CCAMLR

Naganobu et al. (1999) had assessed variability in krill recruitment and density with hypothesized environmental factors; strength of westerly winds (westerlies) determined from sea-level pressure differences across the Drake Passage, sea ice cover and chlorophyll-a in the Antarctic Peninsula area during 1982-1998. They found significant correlations between krill recruitment and those factors. The westerlies were especially regarded as a key environmental index. Fluctuations in the westerlies across the Drake Passage were referred to as the Drake Passage Oscillation Index (DPOI).
We planned to extend time series of DPOI using historical data. We searched the historical data and found the time series since the 1950s until 1988 at the Web sites of the Carbon Dioxide Information Analysis Centre for Rio Gallegos and since the 1940s until 1998 at British Antarctic Survey for Esperanza. Here we calculated a time series of DPOI from 1952 to 1988 at this stage. Time series since 1988 will be soon calculated after obtaining appropriate data.
The total number of monthly data used from 1952 to 1988 was 420. The mean, median, and mode were 13.6, 13.8, and 14.0 hPa, respectively. The maximum, minimum, and range were 27.5, -6.4, and 33.9 hPa, respectively. The standard deviation was 6.2. Linear regression declined as a whole from 1952 to 1988. A time series of 3-month running mean suggested considerable seasonal variability of climate. A time series of 12-month running mean indicated various yearly changes without seasonal variability. High DPOI periods, not less than 16 hPa, were mostly observed in the period before 1964 and only in 1973 and 1986-88 after 1964. Low DPOI periods, less than 14 hPa, were continued longer after 1964. A low DPOI less than 10 hPa appeared in 1967 and 1980. Intervals between the years of low DPOI were generally observed for around 3 years except 6 years between 1958 and 1964.

We assessed the environmental variability of Antarctic krill (Euphausia superba) distribution with comparison between the CCAMLR 2000 Survey and similar scale datasets partially by the Japanese R/V Kaiyo Maru Survey in the 1987/88 austral summer season in the Scotia Sea. There were distinct differences between the 2000 and 1987/88 Surveys with regard to sea ice extent, oceanographic structure and krill distribution. The sea ice cover in 1987/88 extended northward widely during the last winter season such that sea ice remained around the South Orkney Islands until December 1987. In contrast, the sea ice cover in 1999/2000 reduced southward such that no sea ice remained around the South Orkney Islands in December 1999. The Antarctic Surface Water mass, consisting of Winter Water and Summer Surface Water, in 1987/88 extended northward and covered a large area in the Scotia Sea. In contrast, the Antarctic Surface Water in 2000 reduced southward. Geographical distribution of krill, which approximates the area of the Antarctic Surface Water, in 1987/88 extended northward with high density. In contrast, the distribution of krill in 2000 reduced southward with low density. To generally understand the above relationships between oceanographic structure and krill distribution, we introduced integrated water temperature from the surface to 200m (Q200) as an environmental index indicating the structure of the upper ocean, that is referred to as the Environmental Index (EI Q200 ). The isoline of EI Q200 =0.0 ? was located near 60S northward off the South Shetland Islands in 1987/88. In contrast, the isoline of 0.0 ? in 2000 was located in the Bransfield Strait and Weddell Sea southward off the South Shetland Islands. The Antarctic Surface Water in 1987/88 clearly developed northward compared with 2000 reduced southward. The geographical distribution of krill ranged over the area under the isolines of EI Q200 =1.0 ? in the western waters and 2.0 ? in the eastern waters of the Scotia Sea. Krill density became higher with the colder isolines of EI Q200 =0.0 ? , especially south of its steep gradient, namely, the Southern Boundary of the Antarctic Circumpolar Current. It suggested that the geographical distribution of three krill size clusters in the 2000 Survey (Siegel et al., 2002) corresponded with the distribution pattern of EI Q 200 on the whole.

Over 120 adult Adélie penguins were found dead in unusual circumstances on Welch Island near Mawson between 23 November and 4 December, 2001. It is concluded the most likely cause of death was severe injury from being crushed by ice at the ice land interface. Circumstance surrounding the death of the penguins suggested initially that disease may have been implicated and so investigations were treated accordingly. The sampling protocols developed by CEMP were used in these investigations.

