CCAMLR

In 2008, the Ukraine submitted a paper to the Scientific Committee highlighting the magnitude of the scientific uncertainties and data gaps affecting the subdivision of precautionary catch limits among SSMUs in Area 48 (CCAMLR- XXVII/43). The Ukraine suggested that a research plan was necessary for CCAMLR to fill those gaps, together with developing in future a funding mechanism to support the data obtaining process financially. The discussion of that issue at 2009 WG-EMM meeting will be useful in a course of the consideration a decision on a Stage 1 allocation. In addition, this meeting provides a good opportunity to discuss the possibility for the working group to come up with a research and monitoring plan for krill in Area 48. Due to the high level of risk associated with maintaining the current fishing pattern, in the event that WG-EMM is not able to deliver a recommendation on Stage 1 allocation, the working group would consider interim protective measures to protect krill predators from the impacts of fishing. In order to adequately incorporate current uncertainties in regards to krill, krill catches, predators, and environmental, the level of combined uncertainties could be quantified through a simple index - “uncertainty coefficient”.

The research project to digitize former Soviet krill fishing research, exploratory and commercial expedition’s data has been started in Ukraine at the Southern Research Institute of Marine Fisheries and Oceanography. This valuable set of krill data was collected during Soviet Union krill-fishing expeditions in Southern Ocean between the years of 1972-1992. The main purpose of these expeditions was to collect data and samples and to create an estimation of the commercial exploitation of the Antarctic krill stock. The data is currently recorded in the paper forms as expedition logbooks and represent an exclusive set of observations for investigation of the environment and the impact of climate change on krill in the Southern Ocean ecosystem. The data covers more than 50 fishing cruise observer logbooks with krill fishing trawl sets, biological krill sampling. Logbook pages are scanned and the information is digitized in the CCAMLR electronic C1 format. The project will continue to provide the krill fishing efforts and distribution data in Convention areas in electronic format for scientists and CCAMLR consideration for estimations of the changes to the krill ecosystem in connection to climate change and fishing efforts. This project is started under support by the USA based Pew Charitable Trusts’ Antarctic Krill Conservation Project.

In 2008 fishing season the Russian flagged vessel 'Maxim Starostin' started fishing operations for krill in the Convention area. The vessel used both conventional and continuous trawl technology. Two Russian national scientific observers were present during all fishing operations of the 'Maxim Starostin'. Their observation plan targeted at monitoring the amount and size/sex composition of the krill catches, bycatch, spatial distribution and biological condition of krill. This paper provides initial analysis of the 'Maxim Starostin' catch data collected between January and March, 2009 in the area adjacent to the Southern Orkney Islands (CCAMLR Area 48.2). The data obtained during the observation period indicate the presence of several types of Antarctic krill aggregations in South Orkney Islands region. These aggregation have complex size structure and apparently consist of several successive cohorts. The aggregations with dominance of small-, average- and large-size krill have been allocated. Reproduction of krill in these aggregations occurs at different time and, apparently, with different intensity that gives grounds to suggest the possibility of variation in the off-spring survival rate.

The establishment of a new higher predator monitoring location on the north coast of South Georgia is described. Preliminary results obtained from the 2008/09 season are presented. Although this is the first year of monitoring, breeding success of predators monitored in the Cumberland Bay area in 2008/09 was low, consistent with monitoring at Bird Island and coinciding with low krill abundance observed at South Georgia in early 2009.

The diet of mackerel icefish was investigated from stomachs of 828 fish caught on the South Georgia and Shag Rocks shelves in January 2009. The results show that the diet of this normally krill-dependent species was dominated by the hyperiid amphipod Themisto gaudichaudii (92 % IRI) and was low in Antarctic krill (7 % IRI) compared with similar data for the same month in 2004-2006. The results are consistent with other indicators of krill availability on the South Georgia shelf, which indicate that the 2008/09 season was a particularly poor one for krill. Comparison of frequency of occurrence of krill in the diet with earlier data sets (1967-1992) show that 2009 is not unique, as similarly poor krill years occurred in 1969, 1978 and 1991. Spatial analysis of icefish diet indicates heterogeneity in the occurrence of prey, which is likely indicative of krill distribution. The utility of icefish diets as indicators of krill availability and distribution are considered.

