CCAMLR

The size of two of the inaccessible (non-breeding) components of the Adélie penguin population associated with the Béchervaise Island breeding population is estimated over the past decade using a series of mark-recapture analyses and some simple population modelling techniques.

Broad-scale surveys of penguin breeding abundance generally rely on on-off counts of adults, nests or chicks across several or many breeding sites, and the timing of these counts is often outside the control of researchers. Time series counts of Adelie penguin breeding population attributes (adults, nest and chicks) within the breeding period show considerable variability across space and time (between years). Given this variability, there will be substantial uncertainty in correcting one-off counts with date-specific correction factors to estimate the population attempting to breed at the beginning of the breeding season.

This paper presents spatial modelling based abundance and density estimates of pack-ice seals, crabeater, Weddell and leopard seals, based on aerial survey line transect data collected in January 1999 under the Antarctic Pack-Ice Seal (APIS) programme. Estimates are reported for the Antarctic Peninsula and the western Weddell Sea region (90º to 30º W and 60º to 80º S).

More white-chinned petrels (Procellaria aequinoctialis) are accidentally killed in fisheries than probably any other seabird in the world, but the population impact of this mortality is poorly understood, partly because there have been no estimates of the species’ abundance in recent decades. The largest breeding aggregation, comprising the majority of the worldwide total, is believed to be on the sub-Antarctic island of South Georgia. We estimated the size of this population by calculating the area of suitable habitat and the density of occupied burrows within it. Just less than one million pairs of white-chinned petrels laid on South Georgia in the survey seasons (2005/06 and 06/07). This is 50% of the previous estimate, but still represents around two-thirds of the global population. If the population is declining due to fishery bycatch off S America, as is likely, the scale of annual mortality in this population alone is at least in the high tens of thousands, and plausibly hundreds of thousands.

This paper describes methods and results from a recent aerial survey of the macaroni penguin population at South Georgia.

We investigate the influence of krill (principally Euphausia superba) patchiness on the foraging distributions of seabirds to understand how variation in krill influences patch dynamics between krill and birds. At sea surveys were conducted near Elephant Island, Antarctica for three years (2004-2006) during the annual U.S. Antarctic Marine Living Resources (AMLR) program. Standardized strip-transect surveys were used to map seabirds, and a combination of acoustic and net surveys was used to map krill. We measured patch size of krill and seabirds and elucidated how krill patch dynamics influence foraging seabirds. The spatial association between krill and predators was influenced by the size and arrangement of krill patches. We found a negative relationship between abundance and patchiness of krill and predators, indicating that when krill is less abundant, krill and its predators are less abundant and concentrated. We conclude that annual patch dynamics of krill strongly influences the local abundance and distribution of seabirds. Such information should be used to interpret potential interactions between seabirds and krill fisheries operating near Elephant Island.

1. Implementing an ecosystem approach to fisheries management requires an effective ecosystem monitoring programme, the utility of which depends upon its ability (measured by the statistical power) to detect effects that trigger management action. 2. Using data from a long-term ecosystem monitoring programme of the predators of Antarctic krill Euphausia superba at South Georgia together with a krill population model to simulate natural and fisheries induced variability in krill abundance, the power to detect the effects of different levels of fishing was examined. 3. The power to detect the effects of fishing using either the krill population or a combined predator response index was low (20–40% power after 20 years with the probability of a type I error (α) = 0.05). The power increased to >50% when α was increased to 0.2 when the ability to detect change was greater with the predator response index than using the krill population itself. 4. The results indicate that although this monitoring programme has a proven ability to detect the effects of natural variability in krill abundance, its ability to detect the effects of fishing may be limited if there is a requirement for statistical significance at the 95% level. A situation where changing α produces a marked increase in statistical power, and the difference in the relative ecological costs of making type I and type II errors is likely to be high, may require a more flexible approach to choosing significance levels required to trigger management action. 5. Although long-term monitoring provides a wealth of basic ecological information it is essential to evaluate, the ability to detect specific changes in order that management action is not delayed because of an inability to detect an effect rather than the lack of an effect of the fishery.