CCAMLR

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.

Over the past few years WG-EMM has been developing a management procedure for the krill fishery in Statistical Area 48. This procedure will involve, inter alia, subdivision of the precautionary catch limit among a set of small-scale management units. So far the Working Group has identified six candidate methods for subdividing the catch limit and rejected one of them as unsuitable (Option 1 - subdivision based on the distribution of historical catches). In recognising the need for a “staged development” of the management procedure, the Working Group has agreed to defer the development and evaluation of two further options (Option 5 - adjustable catch limits within SSMUs and Option 6 - structured fishing) until a future date. The remaining three options propose subdividing the catch limit according to the distribution of predator demand (Option 2), krill standing stock (Option 3), or the difference between these two (Option 4). In this paper we review the uncertainties relating to the spatial distribution of predator demand and krill standing stock and assess their potential implications. We consider that a strong case can be made against Option 4, which is likely to increase ecosystem risk when the underlying estimates of consumption and/or standing stock are uncertain, especially when they are biased. We suggest that the data documenting the distribution of krill standing stock are likely to be more reliable than those documenting the distribution of predator demand, leading us to favour the use of Option 3. However, we note that though Option 3 appears to be the most favourable, there is little documentary information about temporal variability in the spatial distribution of krill biomass, emphasising the need for monitoring and model-based risk assessments. Finally, we conclude that WG-EMM cannot delay the subdivision of the precautionary catch limit without incurring some risk.

Relatively little ecological information is available for the South Sandwich Islands, so the diversity of information available to determine potential SSMU boundaries is limited. Nevertheless, a suggested SSMU is proposed, based on the foraging distance of the most abundant land-based consumer of krill breeding on the islands, the chinstrap penguin. The proposed SSMU represents a single ecological entity with no internal boundaries. It is based on the best information currently available.

In 2007 WG-SAM defined a set of reference observations for validating and tuning proposed models to evaluate krill catch allocation options for Area 48 (the SAM calendar). The observations, which were endorsed by WG-EMM, were largely qualitative and relative. We used available data to translate these observations into numerical terms (the numerical calendar). We provide spatially-resolved reference points for the density of krill, and the abundance of “generic” seals, penguins and whales in 1970, 2007 and at least one intermediate year. Recent work on baleen whales indicates a higher growth rate than that suggested by WG-SAM, so the numerical calendar for this taxon deviates from the SAM calendar. The numerical calendar is a partly subjective interpretation of limited data and should not be considered a definitive description of the relevant dynamics. This exercise resulted in population sizes for several taxa that are adjusted for asynchronous observations and are potentially more suitable for initialising models than those published in Hill et al (2007).