CCAMLR anticipates that acoustic data collected from krill fishing vessels will be incorporated into krill feed back management.  Acoustic data collected from fishing vessels may have different characteristics to data collected from research ships.  For example, acoustic data collected from fishing vessels may contain seabed aliased echoes and instrument cross-talk.  Analysing acoustic data on a krill-swarm, rather than the current integration grid approach, may facilitate the semi-automated processing of acoustic data and reduce post-processing time.  Using a validated aggregation detection algorithm, I estimated mean areal krill density for a small (65 km by 60 km) survey conducted in the waters off Mawson research station in the East Antarctic (66^o 25' S 63^o 14' E).  I found 61 % overlap between the variance estimates for conventional grid-based swarm-based krill density.  The processing time of the swarm-based appraoch was half that of the standard grid-based technique. Whilst additional test data sets are required, the techniques presented here can be used to further explore the efficacy of swarm-based analysis.

A dedicated krill survey for CCAMLR Division 58.4.1 during 2018/19 season is planned by Japan. No krill biomass has been estimated in the Division since 1996 when Australia carried out BROKE. There are two main objectives of our survey: (1) estimation of krill biomass to update B(0) in the area and (2) oceanographic observations in the area to detect long term changes if any. Japanese research vessel, Kaiyo-maru, will be used in the survey. The krill survey (echosounder and RMT) and subsequent biomass estimation will follow the CCAMLR standard protocol. The survey will be international oriented and participation of foreign scientists is welcome. This document is presented to SG-ASAM-17 with an intention to receive comments from the participants. Every suggestion will be duly examined and incorporated where relevant in the plan and a revised plan will be submitted to WG-EMM-17 and SG-ASAM-18 for further considerations. The final plan will be submitted to WG-EMM-18.

Acoustic data from krill fishing vessels have the potential to provide useful contributions to assess local krill abundance and distribution, and even as input to the CCAMLR feedback management system under development. The possibility to collect such data from the fleet has been demonstrated through a CCAMLR proof of concept, but processing is in most cases essential before any use of the data can be made. Procedures for data processing have not yet been addressed in detail within CCAMLR. Here we show examples of processing with a prerequisite that abundance and distribution of krill are our main interest, and that data of interest include both those collected during fishing operations and during surveying. We have worked with datasets covering most of the fishing season 2015/2016, one from the Chinese vessel Fu Rong Hai and one from the Norwegian vessel Saga Sea, both transect data and data collected during fishing operation. Spike noise and background noise were the most severe source of unwanted backscatter, but could be dealt with adequately using different noise removal techniques. Other sources of noise like integration of bottom echo or false bottom noise could not be removed using the automatic noise removal techniques we had at hand. We also evaluated the use of delineation and integration of only detected swarms for assessment of abundance, and the results are promising both for swarm integration as a robust noise excluding technique, and as a krill identification technique in case data have been collected without one or more of the CCAMLR prescribed three frequencies for target identification.