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USGS · 70148138

A novel approach to surveying sturgeon using side-scan sonar and occupancy modeling

Abstract

Technological advances represent opportunities to enhance and supplement traditional fisheries sampling approaches. One example with growing importance for fisheries research is hydroacoustic technologies such as side-scan sonar. Advantages of side-scan sonar over traditional techniques include the ability to sample large areas efficiently and the potential to survey fish without physical handling-important for species of conservation concern, such as endangered sturgeons. Our objectives were to design an efficient survey methodology for sampling Atlantic Sturgeon Acipenser oxyrinchus by using side-scan sonar and to developmethods for analyzing these data. In North Carolina and South Carolina, we surveyed six rivers thought to contain varying abundances of sturgeon by using a combination of side-scan sonar, telemetry, and video cameras (i.e., to sample jumping sturgeon). Lower reaches of each river near the saltwater-freshwater interface were surveyed on three occasions (generally successive days), and we used occupancy modeling to analyze these data.We were able to detect sturgeon in five of six rivers by using these methods. Side-scan sonar was effective in detecting sturgeon, with estimated gear-specific detection probabilities ranging from 0.2 to 0.5 and river-specific occupancy estimates (per 2-km river segment) ranging from 0.0 to 0.8. Future extensions of this occupancy modeling framework will involve the use of side-scan sonar data to assess sturgeon habitat and abundance in different river systems.

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90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 31.924192605327708° to 36.56260003738548° latitude; -81.84814453125° to -75.509033203125° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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BibTeXRIS

H. Jared Flowers, Joseph E. Hightower. 2013-09-19. A novel approach to surveying sturgeon using side-scan sonar and occupancy modeling. https://doi.org/10.1080/19425120.2013.816396

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