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Person

Steve A Farha

Biological Science Technician
Email: sfarha@usgs.gov
Office Phone: 734-994-3331
ORCID: 0000-0001-9953-6996

Location
1451 Green Road
Ann Arbor , MI 48105
US
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Acoustic seabed classification (ASC) is an important method for understanding landscape-level physical and biological patterns in the aquatic environment. Bottom habitats in the Laurentian Great Lakes are poorly mapped to date, and will require a variety of contributors and data sources to complete. We repurposed a long-term split-beam echosounder dataset gathered for purposes of fisheries assessment to estimate lakebed properties utilizing unsupervised classification of echo return data. We extracted first echo properties and analyzed lakebed hardness and roughness to define and map three statistically supported lakebed classes revealed through cluster analysis. Our results indicate coherent and logical class boundaries,...
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Diet analyses were performed on juvenile lake trout (Salvelinus namaycush) captured by bottom trawl during annual USGS Great Lakes Science Center demersal fish community surveys in mid-October to mid-November 2008–2017 in Lake Huron. This dataset contains lake trout capture information (e.g., location, depth) and diet item data (taxa consumed and their associated lengths).
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These data were derived from hydroacoustic data collected by uncrewed surface vehicles (USVs) and powered research vessels in Lake Superior in 2022. The powered vessels overtook the USVs to study fish avoidance of survey vessels during traditional acoustic surveys. The water column was divided into four depth groups for analysis. Each USV transect was binned into 30-sec intervals and measured hydroacoustic values were averaged in this region. To compare vessels and USVs, parallel overtakes (overtakes where vessel and drone followed the same path) which were ~2 km long, were measured by both platforms and the differences between acoustic measures compared.
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