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The management strategy for the Antarctic krill (Euphausia superba) fishery is being revised. A key aim is to spatially and temporally allocate catches in a manner that minimizes impacts to both the krill stock and dependent predators. This process requires spatial information on the distribution and abundance of krill, yet gaps exist for an important fishing area surrounding the South Orkney Islands in the south Scotia Sea. To fill this need, we create a dynamic distribution model for krill in this region. We used data from a spatially and temporally consistent acoustic survey (2011-2020) and year-specific environmental covariates within a two-part hurdle model. The model successfully captured observed spatial and temporal patterns in krill density. The covariates found to be most important included distance from shelf break, distance from summer sea ice extent, and salinity. The northern and eastern shelf edges of the South Orkney Islands were areas of consistently high krill density and displayed strong spatial overlap between intense fishing activity and foraging chinstrap penguins. High mean krill density was also linked to oceanographic features located within the Weddell Sea. Our data suggest that years in which these features were closer to the South Orkney shelf were also years of positive Southern Annular Mode and higher observed krill densities. Our findings highlight existing fishery?predator?prey overlap in the region and support the hypothesis that Weddell Sea oceanography may play a role in transporting krill into this region. These results will feed into the next phase of krill fisheries management assessment.

Temporal distributions of Antarctic krill (Euphausia superba) density and aggregation types were characterized and compared using Nortek Signature100 and SIMRAD Wideband Autonomous Transceiver (WBAT) upward-looking echosounders. Noise varied between the two echosounders. With the Signature100, it was necessary to correct data for background, transient, and impulse noises, while the WBAT data needed to be corrected for background noise only. For selected regions with no visible backscatter, the signal-to-noise ratio of Sv values (i.e. the ratio between the signal and the background noise level) did not vary between the two echosounders. Surface echo backscatter was similar during similar time periods. Descriptive metrics were used to quantify spatial and temporal krill vertical distributions: volume backscatter, mean depth, center of mass, inertia, equivalent area, aggregation index, and proportion occupied. Krill backscatter density differed between the two instruments but was detected at similar mean depths. Krill aggregations were identified at each mooring location and classified in three types based on morphological characteristics. Each type of aggregation shape differed at the two spatially separated moorings, while the acoustic density of each aggregation type was similar. The Signature100 detected a lower number of krill aggregations (n = 133) compared to the WBAT (n = 707). Although both instruments can be used for autonomous deployment and sampling of krill over extended periods, there is a strong caveat for the use of the Signature100 due to significant differences in noise characteristics and krill detection.

Krillscan software was developed to automatically process echosounder data and achieve an accelerated and transparent analysis of backscatter data that allows calculation of target biomass. Herein, the fishery for Antarctic krill (Euphausia superba, Henceforth Krill) was used as a case study to develop the approach. Implementation of a sustainable management strategy for the krill fishery is complicated by a lack of regularly updated krill abundance data on spatiotemporal scales of the fishery. To increase krill biomass data availability, automatic echosounder data processing and swarm detection software was tested against traditional manual scrutinization with LSSS software and agreed with only minor offsets in estimated nautical area scattering coefficients. In addition to automatic processing and data transfer, Krillscan also has a graphical user interface to supervise automatic krill swarm detection. Echogram size can be compressed up to 100 times and raw data are processed faster than generated, thereby enabling near-real time analysis and data transfer. Compressed data can be transmitted online to allow fishing vessels to conduct surveys without having scientific personnel with special expertise on board.

Knowledge about swarm dynamics and underlying causes is essential to understand the ecology and distribution of Antarctic krill . We collected acoustic data and key environmental data continuously across extensive gradients in the little-studied Southeast Atlantic sector of the Southern Ocean. A total of 4791 krill swarms with swarm descriptors including swarm height and length, packing density, swimming depth and inter-swarm distance were extracted. Through multivariate statistics, swarms were categorized into 4 groups. Group 2 swarms were largest (median length 108 m and thickness 18 m), whereas swarms in both Groups 1 and 4 were on average small, but differed markedly in depth distribution (median: 52 m for Group 1 vs. 133 m for Group 4). There was a strong spatial autocorrelation in the occurrence of swarms, and an autologistic regression model found no prediction of swarm occurrence from environmental variables for any of the Groups 1, 2 or 4. Probability of occurrence of Group 3 swarms, however, increased with increasing depth and temperature. Group 3 was the most distinctive swarm group with an order of magnitude higher packing density (median: 226 ind. m ) than swarms from any of the other groups and about twice the distance to nearest neighbor swarm (median: 493 m). The majority of the krill were present in Group 3 swarms, and the absence of association with hydrographic or topographic concentrating mechanisms strongly suggests that these swarms aggregate through their own locomotion, possibly associated with migration.

