CCAMLR

During 1998/99 austral summer the Adélie penguin monitoring program was carried out for the fifth year. The study site was located in the Adélie penguin (Pygoscelis adeliae) colony of Edmonson Point (74°20'56.7" S, 165°08'10.03" E). The aim of this research was to obtain data to contribute to the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) Ecosystem Monitoring Program and data on the feeding ecology of the Adélie penguin. The rationale for the program is the possibility that the harvest of krill may impact on the reproductive success of its major predators, in this instance the Adélie penguin. It is believed that any such impact will be difficult to detect initially because this species is long lived and changes which may affect the long term survival will be subtle. Large sample sizes and long monitoring research will be required to detect change. The penguin rookery was studied in terms of colony layout, breeding chronology, foraging trip duration, feeding localities and diet composition of breeding male and female. Penguins were monitored using satellite transmitters, time-depth recorders, and electronic tagging .. An Automated Penguin Monitoring System (APMS) was installed which records the weight, identity and direction of penguins as they move between the sea and their breeding colony and support these data with direct observations. The penguins at Edmonson Point are guided by small fences to cross the weigh bridge. This enables researchers to monitor all birds which enter the study area of 500-600 nests. The results of this study up today allowed documentation on colony trends and on breeding biology and showed gender differences in foraging strategies among different stages of reproductive period and among different study seasons. Moreover the automated system enabled collection of baseline data on Edmonson Point penguins population reducing researchers disturbance to the colony.

The antioxidant defences in aerobic organisms represent the detoxification pathway against toxicity of reactive oxygen species (ROS). These highly reactive molecules are normally produced during the 4-electrons reduction of molecular oxygen to water coupled with oxidative phosphorylation and during the activity of several enzymatic systems which produce ROS as intermediates. If the antioxidant capacity is exceeded (i.e. as a consequence of enhanced intracellular formation of ROS) a pathological condition, generally termed as oxidative stress, may arise.
In this preliminary work, susceptibility to oxidative stress has been compared in Adélie penguin (Pygoscelis adeliae) and South polar skua (Catharacta maccormicki), breeding at Edmonson Point (Wood Bay, Ross Sea). At the time of sampling, Adélie penguins were rearing chicks and also for skuas, the redation of eggs and chicks make this period of their biological cycle very stressful.
In the framework of the Italian Research Program in Antarctica (PNRA) , blood samples were collected during the Austral summer 1998-99 and the Total Oxyradical Scavenging Capacity (TOSC) analyzed. The TOSC assay, measuring the capability of biological samples to neutralise different oxyradicals, has been recently standardized to provide a quantifiable value of biological resistance to toxicity of ROS.
Penguins exhibited higher scavenging capacity towards peroxyl radicals than South polar skua. The greater resistance to toxicity of oxyradicals might suggest that penguins are naturally exposed to an higher basal prooxidant pressure in comparison to skuas.

Antarctic and sub-Antarctic seabirds, marine mammals, and human fisheries concentrate their foraging efforts on a single species, Antarctic krill (Euphausia superba). Because these predators may have a significant effect on krill abundance, we estimated the energy and prey requirements of Adelie (Pygoscelis adeliae), chinstrap (Pygoscelis antarctica), and gentoo (Pygoscelis papua) penguins and female Antarctic fur seals (Arctocephalus gazella) breeding on the South Shetland Islands, Antarctica and compared these estimates with catch statistics from the Antarctic krill fishery. Published data on field metabolic rate, population size, diet, prey energy content, and metabolic efficiency were used to estimate prey requirements of these breeding adult, land-based predators and their dependent offspring. Due to their large population size, chinstrap penguins were the most significant krill predators during the period examined, consuming an estimated 7.8 x 108 kg krill, followed by Adelie penguins (3.1 x 107 kg), gentoo penguins (1.2 x 107 kg), and Antarctic fur seals (3.6 x 106 kg). Total consumption of all land-based predators on the South Shetland Islands was estimated at 8.3 x 108 kg krill. The commercial krill fishery harvest m the South Shetland Island region (1.0 x 108 kg) was approximately 12% of this. Commercial harvest coincides seasonally and spatially with peak penguin and fur seal prey demands, and may affect prey availability to penguins and fur seals. This differs from the conclusions of Ichii et al. who asserted that the potential for competition between South Shetland predators and the commercial krill fishery is low.

The current long-term estimates of mean recruitment rates suggest that the population is unsustainable, as they are too low to maintain the estimated mortality rate. The variable annual estimates of recruitment to the population can be used to model in detail interannual variation in the population dynamics of krill and estimate the expected mortality rates. A number of models of the population dynamics of krill are used to assess to what extent they can explain the observed changes in the density of the population in the Antarctic Peninsula region. Two approaches have been explored: the first uses the bulk density estimates and uses a non-linear regression method to estimate the mortality rate. The second method develops a fully age-structured population model and uses only the recruitment data to develop a model of the long-term dynamics. Data on the recruitment of the first and second age groups were used to derive different estimates of mortality rates. Both model approaches applied to the recruitment data for the first age class produced an instantaneous mortality rate estimate of approximately 0.6 (?43% per annum). In both cases however the mortality rate estimate is poorly constrained in a range from about 0.3 to 1.0 (26%-63%) and the long-term trajectories of density estimated by the models give a relatively poor fit to the observed data. Using the recruitment data for the second age class produced higher mortality rate estimates of between 0.8-1.0 (59-63%) and produced better fits to the observed density changes. The need for caution in interpreting the model results was emphasized by an analysis of the sensitivity, which showed that the density data strongly constrain the model trajectories, which are less sensitive to changes in the recruitment rates.