CCAMLR

Distribution patterns and biomasses of Antarctic and ice krill in the Ross Sea in austral summer in 2005 were studied using a multi-disciplinary survey data set combining cetacean, krill and oceanography data. Two research vessels, KM and KS2, conducted the hydroacoustic surveys independently in the same area. Distribution patterns and length frequency information for two species were obtained from samples of RMT hauls and stomach contents of Antarctic minke whales. Ice krill was distributed on the continental shelf region (shallower than 1000m water depth). In contrast, Antarctic krill was distributed mainly in the oceanic waters where water depth is deeper than 1000m though it distributed on the continental shelf where the mean water temperature between 0-200m was higher than -1°C. The Ross Sea was stratified into two strata based on the distribution patterns of two krill species to estimate their biomasses. Biomass densities of Antarctic krill using KM and KS2 data were estimated as 5.13±7.14 and 2.53±2.25 g/m2, respectively. Biomass densities of ice krill using KM and KS2 data were estimated as 2.58±1.47 and 1.13±0.65 g/m2, respectively. Because there was no significant difference between the biomass density estimates from both vessels, two data sets were combined to estimate the biomass. The biomasses of Antarctic and ice krill in this study were estimated as 1.40 (CV=0.32) and 0.60 (CV=0.18) million t, respectively. School sizes of Antarctic minke whales were large where the densities of Antarctic krill were high. Distribution pattern of Antarctic minke whales in the Ross Sea could be regulated by distribution patterns of Antarctic krill.

A work plan to establish an observer scheme for krill fishery was agreed at the previous Scientific Committee and the Commission. The first step was to consider ‘systematic coverage’ of the observers. In this document, Japan proposes a framework for scientific observer system for krill fishery in order to obtain ‘systematic coverage’.

An assessment of the environmental processes influencing variability in the recruitment and density of Antarctic krill (Euphausia superba DANA) is important as variability in krill stocks affects the Antarctic marine ecosystem as a whole. Naganobu et al. (1999) had assessed variability in krill recruitment and density in the Antarctic Peninsula area with an environmental factor; strength of westerly winds (westerlies) determined from sea-level pressure differences across the Drake Passage, between Rio Gallegos, Argentina, and Base Esperanza, at the tip of the Antarctic Peninsula during 1982-1998. Fluctuations in the westerlies across the Drake Passage were referred to as the Drake Passage Oscillation Index (DPOI). They found significant correlations between krill recruitment and DPOI. Additionally, we calculated a new time series of DPOI from January 1952 to March 2008. In addition, we tried to draw a comparison between DPOI and oceanographic condition using CTD data in the Antarctic Peninsula waters during 1990-2007. As a result, DPOI had a significant correlation with mean temperature from the surface to 200 m (MTEM-200). DPOI suggests influence on the variability of oceanic condition and thus for Antarctic krill ecosystem.

Antarctic krill (Euphausia superba) is the key species of the Antarctic marine ecosystem and human fishing resources. Essentially, relationships between krill distribution and oceanographic conditions have been an age-old recurrent problem and many papers have been published on the subject since the British Discovery Reports. However, there was no remarkable achievement in particular the entire Antarctic Ocean. To clarify the relationship between krill distribution of krill and oceanographic environment, we have analysed two datasets combined. One is krill fishing records from 1973 to 2008 from the CCAMLR database. Another is accumulated water temperature data of the World Ocean Database. We here focus mean-field (climatologic analysis) in all season. First, we have examined fishing depth. The peak of fishing catches clearly appeared around 50 m and 94% of all krill fishing catches occurred in water shallower than 200 m. Furthermore, horizontal distribution of krill fishing points concentrated in three waters in the east Antarctic Ocean, the Scotia Sea, and north of South Georgia Island. From the above results, we calculated mean temperature from the surface to 200 m (MTEM-200) and compared it with horizontal distribution of fishing. The result indicated the strong correlation between krill fishing locations and MTEM-200 in the entire Antarctic Ocean. Waters that were efficient and stable for fishing were distributed in a narrow range with steep meridional gradients between -1.0 and 1.0 °C. Large fishing catches indicated the remarkable two peaks; -0.5~0.1 oC and 0.5~0.8 oC which located in the Scotia Sea, and north of South Georgia Island, respectively. Similarly, the historical krill distribution based on the Discovery Report’s net sampling coincided with this study results and each of the isopleths of MTEM-200 substantially corresponded with each oceanic front in the Southern Ocean. MTEM-200 can be applied for the further analysis of seasonal and/or annual variability.

