Inter-annual variability in diet during crèche (December to February) over 22 years (1989 to 2010) was examined for gentoo penguins Pygoscelis papua breeding at Bird Island, South Georgia (54 º0’S, 38º2’W). Overall, diets comprised 51 % crustaceans and 49 % fish by mass. Crustaceans were present in 89 % of samples and were the main prey (> 50 % by mass) in 10 years of the study. Antarctic krill Euphausia superba were present in 85 % of all diet samples. Fish were present in 79 % of samples and were the main prey in 12 years, with Champsocephalus gunnari and Lepidonotothen larseni the most frequently recorded species, in 51 % and 33 % of samples respectively. The energy or mass of krill in the diet was the most reliable predictor of breeding success (the number of chicks fledged per breeding pair); the correlation between model-predicted and observed values was 0.58. We compared annual patterns of gentoo penguin diet variability with those of macaroni penguins Eudyptes chrysolophus breeding at the same location. Our results suggest that the availability of krill is a key source of diet variability for both species, but their diets indicate that gentoo penguins are generalist predators (feeding on pelagic and bentho-pelagic prey), while macaroni penguins are krill specialists (feeding on pelagic prey). Differences in the response to variability in key prey species is an important factor separating the ecological niches of these two sympatric krill predators.

We show that the preferred habitat models developed by Warwick-Evans et al. (2017) for chinstrap penguins breeding at the South Orkney Islands, can be modified to predict the at-sea distribution of chinstraps breeding at the South Shetland Islands, based on the distance from the colony, and the bearing to the shelf edge, adjusting for the at-sea density of Pygoscelis penguins from other colonies. The models performed well, even when using lower resolution tracking data. Cross validations between colonies and between regions indicated that our models perform better if local tracking data were available. Nevertheless, we conclude that these models can, with some confidence, be extrapolated to predict the foraging locations of animals during the breeding season from untracked colonies, even in different regions and across different archipelagos.

Based on mean summer (January to March) near-surface (averaged over 50 m to 150 m) oceanographic flows derived from a high resolution oceanographic model developed using NEMO, we report that chinstrap penguins prefer habitats with slow-flowing, nearshore waters over shallow bathymetry, but that they also sometimes move towards the edge of the continental shelf where the faster-flowing waters of the Coastal Current or fronts of the Antarctic Circumpolar Current exist. The preferred habitats are also locations preferentially used by the commercial krill fishery. Cross-shelf submarine canyons occur near to a number of preferred krill fishing locations, suggesting that these canyons potentially play an important role in krill transport onto the shelf. In the relatively slow-moving shelf waters krill depletion is probably more likely and krill replenishment less likely. Our results suggest that better understanding of krill retention and depletion in areas used by natural predators and the krill fishery are needed, and that  management strategies for the fishery should include assessment of how krill movement can satisfy the demands of both predators and the fishery across a range of spatial and temporal scales.

Effective management of the Antarctic krill (Euphausia superba, hereafter krill) fishery requires an understanding of the controls on the distribution and abundance of krill.  Particularly important are the pathways and timescales of movement and retention affecting krill distribution at scales relevant to the fishery and predators.  This paper reports the initiation of a project that will use numerical ocean-sea ice models at differing resolutions to improve current understanding of the regional and local-scale processes that influence the distribution of krill in CCAMLR Area 48.  Modelling will focus on the South Orkney Islands region, an area that is crucial for determining the overall availability of krill across the Scotia Sea region.  The results will help inform WG-EMM activities on the development of feedback management procedures and provide the present-day context for considering the potential impacts of climate change on this regional system.

The Integrating Climate and Ecosystem Dynamics in the Southern Ocean (ICED) programme is undertaking integrated circumpolar analyses to improve our understanding of Southern Ocean change, the implications for ecosystems and for management. ICED’s current major focus is to build on this work to more comprehensively assess (and where possible quantify) key impacts of change on Southern Ocean ecosystems. This will be achieved through the analysis and integration of available data together with development of models, scenarios and projections. These activities and their outputs will provide valuable information for ecosystem-based management in the region. Following discussions between ICED and CCAMLR to develop joint activities in this regard, planning is underway for an ICED Workshop focussed on projections of change in Area 48 - Developing projections of the future state of Southern Ocean ecosystems: incorporating uncertainties associated with climate variability and change in CCAMLR’s decision making. This Workshop will be held in Hobart in April 2018 in association with the MEASO2018 conference. This paper provides a brief update of recent ICED activities of relevance to WG-EMM and presents current plans for the Workshop, to which we welcome further input from the WG-EMM.

The shelf regions around South Georgia and the South Orkney Islands are highly productive, supporting large colonies of higher predators, and with a history of commercial exploitation. Many of the key oceanographic and ecological processes determining the operation of these marine ecosystems operate over small spatial scales of <10 km. Understanding the drivers of variability in such processes requires the development of high-resolution ocean models. Here we describe the development of regional models for the South Georgia and South Orkney Islands shelves and surrounding regions, and present the results of preliminary analyses. The models are regional applications of NEMO with a ~3 km horizontal resolution, and include key physical processes of relevance to the local ecosystems including tides, atmospheric forcing from reanalysis, glacial melt, and with sea ice processes incorporated using LIM3. The models have been used to generate 20-year hindcast time series of oceanographic flows and water mass properties. This valuable resource is providing the underlying physical environment for detailed examinations of the controls on the distribution of krill and fish around the islands, their interactions with predators and availability to fisheries. Insight from such studies will help inform WG-EMM activities aimed at developing spatial and feedback management procedures.

