Acoustic processing

Post-processing of acoustic data was carried out using Echoview v3.5 (SonarData, Ho- bart). The acoustic data were calibrated, then edited to remove returns from the seabed and the transducer nearfield (see Simmonds and MacLennan 2005), surface noise and false-bottom returns. TVG-amplified noise was removed using the technique described by Watkins and Brierley (1996). No statistically significant differences were observed be- tween on and off continental shelf TVG-amplified noise. Samples from both frequencies (38 and 120 kHz) were convolved using a uniform three-by-three moving kernel filter (see Reid and Simmonds 1993). Convolving the data reduced the sampling volume mismatch, increased the signal to noise ratio and reduced the effect of missing pings on krill swarm boundaries.

To reduce sampling volume overlap between adjacent pings, swarms detected at depths greater than 150 m (cf Woodd-Walker et al. 2003) were ignored. In addition to making the krill swarms identified here comparable to other studies, a cut off depth of 150 m was also selected because of beam geometry and vessel speed. At 150 m the diameter of the sampling volume was approximately 16.8 m for 38 kHz and 9.5 m for 120 kHz, and was considered a compromise between the average 12.5 inter-ping distance and sampling overlap between adjacent acoustic samples at 150 m. The 150 m cut off depth removed 16% of krill swarms (n=2,959 remained), but only 5% of swarm biomass. Swarms with a relative school length image compared to the beam width (N b) of less than 1.5 were also excluded from the analysis (see Diner 2001), which resulted in 2 swarms being removed.

Krill swarm detection took place in three steps. Firstly, the acoustic data were pre- pared for the school detection algorithm. Secondly, sensitivity analysis carried out of krill swarm algorithm detection parameters. Thirdly, selected detection parameters were used to extract krill swarm data for all available transects.

Automated aggregation detection

The “Schools” module of Echoview v3.5 software package was used to detect pelagic aggre- gations in convolved 120 kHz echograms. This module is based on the Shoal Analysis and Patch Estimation System (SHAPES, see Barange 1994). The SHAPES algorithm assesses acoustic data as a matrix of Sv observations. The user selects a processing threshold, and elements in the array above this are assessed by the algorithm in a three stage process. Firstly, candidate swarms are identified, these are elements in the Sv matrix that exceed the processing threshold, are adjacent and the length and height of the group exceed

minimum values of minimum candidate length and minimum candidate height SHAPES algorithm parameters. Spatial discontinuities within a school, which potentially give the appearance of multiple schools, where only one exists, are compensated using the vertical and horizontal linking distances. The SHAPES parameters, maximum horizontal linking distance and maximum vertical linking distance define a search area, which is a search el- lipse that is moved around the boundary of each candidate school in turn. Where another candidate school falls within the search ellipse the current candidate school boundary is extended to encompass both candidate schools i.e. it is assumed that both candi- date schools are a single school. Finally the total minimum height and length SHAPES algorithms are used to select only candidate schools, or linked candidate schools with dimensions that exceed these parameters. For further details of SHAPES (see Barange 1994; Coetzee 2000).

Prior to running the SHAPES algorithm on the full three years of data a sensitivity analysis of the number of detected krill swarms for a range of SHAPES algorithm param- eters for the selected transects was undertaken. The purpose of this was two fold. Firstly, to determine the variation in swarm position, morphological and energetic parameters with respect to the swarm detection parameters. Secondly to define, for the purposes of this research, a krill swarm.

Not all acoustically detected aggregations were necessarily krill. The acoustic data were partitioned using the dB difference technique, under which krill aggregations were identified as aggregations falling in the 2 to 12 dB range for Sv 120-S −v 38 kHz (see Madureira et al. 1993; Brierley et al. 1997b). The dB difference was evaluated for all Sv data within the krill swarm boundaries identified from the 120 kHz convoluted acoustic data, with these boundaries being applied to the 38 kHz convoluted acoustic data, giving two acoustic samples for the dB difference technique.

Swarm descriptors

Once swarms of krill had been identified, krill swarm metrics (Table 2.2) were obtained, again using the Echoview “Schools” module. Corrections to swarm geometry as de- rived by Diner (2001) were applied to swarm morphology. Krill swarm mean Sv values (Sv = 10log10(sv)) were converted to volumetric krill density (ρv) using a TS scale factor calculated from the frequency-distribution of krill caught in the RMT8 ( ˆπl) the Demer and Conti (2005) individual krill theoretical target strength model (σbs, in the linear domain) and the Morris et al. (1988) length to wetmass relationship (w), giving:

k = w( ˆπl)

σbs( ˆπl)

and

pv =svk (2.2)

To enable comparison between swarms, thepv was standardised by corrected swarm length (Lc) divided by 1000 m. This gave a metric describing the contribution made by an individual krill swarm to the overall survey area biomass.

pvc =pv × Lc

1000 (2.3)