One step toward defining small-scale management units is to determine the areas most likely to be foraged by predators from one year to the next, i.e. what is a predator’s feeding range taking into account interannual variation in foraging locations? This paper considers the issues to be addressed in answering that question. The proposed method for defining foraging ranges is based on an approach used to define fishing grounds. The data which is considered as an example here consists of location/time recordings from a satellite tracking system. This data is used to generate a map of feeding effort and this is used to delineate a feeding ground for Adélie penguins on the Mawson coast in eastern Antarctica. This method builds on existing methods but incorporates tools for pooling information across colonies, years, and species to define individual species and pooled foraging ranges. The establishment of these foraging ranges for the purposes of small-scale management units may need to be examined in three parts. The first part is to determine whether the results would be different for different seasons e.g. summer vs winter. The second part is to establish the combined foraging ranges across a number of species for which tracking information is available – pooled foraging ranges. The third consideration is whether some species with low colony biomass have large proportions of their foraging ranges falling outside of the specified pooled foraging ranges. If this makes those species vulnerable in the management process then consideration will need to be given to including those ranges as special extensions to the foraging grounds. Thus, a comparison of foraging ranges for individual species with the pooled foraging range would be a useful step in this process.

This paper provides a method for delineating krill fishing grounds in Area 48 based on commercial catch data for the region held in the CCAMLR database. It also summarises available information on krill distribution and abundance and movement for the region, which can be used to help understand the relationship between the fishing grounds and the krill population. We define a fishing ground as being a predictable location where the fishery obtains relatively reliable catches from one year to the next over a number of years. The quantity of interest is not only the total catch obtained from a location, say a 10 x 10 nmile area, over the years but how important that location is to the fishery each year, which is judged by that location providing a reasonable catch in a given year and that the catch remains sufficiently high on average over a number of years. We call this value the normalised longterm average catch (shortened to the term ‘normalised catch’). An important consideration is the threshold for the normalised catch, such that locations would generally only be considered for inclusion in a fishing ground if their values were greater than the threshold. A method for choosing a threshold is given. The boundary for a fishing ground should predominantly include only locations for which the normalised catch is greater than the threshold. Some simple criteria for designating fishing grounds are presented. The type of analytical tool needed to convert the data to a longitudelatitude grid of normalised catches and for determining boundaries on the grid according to the criteria is also discussed. The components of this process are developed using the commercial krill catch data available in the CCAMLR database.

Four separate acoustic surveys of Antarctic krill (Euphausia superba) were conducted around South Georgia in the 2001/2002 season: one in November 2001 (early); two during January 2002 (middle), and one in May 2002 (late). The surveys were the second in a five-year series of observations designed to complement and extend an existing time series of summer surveys maintained by the British Antarctic Survey regularly since 1996. Krill density in November was low (5 g m-2), higher in both the two surveys in January (46g m-2 and 72 g m-2 ) and had decreased to 12 g m-2 by May. Our repeated surveys at South Georgia have revealed a similar pattern of change to that observed in 2000/2001 and highlight the importance of understanding the relative contributions of physical and biological processes to krill population dynamics in the region.

