CCAMLR

Our ninth complete consecutive season of seabird research at Cape Shirreff allowed us to assess trends in penguin population size, as well as inter-annual variation in reproductive success, diet and foraging behaviour. The gentoo penguin breeding population declined marginally from the previous season and is the third lowest population size in the 10 years of census data. The chinstrap penguin breeding population has been declining for the Past eight years and is at its lowest size in the 10 years of study. Gentoo penguin fledging Success was the highest recorded in all the years of study. The fledging success for chinstrap penguins was noticeably higher during the 2005/06 season than in the previous season and was slightly higher than the previous eight year mean. Gentoo penguin fledge weights for this season was the highest recorded in all the years of study. Chinstrap penguins fledge weights increased slightly from the 2004-05 season and were close to the previous eight year mean. Both gentoo and chinstrap penguin diets were comprised mainly of adult female Antarctic krill, the majority of which were 51-55mm in length. This is a continuation of a four year trend with increasing proportions of female krill and increasingly larger krill. Chinstrap penguin total chick meal mass was lower than almost all of the previous eight years of diet sampling; however, foraging trip durations were shorter than during the 2004/05 season. This may indicate that the provisioning rate of chicks by adults may have been higher, which would account for this difference. This interpretation may be aided by analysis of foraging location and diving behaviour data to be done at a later date.

Mark-resight data were analysed for five cohorts (1997-2001) to estimate age-specific juvenile survival, age at first reproduction, and resight probabilities. Longitudinal histories of presence and pregnancy of adult females (aged by cementum annuli) were used to estimate age-specific survival and natality. Data on tagged juveniles were collected from 1998-2005 and for adult females from 2000-2005. These data are used to construct an age-dependent life table for female fur seals breeding at Cape Shirreff and to estimate net reproductive rate, mean generation time and the intrinsic rate of growth for the population.

Spatially explicit simulation models of the Antarctic marine ecosystem are needed to evaluate management procedures for the Antarctic krill fishery. The key issues to be resolved in the development of a harvest strategy are whether (i) spatial differences in the productivity of krill give rise to differential affects on predators in different locations, (ii) movement of krill between locations ameliorate any local fluctuations in krill abundance and (iii) fishery behaviour could be constrained by regional differences in krill dynamics and cause differential impacts on predators as a result, particularly as the fishery expands to take the large-scale catch limit. A fourth issue is to determine whether climate change will impact on the krill-based food web and whether the potential for achieving conservation objectives for predators could be affected by those changes. Productivity of krill can be impacted by sea temperature and available production. Similarly, survivorship and successful recruitment of juvenile krill is likely to be dependent on the dynamics of sea ice. This paper uses the Ecosystem Productivity, Ocean and Climate (EPOC) modelling framework to develop a spatially explicit model of Antarctic krill, Euphausia superba, within a wider ecosystem context (ocean, productivity, krill and predators) in the southwest Atlantic in order to explore the potential for spatial differences in krill productivity and their affects on predator productivity and fisheries. It uses satellite data as proxies for the key physical environmental drivers that may affect productivity. Illustrative results show that spatial and temporal variability of krill productivity is likely and that attention needs to be given to appropriately parameterising models to explore the sensitivity of management outcomes to these differences.

Large ecosystems are often partitioned into spatial compartments (bio- or biophysical regions and/or ecoregions) in order to better understand the relative importance of ecosystem processes or for the purposes of managing human activities in relatively ecologically discrete areas. Regionalisation algorithms attempt to partition a broad spatial area into discrete spatial regions, each with relatively homogeneous and predictable ecosystem properties but with properties different from neighbouring regions. The Southern Ocean has been divided up into regions before, primarily based on frontal features. In this paper, we demonstrate a method developed for a regionalisation of the southern Indian Ocean in order to facilitate the development of ecosystem models for the area. Here, we extend this work to other areas of the Southern Ocean to see how well the approach might be applied more generally and therefore be of assistance to large scale ecological modelling and, perhaps, to CCAMLR in its work to develop a bioregionalisation of its Convention Area. This paper describes the steps and issues in undertaking a regionalisation and presents a statistical method for achieving a regionalisation of the Southern Ocean in an objective and consistent manner. Results are presented for each of the three CCAMLR Areas. We conclude that it is tractable to resolve the challenges facing the subdivision of the ocean into meaningful regions for the purposes of modelling and management.

