Early detection of zebra mussels using multibeam sonar
This study will test the utility of swath mapping systems such as multibeam sonar for detecting and quantifying the abundance of invasive mussels at a very large scale. Multibeam sonar can map tens to hundreds of square kilometers of river or lake bed in a single day from a moving vessel. There is a strong likelihood that mussels have a distinct acoustic response (echo) compared to their surrounding substrate. If so, this acoustic signature can be readily used to detect and map zebra mussel beds in any navigable waterway of sufficient water depth. This study will define the methodology needed to detect, distinguish and quantify mussels from a moving vessel by studying backscattering of sound by mussels and common mussel-supporting substrates.
The first phase of this study is designed to utilize multibeam sonar to distinguish among substrate, native mussels, and zebra mussels in a controlled laboratory setting. A second phase is planned to validate and develop methodologies for use in the field.
Current methods for detecting and quantifying zebra mussel populations relies on methods that can be very time-consuming and expensive, such as diving, video imaging, and veliger sampling in the water column. Detecting zebra mussel populations early significantly improves the possibility that quarantines can be put in place and treatment options implemented.
Lab experiments are complete as of January 2019. Using that data, researchers developed machine-learning-based substrate classifiers based on hypothetical situations of abiotic and biotic substrates. This information is put into models, which are trained over ten unique substrates: 1) sand, 2) mix sand-gravel (MSG); 3) gravel; 4) sand-supported A. plicata; 5) MSG-supported A. plicata; 6) gravel-supported A. plicata; 7) sand-supported D. polymorpha (low density); 8) sand-supported D. polymorpha (high density); 9) gravel-supported D. polymorpha (low density); and 10) gravel-supported D. polymorpha (high density).