Towards a downstream passage solution for out-migrating American eel (Anguilla rostrata) on the St. Lawrence River

Abstract

There is a need to develop safe and effective downstream passage for diadromous anguillid eels on large rivers, where the combination of physical screening and bypass structures that is used to protect eel on smaller rivers is infeasible. The Eel Passage Research Center was established in 2013 to address the challenge of providing safe passage for out-migrating silver-stage American eel (Anguilla rostrata) on the St. Lawrence River, the outflow of the Laurentian Great Lakes, which is spanned by two large, mainstem hydroelectric generating stations. The Center's goal is to develop the technology to behaviorally guide eel to a collection point for capture and transfer around the two generating stations. To achieve this goal, three parallel initiatives were undertaken; 1) research into behavioral guidance techniques suitable for guiding out-migrating eel on large, high flow rivers, 2) the evaluation of sonar technologies for detection of eel, necessary for assessing in situ guidance, and 3) the commissioning of literature reviews for the most promising guidance technologies. A number of behavioral guidance stimuli, including electricity, flow, sound and electromagnetic fields were examined in laboratory and flume settings for their utility in guiding out-migrating silver-stage eel. Of the techniques examined, only sound showed any promise for guidance. Three sonar technologies (Simrad 120 kHz EK60 echosounder, Sound Metrics ARIS Explorer 1800 sonar, and the Kongsberg Mesotech 500 kHz M3 multi-mode multibeam sonar) were evaluated to determine if existing acoustic technologies could be used to estimate the relative abundance and distribution of out-migrating silver-phase American eel in the St. Lawrence River. Only the ARIS unit could successfully identify eel targets, and only over a short (<15 m) range. The immense volume of sonar data accumulated over a portion of a single outmigration season contributed to the successful development of machine learning tools to automate the identification of American eel targets. Based on previous research on the St. Lawrence River identifying light as a promising deterrent, and the potential for sound as a deterrent in this study, full literature reviews were commissioned for these two stimuli. Lessons learned to date have resulted in the design of a subscale, prototype guidance structure that is expected to be deployed in 2022.