During austral summer (January 21st to February 5th) 2000 an oceanographic cruise, devoted to study two krill species (Euphausia superba and Euphausia crystallorophias), was carried out in the Ross Sea area. Activities included acoustic, fishery and physical measurements. More than 2370 nautical miles were acoustically sampled to determine the euphausiid biomass; during the echosurvey, every 6 hours a haul and a CTD cast (or a XBT launch) were performed, and a XBT was launched between consecutive hauling stations. This allowed to collect 34 CTD stations and 73 temperature profiles (XBT), so identifying main water masses and oceanographic features. Acoustic data were processed in order to distinguish between the two species. Euphausia superba and Euphausia crystallorophias swarms were recognized, and average length estimated, by means of the three-frequency method, based on the fluid sphere model. Net samples were considered the ground truth data, being compared to the acoustic estimates of krill species and size. Characteristics (dimensions, volume, weight, krill mean length) for each krill swarm were determined, and the krill average biomass per squared nautical miles was computed along the ship route track. Adopted methodologies for cruise execution and for acoustic data post-processing allowed to obtain for the first time a detailed description of the krill distribution in the Ross Sea area related to oceanographic characteristics. Horizontal distribution of krill average biomass is showed separately for each species, and associated to thermohaline properties. Highest krill density biomass areas are closely investigated, and vertical sections with krill swarms weight and sea temperature are reported. Results indicate that the Euphausia superba detected biomass was about one order of magnitude greater than Euphausia crystallorophias one. The first species was almost exclusively present in the northern area, interesting only a limited portion of the continental shelf, while the second one dominated the southern area, starting from the Ross Ice Shelf region until the zone close to the shelf break, with some presence in the open ocean region too. In the proper shelf break area, Euphausia crystallorophias was practically absent, while Euphausia superba was relatively abundant. The two species had a very limited, but with relatively high biomasses, overlapping area in the northernmost part of the Joides basin. Few other minor overlapping areas were detected. Swarms of both species were mostly found in the surface water layer (Euphausia crystallorophias being located at quite deeper depths, often close to the seasonal thermocline zone) and they appeared to prefer cold waters, avoiding the warm Modified Circumpolar Waters and the warmest portion of the surface waters.

In this paper an acoustic method for identifying two euphausiid species and estimating their length is described. The approach is in fact an outgrowth from both the fluid sphere and Bayes rule methodologies.
This paper explores applications of the multi-frequency method using data from three expeditions to the Ross Sea (1980-90; 1997-98 and 1999-2000), where the environmental conditions, the sampled areas, the instrumental and the sampling strategies varied.
First, on the basis of the echo-integrations, made simultaneously either at two or at three frequency, and of the results of net samplings, the thresholds and the decision criteria to recognize the two species are established.
Next the acoustic estimates of euphausiid lengths, derived from the fluid sphere model, are compared with lengths collected from net samplings.
Finally, the developed criteria and algorithms are effectively applied to estimate E. superba biomass found in the area of the Ross Sea investigated in the December 1997 and in January/February 2000. The results are compared with those obtained from the standard method.

We present Maximum Entropy (MaxEnt) reconstructions of krill distribution and estimates of mean krill density within two survey boxes of dimensions 80 km x 100 km to the north east and north west of South Georgia. The reconstructions are generated from acoustic line transect survey data gathered in the boxes in austral summers 1996 – 2000. Krill densities had previously been determined at approximately 0.5 km intervals along transect for each of the ten 80 km transects in each box, providing about 1600 krill density estimates per box. The MaxEnt technique uses a Bayesian approach to infer the most probable krill density for each of the 32000 0.5 km x 0.5 km cells in each survey box, taking explicit account of the spatial relationship between densities in the observed data. Despite some very large interannual and regional (east box cf west box) differences in mean krill density, the MaxEnt approach seems to work well, providing plausible maps of distribution. The technique also yields mean krill densities for which the confidence limits are often narrower than for estimates based upon more conventional (Jolly and Hampton, 1990) analyses.

We present a maximum entropy (MaxEnt) method for inferring stock density and mapping distribution from acoustic line-transect data. MaxEnt is founded on the bedrock of probability theory and allows the most efficient possible use of known data in the inference process. The method takes explicit account of spatial correlation in the observed data and seeks to reconstruct a distribution of density across the whole survey area that is both consistent with the observed data and for which the entropy is maximized. The method is iterative and uses the Bayesian approach of evaluating the posterior probability of a candidate solution under the constraint of the observed data to progress towards a converged solution. We apply the method to reconstruct maps of distribution of Antarctic krill throughout areas 100 x 80 km. Survey data were integrated at 0.5 km intervals along ten 80 km transects, giving approximately 1600 observed data. We inferred krill density for all 32000 0.5 x 0.5 km cells in the area. The method is computationally demanding but appears to work well, even in cases when the distribution of density is highly skewed. The MaxEnt technique has proved powerful for reconstruction of quantitative images from incomplete and noisy physical data (e.g. radio telescope data) and we suggest that it could be of benefit to the fisheries acoustic community, increasing the accuracy of acoustic estimates of stock density and generating superior maps of stock distribution.