WANG Xinliang, ZHAO Xianyong, ZUO Tao, LI Xiansen. Acoustical identification and density estimation of Euphausia pacifica in the Yellow Sea[J]. Journal of fisheries of china, 2016, 40(7): 1080-1088. DOI: 10.11964/jfc.20151010102
Citation: WANG Xinliang, ZHAO Xianyong, ZUO Tao, LI Xiansen. Acoustical identification and density estimation of Euphausia pacifica in the Yellow Sea[J]. Journal of fisheries of china, 2016, 40(7): 1080-1088. DOI: 10.11964/jfc.20151010102

Acoustical identification and density estimation of Euphausia pacifica in the Yellow Sea

  • Euphausia pacifica is a key species of zooplankton in the Yellow Sea ecosystem. To estimate the biomass and distribution of E. pacifica is essential for better understanding local ecosystem structure and energy flow. However, due to its avoidance to zooplankton net, it is difficult to quantify the krill biomass using traditional vertical net sampling method. Based on acoustic and biological data of E. pacifica swarms collected in the Yellow Sea in January 2010, the target strength (TS) of E. pacifica at 38 and 120 kHz was analyzed using SDWBA theoretical model. Moreover, the E. pacifica swarms were identified using dB-differencing method based on 38 and 120 kHz acoustic data and the krill density in the sound scattering layers (SSL) was estimated subsequently. Numerical simulation showed that the TS of E. pacifica was sensitive to its orientation and length distribution. The TS at 120 kHz was obviously higher than 38 kHz, while the difference decreased with increasing krill length. A linear relationship with an intercept of 14.1 dB was observed for the mean volume backscattering strength (MVBS) between the two frequencies. Based on 120 kHz data, the density of E. pacifica in the SSL was estimated between 1.8 and 2351.8 ind/m3 with a mean of 255.1 ind/m3. This paper preliminarily introduced the species identification and density estimation of E. pacifica based on the dB-differencing method, which provides useful reference for the acoustical estimation of zooplankton biomass. To improve the accuracy and precision, further investigations on the parameters in the TS model and the target discrimination method are needed.
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