The common carp is an invasive species of fish which poses a significant threat across the Midwest. This species pollutes lakes by uprooting plants and releasing large quantities of harmful nutrients while bottom-feeding. It is important to track and control the species — which is what Professor Peter Sorensen, a leading expert of fish behavior in the Department of Fisheries, Wildlife, and Conservation Biology at the University of Minnesota, is dedicated to doing. In an effort to study fish behavior, Dr. Sorensen’s team tags the carp with radio emitters. But the process is labor-intensive. The fish are caught and emitters are surgically inserted under their skin before they are reintroduced into the lake. Collecting data then requires the work of two lab members (typically a postdoc and a graduate student): one to steer the boat toward locations where the fish are likely to be found, and the other to rotate a directional antenna in search of the fish, as the receiver loops through a list of frequencies. Consequently, data collection can be performed only for a limited time. Yet, Dr. Sorensen’s group is often interested in determining carp distributions at obscure places and times such as daybreaks and shallow wetlands where carp can migrate and reproduce. The ability to continuously monitor the lakes and outlining environments can be very useful.
We are collaborating with Prof. Sorensen’s group to automate the data collection process. At first, one might think that a network of stationary antenna would be suitable for this task. However, a datalogger, receiver and antenna combination costs about $3,000, and has a range of roughly 40 meters. Therefore, even covering a single lake would be very costly. Even though this may be feasible for a single lake, deploying such networks across numerous lakes around the Twin Cities would be prohibitively costly. We believe that a network of a small number of light-weight robotic rafts would be ideal for this task. Such a network can be easily deployed. It can autonomously reconfigure itself based on the location of the tagged fish.