Using a long‐range autonomous underwater vehicle to find and sample harmful algal blooms in Lake Erie
Yanwu Zhang, Brian Kieft, Brett Hobson, Ben‐Yair Raanan, William Ussler, Christina M. Preston, Reagan M. Errera, Paul A. Den Uyl, Andrea Vander Woude, Gregory J. Doucette, Steven A. Ruberg, Kelly D. Goodwin, James M. Birch, Christopher A. Scholin
Abstract
Abstract Cyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long‐range autonomous underwater vehicle (LRAUV) equipped with a 3 rd ‐generation Environmental Sample Processor (3G‐ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search‐and‐sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web‐based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings.