Detection probabilities of electrofishing, hoop nets, and benthic trawls for fishes in two western North American rivers
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Abstract
Research comparing different sampling techniques helps improve the efficiency and efficacy of sampling efforts. We compared the effectiveness of three sampling techniques (small-mesh hoop nets, benthic trawls, boat-mounted electrofishing) for 30 species in the Green (WY, USA) and Kootenai (ID, USA) rivers by estimating conditional detection probabilities (probability of detecting a species given its presence at a site). Electrofishing had the highest detection probabilities (generally greater than 0.60) for most species (88%), but hoop nets also had high detectability for several taxa (e.g., adult burbot Lota lota, juvenile northern pikeminnow Ptychocheilus oregonensis). Benthic trawls had low detection probabilities (<0.05) for most taxa (84%). Gear-specific effects were present for most species indicating large differences in gear effectiveness among techniques. In addition to gear effects, habitat characteristics also influenced detectability of fishes. Most species-specific habitat relationships were idiosyncratic and reflected the ecology of the species. Overall findings of our study indicate that boat-mounted electrofishing and hoop nets are the most effective techniques for sampling fish assemblages in large, coldwater rivers.
Study Area
Publication type | Article |
---|---|
Publication Subtype | Journal Article |
Title | Detection probabilities of electrofishing, hoop nets, and benthic trawls for fishes in two western North American rivers |
Series title | Journal of Fish and Wildlife Management |
DOI | 10.3996/022015-JFWM-011 |
Volume | 6 |
Issue | 2 |
Publication Date | September 01, 2015 |
Year Published | 2015 |
Language | English |
Publisher | Scientific Journals |
Contributing office(s) | Coop Res Unit Seattle |
Description | 21 p. |
First page | 371 |
Last page | 391 |
Country | United States |
State | Idaho, Wyoming |
Other Geospatial | Kootenai River, Green River |
Online Only (Y/N) | N |
Additional Online Files (Y/N) | N |