NDLI: Species identification of Chinese sturgeon using acoustic descriptors and ascertaining their spatial distribution in the spawning ground of Gezhouba Dam

Content Provider	Springer Nature Link
Author	Tao, JiangPing Qiao, Ye Tan, XiChang Chang, JianBo
Copyright Year	2009
Abstract	This study has developed an improved subjective approach of classification in conjunction with Stepwise DFA analysis to discriminate Chinese sturgeon signals from other targets. The results showed that all together 25 Chinese sturgeon echo-signals were detected in the spawning ground of Gezhouba Dam during the last 3 years, and the identification accuracy reached 90.9%. In Stepwise DFA, 24 out of 67 variables were applied in discrimination and identification. PCA combined with DFA was then used to ensure the significance of the 24 variables and detailed the identification pattern. The results indicated that we can discriminate Chinese sturgeon from other fish species and noise using certain descriptors such as the behaviour variables, echo characteristics and acoustic cross-section characteristics. However, identification of Chinese sturgeon from sediments is more difficult and needs a total of 24 variables. This is due to the limited knowledge about the acoustic-scattering properties of the substrate regions. Based on identified Chinese sturgeon individuals, 18 individuals were distributed in the region between the site of Gezhouba Dam and Miaozui reach, with a surface area of about 3.4 km$^{2}$. Seven individuals were distributed in the region between Miaozui and Yanshouba reach, with a surface area of about 13 km$^{2}$.
Starting Page	3972
Ending Page	3980
Page Count	9
File Format	PDF
ISSN	10016538
Journal	Chinese Science Bulletin
Volume Number	54
Issue Number	21
e-ISSN	18619541
Language	English
Publisher	SP Science in China Press
Publisher Date	2009-11-25
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	hydroacoustics fisheries acoustics echo-signal species identification Chinese sturgeon spatial distribution Engineering Earth Sciences Chemistry/Food Science Physics Life Sciences Science
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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