We studied the relationship between the proximity of land and the distribution and swarming characteristics of Antarctic krill across the Scotia Sea in January and February 2003. Krill swarms identified with a Simrad EK60 (38 kHz, 120 kHz) echosounder were grouped into 4 categories according to distance from shoreline: 0 to 50 km, 50 to 100 km, 100 to 200 km and >200 km. Cross-sectional areas of swarms were significantly larger inshore, with a mean value of 120 m² in the 0 to 50 km zone compared to <80 m² further offshore. The packing concentration of krill within inshore swarms was also significantly greater, with an average density of 95 ind. m^–3 compared to between 24 and 31 ind. m^–3 elsewhere. A large proportion of the biomass was concentrated into a small number of large, dense swarms throughout the survey area, and this trend increased with decreasing distance from shore. The highest median number of swarms per km and krill acoustic biomass per km was found in the 50 to 100 km zone. However, a significantly greater number of large, biomass-rich swarms occurred in the 0 to 50 km zone compared to all other zones. Swarms in the 0 to 50 km zone were also significantly further apart. The majority of swarms were located in the upper 50 m during the daytime although they were marginally deeper in the night in offshore regions. Krill are likely to move between inshore and offshore environments continuously over their lifetimes. The change in krill behaviour between environments could be a response to local predatory threats over short spatial and temporal scales.

Wiebe, P. H., Chu, D., Kaartvedt, S., Hundt, A., Melle, W., Ona, E., and Batta-Lona, P. 2010. The acoustic properties of Salpa thompsoni. – ICES Journal of Marine Science, 67: 583–593.Aggregations of the salp Salpa thompsoni were encountered during the Antarctic krill and ecosystem-studies cruise on the RV “G.O. Sars” from 19 February to 27 March 2008. The salp's in situ target strength (TS), size, number of individuals in aggregate chains, and chain angle of orientation were determined. Shipboard measurements were made of Salpa thompsoni's material properties. Individual aggregates were mostly 45.5–60.6 mm in mean length; relatively rare solitaries were ∼100 mm. Chains ranged from 3 to at least 121 individuals, and in surface waters (&lt;20 m), they showed no preferred angle of orientation. Sound-speed contrast (h) ranged from 1.0060 to 1.0201 and density contrast (g) estimates between 1.0000 and 1.0039. The in situ TS distributions peaked between −75 and −76 dB at 38 kHz, with a secondary peak at approximately −65 dB. TS ranged between −85 and −65 dB at 120 and 200 kHz and peaked around −74 dB. The measured in situ TS of salps reasonably matched the theoretical scattering-model predictions based on multi-individual chains. The backscattering from aggregate salps gives rise to TS values that can be similar to krill and other zooplankton with higher density and sound-speed contrasts.

Swarming is a fundamental part of the life of Euphausia superba, yet we still know very little about what drives the considerable variability in swarm shape, size and biomass. We examined swarms across the Scotia Sea in January and February 2003 using a Simrad EK60 (38 and 120kHz) echosounder, concurrent with net sampling. The acoustic data were analysed through applying a swarm-identification algorithm and then filtering out all non-krill targets. The area, length, height, depth, packing-concentration and inter-swarm distance of 4525 swarms was derived by this method. Hierarchical clustering revealed 2 principal swarm types, which differed in both their dimensions and packing-concentrations. Type 1 swarms were generally small (<50m long) and were not very tightly packed (<10ind.m−3), whereas type 2 swarms were an order of magnitude larger and had packing concentrations up to 10 times greater. Further sub-divisions of these types identified small and standard swarms within the type 1 group and large and superswarms within the type 2 group. A minor group (swarm type 3) was also found, containing swarms that were isolated (>100km away from the next swarm). The distribution of swarm types over the survey grid was examined with respect to a number of potential explanatory variables describing both the environment and the internal-state of krill (namely maturity, body length, body condition). Most variables were spatially averaged over scales of ∼100km and so mainly had a mesoscale perspective. The exception was the level of light (photosynthetically active radiation (PAR)) for which measurements were specific to each swarm. A binary logistic model was constructed from four variables found to have significant explanatory power (P<0.05): surface fluorescence, PAR, krill maturity and krill body length. Larger (type 2) swarms were more commonly found during nighttime or when it was overcast during the day, when surface fluorescence was low, and when the krill were small and immature. A strong pattern of diel vertical migration was not observed although the larger and denser swarms tended to occur more often at night than during the day. The vast majority of krill were contained within a minor fraction of the total number of swarms. These krill-rich swarms were more common in areas dominated by small and immature krill. We propose that, at the mesoscale level, the structure of swarms switches from being predominantly large and tightly packed to smaller and more diffuse as krill grow and mature. This pattern is further modulated according to feeding conditions and then level of light.