CCAMLR

Antarctic krill (Euphausia superba) is a large euphausiid, widely distributed within the Southern Ocean, and a key species in the Antarctic food web. The Discovery Investigations in the early 20th century, coupled with subsequent work with both nets and echosounders, indicated that the bulk of the population of postlarval krill is typically confined to the top 150 m of the water column. Here, we report for the first time the existence of significant numbers of Antarctic krill feeding actively at abyssal depths in the Southern Ocean. Biological observations from the deepwater remotely operated vehicle Isis in the austral summer of 2006/07 have revealed the presence of adult krill (Euphausia superba Dana), including gravid females, at unprecedented depths in Marguerite Bay, western Antarctic Peninsula. Adult krill were found close to the seabed at all depths but were absent from fjords close inshore. At all locations where krill were detected they were seen to be actively feeding, and at many locations there were exuviae (cast molts). These observations revise significantly our understanding of the depth distribution and ecology of Antarctic krill, a central organism in the Southern Ocean ecosystem.

Scientific observation on the species composition and abundance of fishes incidentally caught during Antarctic krill (Euphausia superba DANA) fisheries by F/V Niitaka Maru (5200t) were made from 6 August to 30 August to the north of South Georgia Is. Among 87 net hauls quantitatively examined, by-catch fish was recognized in 26 trawl catches (29.9%) and only one fish was found as by-catch in 21 hauls. Among a total of 7 fish species, Myctophidae 3, Zoarcidae 1, Nototheniidae 1 and Channichthyidae 2, recognized, Krefftichthys anderssoni of Myctophidae occurred most frequently (38.5% of net hauls containing by-catch fish). Owing to the small amount of by-catch, no clear relationships between krill CPUE and fish by-catch could be confirmed in the present study.

In order to calculate the target strength of Antarctic krill (Euphausia superba) using an acoustical scattering model, information on size, morphology, orientation, sound–speed and mass-density contrasts between the animal and the surrounding water are required. Sound-speed and mass-density of krill were measured during the Antarctic surveys conducted by the Japanese RV Kaiyo Maru in 1999/2000 and 2004/2005. Samples of krill were caught by a RMT(1+8). Mass-density of krill was measured by density bottle method. The mean total length and the mean mass-density contrast were 43.5mm and 1.028 near South Shetland Islands in February 2000. These were 21.7mm and 1.049, and 45.1mm and 1.043 in the Ross Sea in January and February 2005. Sound-speed was measured using the "time of flight" method. The corresponding sound-speed contrast of krill with mean total lengths of 44.2mm was 1.011 in the South Shetland Islands. These contrasts of krill with mean total lengths of 25.1mm and 48.6mm were 1.044 and 1.035 respectively in the Ross Sea. To examine the effect of these parameter differences, the target strength and its directivity of krill were calculated using the stochastic DWBA model.

In this paper the krill spatial distribution in the in coastal and pelagic SSMUs of the Subarea 48.2 depending on oceanological factors is considered. Estimates of krill biomass and aggregation characteristics, and krill transport factors according to different modifications of Antarctic water mass are presented based on the CCAMLR 2000 Survey data. It is shown, that despite the high biomass concentrated in the South Orkney Pelagic Area (SOPA) during the CCAMLR 2000 Survey, the krill aggregation patterns did not meet the requirements of the present day fishery. Krill distribution and water mass circulation according to CCAMLR 2000 Survey (January-February 2000) are compared with those obtained from the data of long-term observations and fishery in Subarea 48.2. The types of geostrophic current fields and correspondent fishing ground allocations in the South Orkney Islands area revealed from 1962 to 1997 are presented. It is concluded that the development of options for krill stock management call for actual materials, describing annual and seasonal changes in biomass and characteristics of krill distribution in the SSMUs areas.

We validate the acoustic target classification protocols developed for the Stochastic Distorted-Wave Born Approximation (SDWBA) model using three frequency acoustic data and concurrent net hauls that were collected during two cruises to the South Georgia region in 1996. For each krill aggregation sampled by net we calculated the difference between acoustic backscatter at 120 and 38 kHz (Sv120-38) and at 200 and 120 kHz (Sv200-120). We considered the performance of 4 different acoustic target identification algorithms for krill: (i) ‘3 freq model’ - using the SDWBA to set the acceptance windows for both Sv120-38 and Sv200-120, (ii) ‘2 freq model’ - using the SDWBA to set the acceptance window for just Sv120-38, (iii) ‘2 freq 2-16’ - where the Sv120-38 window was fixed at 2-16 dB and (iv) ‘2 freq 2-12’ - where the Sv120-38 window was fixed at 2-12 dB. The overall aggregation dB difference for 120 – 38 kHz for every net fell within the SDWBA model derived target id window, however, for 200 - 120 kHz the SDWBA model derived target id window only identified krill in 6 of the 16 nets correctly. The ‘2 freq 2-16’ algorithm attributed more than 90 % of the total backscatter to krill in all but 1 aggregation with the ‘2 freq model’ using a smaller window but still attributing more than 90 % of the total backscatter to krill in 12 out of the 16 nets. The ‘2 freq 2 – 12’ window only attributed more than 90 % of the total backscatter to krill in 6 nets while the ‘3 freq model’ attributed only just greater than 50 % of the backscatter to krill in only 2 aggregations, and in 6 aggregations attributed less than 10 % of the total backscatter to krill. Therefore the SDWBA(11,4) using 38, 120 and 200 kHz in the present configuration to set variably sized windows is likely to substantially underestimate krill. In contrast the SDWBA(11,4) used at 38 and 120 kHz identifies very well the krill detected during these net hauls and because it uses a window substantially smaller than the fixed 2-16 window, will at the same time reduce the amount of bycatch that may occur when targets other than Antarctic krill are present in the water column.

