Th-124-5
Experiences Using Autonomous Technologies to Survey Fish Populations

M. Elizabeth Clarke , Northwest Fisheries Science Center, NOAA National Marine Fisheries Service, Seattle, WA
Erica Fruh , Northwest Fisheries Science Center, NOAA National Marine Fisheries Service, Newport, OR
Curt Whitmire , Northwest Fisheries Science Center, NOAA National Marine Fisheries Service, Monterey, CA
Hanumant Singh , Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA
Chris Roman , Graduate School of Oceanography, University of Rhode Island
Jeremy Taylor , Joint Institute for Marine and Atmospheric Research, University of Hawaii/Pacific Islands Fisheries Science Center, Honolulu, HI
Jeff Anderson , Nature Imagery
Clay Kunz , Seabed Tech., Inc.
New technologies can improve the efficiency and quality of fisheries data collections. This is especially true in areas such as rocky habitats and marine protected areas, where traditional survey gears such as bottom trawls are not appropriate. We will discuss the role of Autonomous Underwater Vehicles (AUVs) and bottom-tracking floats for such applications.  

We have been successful at collecting bottom imagery and using those images to determine local abundances of groundfish and invertebrates.  Without careful planning, the combined costs of maintenance, operation, support vessels, data analysis, intercalibration, and operationalization into full-scale surveys can dwarf the original cost of platforms and sensors.  Ideally, the acquisition of technologies would start with the identification of the problem, and avoid the inefficiencies in retrofitting or using assets in a sub-optimal manner.  Planning and analysis of the risks of damaging or losing gear should occur up front as part of an overall operational cost projection.  In practice, the operation of technologies is not usually year-round and therefore efficient operations can involve equipment and staff sharing.  We will offer some suggestions on how to choose appropriate platforms, minimize the aggregate cost, and develop an efficient operation that could realistically collect information on a regional scale.