We have surveyed the ecological interactions between the oceanography and biological ecosystem in the Ross Sea and its adjacent waters with a joint survey the R/V Kaiyo Maru and the Japanese Whale Research Program in the 2004/05 austral summer. We compared the relationship between the geographical distribution of the main biological prey and predator populations such as krill including other zooplankton and fishes as well as baleen whales with an oceanographic environment index (namely MTEM-200 which is the averaged temperature in degrees Celsius from the surface to 200 m; note all temperatures given below are MTEM-200 values). - Antarctic krill mainly distributed in the waters between 0 to -1℃ of MTEM-200, which approximated the area covered by the Antarctic Surface Water (ASW) zone, and slightly extended in the waters less than -1℃ of the Shelf Water (SW) zone. Ice krill distributed in the waters colder than -1℃ (SW) but did not ℃ cur in ASW (warmer than -1℃ ). Other zooplankton and fishes also showed distribution patterns that could be approximately segregated patterns with MTEM-200. Humpback whales mainly distributed in the waters warmer than 0℃, which agreed with the Antarctic Circumpolar Current (ACC) zone, with a high density around 0℃ near the Southern Boundary of ACC. Antarctic minke whales mainly distributed in the ASW and SW zones with high density around -1 ℃ in a continental shelf slope frontal zone. The interaction between distributions of krill and baleen whales with MTEM-200 could give quantitative information to identify the boundary of distribution of Antarctic krill and ice krill for biomass estimations using acoustic data. We summarized a geographical image between oceanography relating water mass and circulation pattern of the surface layer with MTEM-200, the distribution and abundance of krill and baleen whales. We conclude that this is useful for characterizing the Ross Sea and adjacent waters ecosystem and comparing other areas in the Antarctic Ocean.

We used Foosa and the reference set of parameterizations developed by Watters et al. (2008) to assess the risks and tradeoffs associated with various management strategies for subdividing the precautionary krill catch limit among SSMUs in Area 48. Our methodological approach follows directly from specifications made by the WG-SAM and the WG-EMM. We predict that the tradeoffs inherent in selecting among Fishing Options 2, 3, and 4 are tradeoffs between the risks to predator populations and fishery performance; risks to krill are relatively insensitive to differences among these options. Implementation of Fishing Option 2 (using the subdivisions reported here) will require that the fishery mostly operate in pelagic SSMUs. Up to harvest rates of 0.5 x γ, this subdivision is unlikely to reduce predator populations to 75% or less of the abundances that might occur in the absence of fishing; the risks of such depletion are, however, likely to increase as harvest rates increase beyond 0.5 x γ. Although we predict that catches can be highest and relatively less variable in pelagic SSMUs, the risks that krill densities will fall below thresholds which necessitate involuntary changes in the behavior of the fleet are substantially increased in pelagic SSMUs. We are uncertain about relative catchabilities in pelagic versus coastal SSMUs, and we do not know how much fishing effort might actually be required to catch the SSMU-level quotas that would be allocated to each SSMU given the subdivisions reported here. Implementation of Fishing Option 3 will also require substantive fishery operations in pelagic SSMUs, but, if krill move, to a lesser extent than Fishing Option 2. Up to harvest rates defining the current trigger level (i.e., 0.15 x γ), implementation of Fishing Option 3 is unlikely to reduce predator populations to 75% or less of the abundances that might occur in the absence of fishing. As harvest rates are increased past that defining the current trigger level, the risks of depleting penguin and fish populations increase in some SSMUs. In general, the risks of depleting penguin and fish populations are greater for Fishing Option 3 than for Fishing Option 2 because the former option requires slightly more fishing in coastal SSMUs. Nevertheless, fishery performance under Fishing Option 3 is comparable to that for Fishing Option 2. Implementation of Fishing Option 4 will, relative to the other two options, substantially limit the spatial distribution of the fishery. Furthermore, since Fishing Option 4 would concentrate fishing in a few coastal SSMUs, implementing this option would increase the risks that predator populations will be reduced to 75% or less of the abundances that might occur in the absence of fishing. In fact, in a few SSMUs, such risks even occur at harvest rates near the rate defining the current trigger level. Relative to Fishing Options 2 and 3, fishery performance under Option 4 is also poor, with decreased catches and increased variations in catch... continued

Knowledge of the scattering properties of Antarctic krill (Euphausia superba) is crucial for biomass estimates of the species, but in situ investigations regarding this are still very scarce. We conducted a field study in the Southern Ocean where one of the objectives was to acquire data on orientation angles and target strength of Antarctic krill. The main investigations were done off South Georgia and the Bouvet Island and we here in part present the methods applied and give examples of data acquired. The post-processing and analyses are ongoing and the results from this work will be presented in future reports.

