論文

査読有り
2010年9月

Density estimation of Yangtze finless porpoises using passive acoustic sensors and automated click train detection

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
  • Satoko Kimura
  • ,
  • Tomonari Akamatsu
  • ,
  • Songhai Li
  • ,
  • Shouyue Dong
  • ,
  • Lijun Dong
  • ,
  • Kexiong Wang
  • ,
  • Ding Wang
  • ,
  • Nobuaki Arai

128
3
開始ページ
1435
終了ページ
1445
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1121/1.3442574
出版者・発行元
ACOUSTICAL SOC AMER AMER INST PHYSICS

A method is presented to estimate the density of finless porpoises using stationed passive acoustic monitoring. The number of click trains detected by stereo acoustic data loggers (A-tag) was converted to an estimate of the density of porpoises. First, an automated off-line filter was developed to detect a click train among noise, and the detection and false-alarm rates were calculated. Second, a density estimation model was proposed. The cue-production rate was measured by biologging experiments. The probability of detecting a cue and the area size were calculated from the source level, beam patterns, and a sound-propagation model. The effect of group size on the cue-detection rate was examined. Third, the proposed model was applied to estimate the density of finless porpoises at four locations from the Yangtze River to the inside of Poyang Lake. The estimated mean density of porpoises in a day decreased from the main stream to the lake. Long-term monitoring during 466 days from June 2007 to May 2009 showed variation in the density 0-4.79. However, the density was fewer than 1 porpoise/km(2) during 94% of the period. These results suggest a potential gap and seasonal migration of the population in the bottleneck of Poyang Lake. (c) 2010 Acoustical Society of America. [DOI: 10.1121/1.3442574]

リンク情報
DOI
https://doi.org/10.1121/1.3442574
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000281799800053&DestApp=WOS_CPL
ID情報
  • DOI : 10.1121/1.3442574
  • ISSN : 0001-4966
  • Web of Science ID : WOS:000281799800053

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