CCAMLR

Single frequency (38 and 120 kHz) acoustic and associated net data were extracted from US AMLR datasets and used to mimic data that might be collected by the commercial fishery during the fishing seasons. From these data we develop semi-empirical indices of acoustic biomass in a Generalized Linear Modeling framework. We correlate these estimates of biomass with acoustic estimates derived from the 3-frequency method using the CCAMLR protocol for the period between 1996 and 2011 in both the Elephant Island and West Shelf Areas of the US AMLR survey grid. Cross-validation of acoustic estimates between these two areas and the 3-frequency biomass showed that models developed using a wide length frequency distribution (Elephant Island) could be used to estimate biomass from other areas where the length frequency of animals is skewed towards larger animals. We show that it is possible to develop semi-empirical models of krill biomass, at 120 kHz frequency that can be used to augment research acoustic surveys if proper survey design and calibration of transducers is maintained and if time series are sufficiently long to average out differences among years.

The use of fishing-vessel-based acoustic data has been recognized by SC-CAMLR as an important way to estimate the distribution and relative abundance of Antarctic krill (Euphausia superba), yet the quality and even the utility of the data may be seriously degraded by interferences due to the lack of synchronization device for the acoustic instruments equipped on some of the vessels. A simple algorithm to remove noise and significant interference from other acoustic instruments was introduced. The algorithm was built on relevant modules in the Echoview acoustic data post-processing software. The utility of the method was demonstrated by comparing the appearances of krill swarm echograms, the dB differences and the echo integrations. Results showed that the interference were effectively removed while the retained krill swarms were maintained in good geometrical shape and volume backscatter strength.
 

Increasing volumes of acoustic data are being collected which necessitates a reappraisal of many of the current methods for data processing and analysis.  For some acoustic data sets manual processing is no longer an option; the large size of the data set is preventing analysis.  This document provides an overview of an automated procedure for processing acoustic data that is illustrated using Echoview, EK60 data and the Echoview^R package available in the statistical language R.  Whilst the scripting of Echoview through EchoviewR has been successful, there are several challenges remaining before the acoustic processing can be truly automated, such as robust seabed and false bottom detection.