Hydroacoustic surveys using hull-mounted down-looking transducers are useful for estimating pelagic fish densities; however, this method may miss shallow fish owing to the acoustic surface dead zone and vessel avoidance. Our objective was to compare pelagic fish density estimates acquired by a traditional down-looking acoustic survey to estimates obtained by a new multi-directional-towed sled capable of sampling the entire water column using upward-, sideways-, and downward-aimed transducers simultaneously. We deployed both systems concurrently in the western arm of Lake Superior during a period of stable stratification. We found the two survey approaches provided significantly different estimates of fish density in the upper water column layer (~4–9 m below the lake surface) with the sled up-looking transducer providing 56 times higher densities compared to the traditional ship down-looking method. Densities also varied significantly in the 9–14 m layer where densities were 6.2 times higher in the sled survey. Midwater trawl sampling indicated that cisco (Coregonus artedi) and rainbow smelt (Osmerus mordax) were the predominant species occupying the uppermost 14 m of the water column. The two acoustic approaches provided similar results at water column depths >14 m where rainbow smelt and kiyi (Coregonus kiyi) were predominant. Overall, the sled-based method estimates were, on average, 2.5 times higher for the whole water column. Our findings show that the new sled can reduce bias by better sampling the surface dead zone leading to more accurate estimation of pelagic fish densities for both management and research.
|Title||Spatial and vertical bias in down-looking ship-based acoustic estimates of fish density in Lake Superior: Lessons learned from multi-directional acoustics|
|Authors||Ryan C Grow, Thomas R. Hrabik, Daniel Yule, Bryan G. Matthias, Jared T. Myers, Chad Abel|
|Publication Subtype||Journal Article|
|Series Title||Journal of Great Lakes Research|
|Record Source||USGS Publications Warehouse|
|USGS Organization||Great Lakes Science Center|