This report describes the multipurpose AKES survey (Antarctic Krill and Ecosystem Studies) carried out with R/V G. O. Sars, 4.01-27.03.08, including some selected results. The main purposes for the AKES project were to • evaluate the links between the krill resources and distribution in the area and Bouvetøya based mammals and birds • study krill biology and ecology • establish TS (Target strength; the ability of an organism to reflect sound) for krill and ice fish • study aggregations of krill, fish and plankton relative to the hydrography • compare aggregations and abundance of krill and plankton relative to hydrography in Antarctica and Nordic Seas • stomach contents and feeding behavior of krill and fish

Ecosystem models are being developed to explore a range of issues globally. This paper is currently in draft form open to comment and is being developed to provide an introduction to the CCAMLR-IWC Workshop to review input data for Antarctic marine ecosystem models. It summarises background to the use of ecosystem models in CCAMLR and the IWC and a history of the developmental work in those organisations. It also provides an outline of the nature of modelling for these purposes and the general issues that need to be considered in parameterising a model, providing input data for those models and for addressing uncertainties in this process. Lastly, it summarises the modelling platforms being developed in CCAMLR and the IWC and the manner in which uncertainties surrounding data inputs to these models are being addressed by the joint CCAMLR-IWC workshop to be held in August 2008.

We tuned four parameterizations of Foosa to provide predictions that are consistent with the agreed calendar, as specified by the WG-SAM, that describes changes in the abundances of krill and their predators in the Scotia Sea. First, we compiled a set of base parameterizations from information in the literature and following specifications laid out by the WG-SAM. These base parameterizations cover a combinatorial framework that considers krill movement (or lack thereof) and the shape of a relationship determining how the effective abundance of breeding predators depends on foraging success during the breeding season. We also added a new functional relationship to Foosa: a relationship that determines the degrees to which the survival of juvenile predators depends on foraging success in their first winter of life. The dynamics predicted by our base parameterizations were loosely consistent with the direction and timing of changes in predator abundance specified by the numerical calendar from Hill et al. (2008). This indicated that our base parameterizations were reasonable and that tuning to the numerical calendar would be feasible. Second, we tuned, via sums of squares, one stock-recruitment parameter for each predator population in each parameterization to the “empirical abundance estimates” for predators reported by Hill et al. (2008). Tuning the peak recruitment by all 19 predator populations was sufficient to predict the empirical abundance estimates almost exactly for all predators by all parameterizations. The dynamics predicted by these tuned parameterizations often had trends and changes in magnitude that were roughly consistent with those in the numerical calendar, lending additional support to the validity of the initial conditions, un-tuned parameters, and functional forms used in this application of Foosa. Finally, we tuned, via an objective function that minimizes the sum of absolute proportional differences in abundance, one or two stock-recruitment parameters for each predator population to the numerical calendar itself. Parameterizations tuned in this last step constitute our reference set and predict plausible dynamics by reasonably matching the timing of events and magnitude of changes that are specified in numerical calendar. This reference set encapsulates hypotheses that go beyond the basic contrasts between krill movement and predator response to foraging success in the breeding season, implying a diverse set of hypotheses that includes SSMU-specific views about the productivities of individual predator populations and the effects of winter foraging conditions on juvenile survival. All four parameterizations in our reference set imply ongoing trends in predator populations, and, in forward simulations, changes in abundance predicted from these ongoing trends will likely need to be separated from changes caused by krill fishing. Although we believe that all four parameterizations in our reference set are plausible to some degree, we do not think that they are equally plausible. We suggest plausibility ranks for these four parameterizations that might be useful for synthesizing the output of future modeling efforts and simplifying communications with decision makers. After completing our analytical work and writing most of this paper, we found a small error in the initial conditions used in one of our four parameterizations. We discuss why this error does not affect the conclusions presented here or in our follow-on effort to conduct a risk assessment using the reference set.