The analysis of krill density and biomass distributions was made by the example of SGE and SGW which are located in ecologically crucial areas affecting land-based predators around South Georgia, being the traditional krill fishing areas. The data collected during the 2000 CCAMLR Survey and Russian survey 2002 were used. Processing of the acoustic survey data in 2000-2002 was made applying geostatistical methods.
It was revealed significant inter-annual and seasonal variability of the standing stock and its fishable part in each SSMU. The relationship between the biomass available to fishery and the total biomass may vary by SSMUs, e.g. in SGE this relationship based on the two surveys data constituted 0.18 and 0.40, and in SGW - 0.06 and 0.24 respectively . The survey 2002 indicated that the significant differences between commercial biomass can be obtained for areas with the comparable total biomass, e.g. total biomass constituted 1.0587 mlt for eastwards of 37°W and 1.000mt for westward of 37°W , but commercial biomass was 0.41985 mlt and 0.24 mlt respectively.
The option “standing stock of krill in the SSMUs”, considered for allocating this catch limit among the SSMUs in the Scotia Sea, has to be supplemented with the fishable biomass estimate within SSMUs.
A success of assessment of the standing stock and its fishable part will be related to the problem of acoustic data processing improvement. The methods specially designated for spatially distributed data are preferable. Special researches are necessary to compare the precision of different methods of spatial data processing, such as classic geostatic methods, Maximum Entropy method, Bayesian geostatic methods based on the maximum Entropy principle

Many modern spatially explicit ecosystem models use modelling subdomains of different shape and size (‘polygons’) to resolve space, and movement of biomass between them forms an important part of the modelling effort. In marine applications, a flow field grid describing the water movement usually forms the basis for movement of passive or nearly passive biomass. Grid-based advection algorithms are not designed to model movement on the larger scale of polygons, resulting in disproportionately large computational costs and difficult communication between model layers. In this paper, a simple and effective algorithm to model movement at the polygon level is proposed, preserving the general properties of biomass distribution in comparison to a grid-scale model. A nonparametric description of inter-polygon movement in the domain is generated which is used to approximately replicate the observed movement pattern. To estimate the movement description, the moves of passive numerical drifters between polygons are observed. The resulting algorithm outperforms the conventional polygon-based transport equation approach both in artificial and realistic scenarios.

Brief description of scientific observation aboard Ukrainian krill vessel Konstruktor Koshkin in 48.1, 48.2 Subareas in the last fishing season.

Description of some biological and ecological investigations of Ukraine in Antarctica in vicinity of Argentine Islands Archipelago are presented. Main activity: investigations of birds populations, krill population, complete meteorological observations, hydrological researches, long-term variations study of the atmosphere ozonedepleting organic halides, study of the ice caps and glaciers dynamics, influence on environment pollution.

Using data collected from US AMLR surveys conducted in the South Shetland Islands we review the trends in biomass, size frequency distribution and proportional recruitment of Antarctic Krill (Euphausia superba) between 1997 and present. Over the last five years proportional recruitment as been extremely low, and the population has aged, suggesting there has been little recruitment since 2000-2001 season. Relative biomass, estimated from acoustic surveys, and calculated using both the Greene et al (1991) and the simplified Stochastic Wave Borne Approximation (SDWBA) target strength algorithms are compared. Biomass estimates from the two algorithms are highly correlated (>0.95), with identical CV’s. Use of acoustic windows based on the range of krill size increases variability in biomass estimates and CV’s. These data suggest that future development focus on better propagating error through levels of analysis, to better account for process and model error structure, now that a physically based krill target strength model has been developed.