CCAMLR

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.

The South Georgia region supports a high biomass of krill that is the subject to high inter-annual variability. The lack of a self-sustaining krill population at South Georgia means that understanding the mechanism underlying these observed population characteristics is essential to a successful ecosystem-based management of any krill fishery in the region. Krill acoustic density data from surveys conducted in the early, middle and late period of the summers of 2001 to 2005, together with krill population size structure over the same period from predator diet data, were used with a krill population dynamics model to evaluate potential mechanisms behind the observed changes in krill biomass. Krill abundance was highest during the middle of the summer in 3 years and in the late period in 2 years; in the latter there was evidence that krill recruitment was delayed by several months. A model scenario with empirically derived estimates of both the magnitude and timing of recruitment in each year showed the greatest correlation with the acoustic series. The results are consistent with a krill population with allochthonous recruitment entering a retained adult population; i.e. oceanic transport of adult krill does not appear to be the major factor determining the dynamics of the adult population. The results highlight the importance of the timing of recruitment, especially where this could introduce a mismatch between the peak of krill abundance and the peak demand from predators which may exacerbate the effects of changes in krill populations arising from climate change.

This document summarises progress by expert groups towards having manuscripts ready for the CCAMLR-IWC Workshop to review input data for Antarctic marine ecosystem models: update on progress 2008. Data collation is progressing well for all groups, except for flying birds. Draft manuscripts will be available for review at WG-EMM or from the convenors on request.

Catch uncertainty is a component of uncertainty that is not routinely considered by CCAMLR. However, given the currently reported variability in conversion factors for krill, a nominal reported catch of 600 000t could actually represent a catch in ‘green weight’ of 2.5 million tonnes. Quantifying the level of uncertainty in reported catches of krill would require information on product specific conversion factors (including the time-scale over which those conversion factors were produced) as well as the product composition of catches.

CCAMLR has agreed that there is a requirement for systematic observation from the krill fishery, however, current levels of observer coverage are not distributed evenly across years, regions or vessels. The number of observer days as a proportion of the number of days fished varied between c10% (Subareas 48.1 and 48.2) and c30% (in Subarea 48.3). 13 of the 16 vessels that have fished for krill in 48.3 have carried observers at some time, compared to 6 of 15 in 48.1 and 4 of 15 in 48.2. An analysis of existing observer data submitted to the Secretariat indicates that measuring 200 krill from 5 hauls should produce a sufficiently precise length frequency distribution and would free up time for other priority tasks of observers. Suggestions are provided for a revision of the Scientific Observer Manual for consideration by the Working Group.