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Visual census and hydro-acoustic survey of demersal fish aggregations in Ulju small scale marine ranching area (MRA), Korea

수중촬영조사법과 음향자원조사법을 활용한 울주군 연안 소규모 바다목장 해역의 어류 군집 조사

  • Hwang, Bo-Kyu (Dept. of Marine Science & Production, Kunsan National University) ;
  • Lee, Yoo-Won (Education and Research Team, Korea Institute of Maritime and Fisheries Technology) ;
  • Jo, Hyun-Su (Dept. of Marine Science & Production, Kunsan National University) ;
  • Oh, Jeong-Kyu (Korea Ecology Institute Co., Ltd.) ;
  • Kang, Myounghee (Dept. of Maritime Police and Production System, The institute of Maritime Industry, Gyeongsang National University)
  • 황보규 (군산대학교 해양생산학과) ;
  • 이유원 (한국해양수산연수원 교육연구팀) ;
  • 조현수 (군산대학교 해양생산학과) ;
  • 오정규 (한국생태연구원 (주)) ;
  • 강명희 (경상대학교 해양경찰시스템학과)
  • Received : 2014.08.27
  • Accepted : 2015.02.11
  • Published : 2015.02.28

Abstract

Visual census and hydro-acoustic survey was carried out at Ulju small scale marine ranching area (MRA) to estimate demersal fish aggregations on September and November 2013. In this hydro-acoustic survey, the authors combined an image sonar with a scientific echo sounder to monitor an underwater situation and compare two acoustic data. Consequently, visual census survey was useful to estimate fish species composition for hydro-acoustic survey, because it is easy to identify aggregated fish species and overcome limits on a fishing depth and ability of an conventional fishing gear like a bottom gill-net or a fish trap at marine ranching area. Mean fish density was estimated as $0.757g/m^2$ on September and $0.219g/m^2$ on November and Fish abundance was finally calculated as 1.51ton (coefficient of variation, CV=13.1%) on September and 0.44ton (CV=47.7%) on November, respectively. Hydro-acoustic survey combined with the image sonar was useful to monitor fish aggregations and estimate fish stocks around artificial reefs at shallow coastal MRA. We were able to easily identify the underwater structures like an artificial reef and a fishing rope as well as fish aggregations from image sonar data. Therefore, the method was effective to separate unwanted echo signals in acoustic data of scientific echo sounder.

Keywords

Scientific echo sounder;Abundance estimation;Theoretical model;Image sonar

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