The CCAMLR ecosystem monitoring programme (CEMP) primarily indicates the short term response of air breathing predators to localised environmental conditions. CEMP data do not directly indicate population size, which is the parameter that many conservation objectives attempt to control. Furthermore, CEMP data cannot be used in a standard environmental impact assessment framework as they lack control sites. Identifying how these data could be used in an ecosystem management strategy is therefore an important challenge. Potential strategies are likely to consist of a method for inferring impacts and a schedule of tactical interventions in response to these impacts (e.g. restrictions on fishing activity). We discuss a range of inference methods which are tractable using CEMP data. These methods either assess the expected probability of an observed value in an unimpacted system, or they assess the frequency of values below a fixed reference point. The former approach allows inference criteria based on changes in this frequency rather than by reference to a critical probability. For example, this approach would have provided a timely and sustained indication of a non-fisheries impact on fur seal pup production at Bird Island from the early 1990s whereas critical probability methods would not have detected the impact until almost a decade had elapsed. Shorter reference periods over which the frequency is assessed increase the risk of Type II error (failing to detect a real impact, which is a risk to the ecosystem). Longer reference periods increase the risk of Type I error (falsely detecting an impact, which is a risk to the fishery) and of detrimental delays in the management response. Higher frequencies of low values required to infer an impact decrease Type I risk while increasing Type II risk. An example in which low values occur according to the binomial distribution illustrates the trade-offs between these risks. No inference method can eliminate these risks, but characterising the trade-offs allows managers to choose inference criteria which match their management approach. This could include minor interventions based on subtle indications of an impact. Our example impact was characterised by an increase in the frequency of very anomalous observations with no detectable change in the frequency of moderately anomalous observations. We therefore recommend that ecosystem managers should compare the state of indicators with several (moderate and extreme) reference points and that the response to an impact should be determined by the dynamics of the system.

1. CCAMLR International Scientific Observer data collected from conventional trawl vessels fishing for krill in Subarea 48.3 were analysed using Variance Components Analysis. Krill mean and median length and larval fish bycatch were analysed.
2. In Subarea 48.3, a partial coverage sampling programme has been implemented since 2002. Observers have been placed on approximately 50% of vessels fishing in any one season, and have been present on board for about 30% of the fishing season. They have achieved a rate of sampling equivalent to 18% of total hauls taken in a season being sampled for krill and 11% for larval fish.
3. For both krill and larval fish between vessel variance was slightly lower than between haul variance, the between vessel variance being about 45% of total variance. However, the ratio is sufficiently close to 50% that sampling needs to be efficient at both vessel and haul level.
4. We propose that an efficient sampling proportion, at least for 48.3, should be >50% of vessels sampled each season, 20% of total season hauls sampled for krill and an equivalent or higher sampling proportion for larval fish.
5. The Scientific Committee’s method of systematic partial coverage appears to have been sufficient in subarea 48.3 to determine appropriate coverage levels in that fishery. The 48.3 data suggest that such strategies should be pursued for at least 4 years before the Scientific Committee will have sufficient data to determine appropriate sampling strategies.

This paper discusses some of the implications of climate change and how these concerns necessitate that CEP and CCAMLR address a number of key issues if both organisations are to fulfil their international obligations. We suggest that in order for CEP and CCAMLR to undertake their respective schedules of work, it will be essential for them to try to determine the relative risks (uncertainty), impacts and timescales, of the various processes consequent on climate change. With current levels of understanding, such a risk assessment should be feasible and should provide a focus for future work. As part of this process, we consider that it will be important to focus on issues that reduce uncertainty by the greatest amount. All of the risks described in this paper probably vary with latitude and longitude, with regional climate change, with local intensity of fishing or tourism, and with local foodweb structure, etc.. Therefore, a plan for the future would likely involve delegated responsibility (e.g. to CEP, or CCAMLR, or SCAR) for each of the risks described.

The marine pelagic system around South Georgia is characterised by considerable inter-annual variability which is linked to large-scale climate variability, indicated and probably mediated by local oceanographic and atmospheric conditions. Much of the observed variability in various fitness metrics for birds, seals and fish seems to be connected with the availability of Antarctic krill, Euphausia superba, which is a major prey species for many vertebrate populations at South Georgia. The marine ecosystem at South Georgia is the focus of a major research effort and consequently there are many sources of data available indicating its state. These include remotely-sensed satellite data, fishery data, surveys of krill and mackerel icefish, and monitoring of land-based predators. Each of these data sources revealed a strong anomaly in early 2009. Above average sea temperatures, without any evidence of increased warm water inflow were followed by reduced icefish catches, a paucity of krill in icefish and penguin diets and low krill biomass in the regularly surveyed “Western Core Box”. Consequently many of the nominally “krill dependent” populations of land based predators produced under-weight offspring and the combined standardised index of CCAMLR ecosystem monitoring programme data from Bird Island reached its lowest level in over two decades of monitoring. These observations provide support for the idea that krill is a major mediator of climatic effects on the ecosystem. They also suggest that icefish diets, in addition to metrics from land-based predators, can provide a useful and, potentially, early indication of krill availability.