Improving acoustic estimates of krill: experience from repeat sampling of northern krill (Meganyctiphanes norvegica) in Gullmarsfjord, Sweden

Abstract Everson, I., Tarling, G. A., and Bergström, B. 2007. Improving acoustic estimates of krill: experience from repeat sampling of northern krill (Meganyctiphanes norvegica) in Gullmarsfjord, Sweden – ICES Journal of Marine Research, 64, 39–48. A series of eight replicated acoustic surveys, fou...

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Bibliographic Details
Published in:ICES Journal of Marine Science
Main Authors: Everson, Inigo, Tarling, Geraint A., Bergström, Bo
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press (OUP) 2006
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Online Access:http://dx.doi.org/10.1093/icesjms/fsl010
http://academic.oup.com/icesjms/article-pdf/64/1/39/29127190/fsl010.pdf
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Summary:Abstract Everson, I., Tarling, G. A., and Bergström, B. 2007. Improving acoustic estimates of krill: experience from repeat sampling of northern krill (Meganyctiphanes norvegica) in Gullmarsfjord, Sweden – ICES Journal of Marine Research, 64, 39–48. A series of eight replicated acoustic surveys, four by day and four by night, was undertaken in Gullmarsfjord on the Swedish west coast during two 24-h periods on 8 and 10 September 2003, using a calibrated echosounder operating at 120 and 38 kHz. The difference in signal strength (ΔSv) was used to distinguish northern krill (Meganyctiphanes norvegica) from other acoustic scatterers. The approach is concluded to be very effective, but it can be improved greatly by applying the following series of simple extensions to current protocols: first, set a very low threshold on both frequencies to minimize sampling bias; second, undertake tests to confirm that the data extracted from each acoustic frequency apply to the same scatterers; third, ensure that the range of ΔSv is not greater than the TSrange at either frequency; and finally, when abundance estimation is the primary aim, arrange for sampling at the time of day and using the acoustic frequency that together provide the least variance.