• We provide a method of analysing penguin tracking data to identify marine IBAs, based on pre-existing approaches but revised according to the specific ecology of Pygoscelis penguin species;

• Key improvements to the pre-existing approach include 1) refining track interpolation methods, and 2) revision of parameters for the kernel analysis (the smoothing factor and the utilization distribution);

• We applied the revised method to 24 datasets of tracking data (three species, seven colonies and three different breeding stages – incubation, brood and crèche);

• We compared the results of the marine IBA protocol with the results of habitat models, developed with data collected at other colonies;

• We show, for the first time, that maps of predicted distributions based on habitat suitability models can be used with a high degree of confidence to identify marine IBAs for penguins;

• The results reported here provide a new method for the designation of a network of marine IBAs in Antarctic waters for penguins, based on tracking data (when available) and habitat modelling, which can contribute to an evidence-based, precautionary, management framework for krill fisheries.

SO-AntEco was a British Antarctic Survey (BAS) led expedition undertaken in conjunction with an international team of scientists from the Scientific Committee for Antarctic Research (SCAR) AntEco research programme. The team included participants from nine different countries and 15 institutes. The expedition took place on board the BAS research ship RRS James Clark Ross in early 2016. The focus of the cruise was to contribute towards a better understanding of selected benthic habitats around the South Orkney Islands, and the biodiversity within those habitats, in relation to the geomorphic zones both inside and outside the South Orkney Islands Southern Shelf (SOISS) Marine Protected Area (MPA). This addresses a key objective set out by the draft SOISS MPA Research and Monitoring Plan (SC-CAMLR, 2014).

The benthic assemblages of the SOISS MPA region were found to be strongly correlated with substrate, where hard substrates hosted a greater number of individuals, taxa and biomass with a dominance of filter feeding Vulnerable Marine Ecosystem (VME) taxa, and soft sediments were dominated mostly by deposit feeders. Substantial differences in the abundance of VME taxa were found between two sampling gears used (shallow underwater camera system and Agassiz trawl). We conclude that camera systems may be more suitable for VME assessments than the Agassiz trawl, but where possible, trawling is advisable for collecting all faunal types and for higher taxonomic resolution. The designation of VME locations or MPAs based purely on geomorphic classification is not advisable, due to small scale variation in substrate and other local physical influences; however, the utility of such classifications may be improved with the inclusion of additional environmental factors e.g. substrate type.

The results from this study will contribute to an updated MPA Report for the South Orkney Islands, as well as the SOISS MPA review scheduled in 2019. In addition, it makes an important contribution to the understanding of benthic habitats in Domain 1, which will be relevant for further spatial planning in this region.

We present a preliminary analysis to estimate the consumption of prey by Adélie penguins Pygoscelis adeliae in Subareas 48.1 and 48.2 during the period when penguins are constrained by breeding. We parameterised our analysis using data from the population of Adélie penguins breeding at Signy Island, South Orkneys. We show that the highest levels of consumption occur during the crèche and pre-moult period, consistent with similar studies of Adélie penguins breeding in East Antarctica. On the basis of variable breeding success and the proportion of krill and fish in their diet, we estimate that the population of Adélie penguins at Signy Island consumes 4,600 t of krill and 696 t of fish during a breeding season. Based on this we estimate Adélie penguins in Subareas 48.1 and 48.2 respectively consume 304,980 and 53,161 t of prey during a single breeding season. Our study builds on previous work using a similar methodology to assess the consumption by macaroni penguins in Subarea 48.3. We report that further analyses will be undertaken for other krill-eating penguin species in Area 48, in particular chinstrap P. antarcticus and gentoo P. papua penguins.

The inter-vessel variability of krill distribution in the catches obtained in the Bransfield Strait (SSMU APBSW and APBSE) in April and May 2014 and 2015 was analyzed. The presence of statistically significant differences in the krill length distributions from  catches obtained by the vessels operating in the fishing grounds with spatio-temporal overlapping is shown. At the same time, significant differences exist both for vessels using the same trawl fishing method and for vessels operating with different fishing methods. The inter-vessel variability of krill length in the catches is more pronounced with respect to the length distribution (content of young and large specimens), and to a lesser extent to discrepancies in the weighted mean length estimates per SSMU by month and vessel.

An analysis of spatio-temporal variability of CPUE indices in the krill fishery in subarea 48.1 by years and small-scale management units, including inter-annual, monthly and inter-vessel variability with account of the fishing method used is presented. Particular attention is paid to the fishery in the Bransfield strait, where the main fishery in Subarea 48.1 is concentrated  The possibility of using the standardized CPUE indices as krill biomass density indices for monitoring the variability of krill biomass in SSMU during the fishing season is discussed. Variability of monthly average standardized CPUE estimates in the Bransfield strait shows that despite intensive fishery the density of krill biomass during the fishing season did not decrease and even often increased there.  The latter is an evidence of krill biomass replacement and supplement during the fishing season due to geostrophic drift, and this also points to unjustified hypothesis on the fishery effect on krill biomass and dependent predators.