Deep Lake Explorer: A web application for crowdsourcing the classification of benthic underwater video from the Laurentian Great Lakes

Abstract

Underwater video is increasingly used to study aspects of the Great Lakes benthos including the abundance of round goby and dreissenid mussels. The introduction of these species has resulted in major ecological shifts in the Great Lakes, but the abundance and impacts of these species have heretofore been underassessed due to limitations of monitoring methods. Underwater video (UVID) can “sample” hard bottom sites where grab samplers cannot. Efficient use of UVID data requires affordable and accurate classification and analysis tools. Deep Lake Explorer (DLE) is a web application developed to support crowdsourced classification of UVID collected in the Great Lakes. Volunteers (i.e., the crowd) used DLE to classify 199 videos collected in the Niagara River, Lake Huron, and Lake Ontario for the presence of round gobies, dreissenid mussels, or aquatic vegetation, and for dominant substrate type. We compared DLE classification results to expert classification of the same videos to evaluate accuracy. DLE had the lowest agreement with expert classification for hard substrate (77%), and highest agreement for vegetation presence (90%), with intermediate agreement for round goby and mussel presence (89% and 79%, respectively). Video quality in the application, video processing, abundance of species of interest, volunteer experience, and task complexity may have affected accuracy. We provide recommendations for future crowdsourcing projects like DLE, which can increase timeliness and decrease costs for classification but may come with tradeoffs in accuracy and completeness.