- HAB-App Field Testing: We are seeking a small number of dedicated volunteers (1-3 per lake) to take and submit geotagged photos of potential HABs, to field test a new computer program that detects the presence or absence of cyanobacteria in the water (developed by our collaborators at NKU). Volunteers that detect a possible HAB occurrance will be asked to collect and preserve a water sample for later microscopic analysis.
1. Survey Methods
During our large-scale field survey we will be:
- Testing water and SPATT (solid phase absorption toxin tracking resin) samples for the presence of harmful cyanobacteria.
- qPCR (DNA detection method), ELISA (antibody method), and GC-MS (mass spectrometry) to quantify total cyanobacteria and their toxin genes.
- Collect water chemistry data at each sampling date, including temperature, pH, conductivity, chlorophyll a, phycocyanin, turbidity, dissolved oxygen, and oxidation-reduction potential.
- Collect continuous temperature and weather data using HOBO data loggers and climate databases
- Lake profiling (water chemistry and sample collections at varying depth)
- Track invasive zebra mussel abundance visually (quadrat sampling) and accumulation over the survey period (PVC sampling tiles)
Dr. Michael Waters (NKU) has developed a computer program and accompanying iOS device app to identify the presence or absence of cyanobacteria. The detection algorithm is based on machine learning (below) and is trained by a series of images that are known to have either green algae (generally nontoxic) or cyanobacteria (toxic). The program analyzes the hue (the pure color profile), saturation (the color in proportion to its brightness), and value (the color based on amount of light reflected). Users will be able to take a photo and the algorithm will produce a probability of green algae vs. cyanobacteria.