One recommendation from the Predator Survey workshop was the undertaking of some immediate inter-sessional work to be reported to WG-EMM-08. The work to be undertaken included preliminary estimation of SSMU-specific gentoo and Adélie breeding abundance in Area 48, and similar analyses of east Antarctic data for Adélie penguins. It was recognised that the abundance estimates would not be corrected for availability (e.g. nest failure) and hence would to some extent be biased estimates of the breeding population, and would only account for uncertainty in the accuracy of the count data. However, the work was seen as useful in illustrating both the database of penguin count data compiled for the workshop, and the underlying basis of new estimation procedures presented to the workshop. At an SSMU level, uncertainty associated with accuracy or repeatability of the counts varied substantially between SSMUs (95% C.I. as a percent of the count ranged from 1.5% to 37.1%).

At its meeting in 2007 CCAMLR endorsed the proposal from the Scientific Committee for a joint CEP/SC-CAMLR workshop. The CEP discussed this at its meeting in June 2008 and suggested a theme of ‘Opportunities for collaboration and practical cooperation between the CEP and SC-CAMLR’. The CEP suggested that areas of common interest might include, though may not be limited to: • climate change research • ecosystem and environmental monitoring • protected areas and spatial management measures • species requiring special protection • marine pollution • biodiversity and non-native species. WG-EMM is invited to consider CCAMLR input into the Workshop agenda and work plan to inform SC-CAMLR at its meeting in October 2008.

We apply Foosa at the scale of interactions among the 3 breeding penguin colonies, krill, and environmental variability at the long-term research site in Admiralty Bay, King George Island. This work-in-progress serves 2 purposes: 1) to use historical data to estimate parameters in a model-fitting framework for the purpose of model validation, and 2) to add and explore functionality in Foosa to investigate alternative, competing hypotheses about juvenile penguin survival. Our preliminary results suggest that Foosa capably captures the general trends in adult abundance at Admiralty Bay with minimal formal estimation. Preliminary examination of top-down and bottom-up forcing on juvenile penguin survival further helps to explain trends in adult abundance. From a bottom-up perspective, there appears to be a trade-off between per-capita productivity at low adult abundance and the sensitivity of juvenile survival to foraging conditions during the first winter of life. From a top-down perspective, strong depensatory stock-recruitment dynamics suggest that understanding predatory effects on juveniles may be fundamental for understanding penguin dynamics at our study colony. To better capture the inter-annual variability in the adult abundance data, we propose future work that includes, inter alia, expanding the spatial scope to account for seasonal movement of the penguins, incorporating alternative environmental drivers, and continued hypothesis testing to make strong inference about the dominant drivers of the penguins at Admiralty Bay.

Gentoo penguins Pygoscelis papua show considerable plasticity in their diet, diving and foraging behaviors among colonies; we expected that they might exhibit similar variability over time, at a single site, since flexible foraging habits would provide a buffer against changes in prey availability. We examined inter-annual changes in the foraging strategies and diet of gentoo penguins in the South Shetland Islands, Antarctica, over five years. Antarctic krill Euphausia superba was the primary diet item, and fish the secondary, though the importance of these items varied among years. Diving behavior also varied over time; different dive depth-distributions were observed in each year. Nonetheless, chick-rearing success remained relatively constant, indicating that gentoo penguins were able to cope with differences in prey availability by altering their foraging strategy among years. We suggest that this flexibility may contribute to why gentoo penguin populations have remained stable in the region, while their congeners with less flexible foraging strategies have declined.

We provide a worked example of how a systematic conservation planning methodology (Margules & Pressey, 2000) might be used to identify important areas for conservation of biodiversity in the pelagic environment, using Subarea 48.2 (South Orkney Islands) as a pilot study area. The aim of the worked example is not to identify areas for protection or management at this stage, but rather to test the utility of this methodology, and to demonstrate the types of data and the range of decisions that would be required to undertake such an analysis. ‘MARXAN’ software (Ball & Possingham, 2000; Game & Grantham, 2008) is used to objectively determine the possible contribution of individual areas towards meeting conservation targets, using example datasets for pelagic species, bioregions and other environmental characteristics. It is concluded that this methodology could be used to provide meaningful results with those datasets currently available. With further refinement, the results from this type of analysis could be used to inform the implementation of a range of actions to conserve marine biodiversity.