CCAMLR

Acoustic estimates of densities of Antarctic krill, Euphausia superba, at South Georgia are presented for eleven austral summers between 1981 and 1998. Krill abundance at the island fluctuated widely from year to year over this time, ranging from ?2 to ?150 gm-2 (wet weight). The 1982, 199 1 and 1994 austral summer seasons were characterised by particularly low abundances of krill. For five of the summers between 1990 and 1998 it was possible to calculate separate density estimates for north-eastern and north-western ends of South Georgia. In four of these years density was higher to the east.

The summer 1998 AMLR field season followed late-forming but spatially extensive winter sea ice in the Antarctic Peninsula region. Overall krill length/maturity composition reflected poor recruitment from the past two years. Unusually small juvenile krill in 1998 suggested that survival success was limited to late-spawned eggs and larvae in 1997. Delayed spawning and few larval krill in 1998 were associated with extremely high salp abundance. Sa/pa thompsoni abundance was similar to levels during the 1993 "salp year". The magnitude of this salp bloom may have resulted from a large overwintering "seed" population developed the preceding autumn. Presence of large numbers of another salp species, Ihlea racovitzai, was unique to AMLR surveys and indicated faunal input from the east.
Comparisons of January and February 1998 survey data with data from previous AMLR field seasons showed recurring patterns of species abundance relationships that allowed definition of different ecological regimes identified here as "cope pod years", "salp years" and "transition periods". The 4-5 year periodicity of "salp years" over the past fifteen years is discussed with respect to the Antarctic Circumpolar Wave (ACW).

We present a model at the level of the foraging trip for the effects of a fishery on krill (Euphausia superba) predators, using the Adelie penguin (Pygoscelis adeliae) as a model organism. The model involves: (i) the description of the biomass and spatial distribution of krill; (ii) the effects of the fishery on the krill; (iii) the description of penguin breeding; and (iv) the indirect effects of the fishery on penguin reproduction and survival. The objective is to make relative comparisons of penguin reproductive success and adult survival in the absence or presence of a fishery. The biomass of krill appropriate for the predators (and the fishery) fluctuates from one year to the next according to an age-structured, stochastic recruitment model. Furthermore, there is a spatial-temporal structure, determined by diffusion and advection, to krill availability in relation to the location of the penguin breeding colony. Fishing is assumed to change the spatial and temporal distribution of available krill. After fledging, offspring survival depends in part upon the amount of krill delivered to them during the feeding periods. We use empirical data to estimate parental and offspring needs and a standard life history model to set the upper limits for survival. Parental survival after breeding depends upon the krill deficiency (relative to needs) that parents accumulate while feeding their young. A sensitivity analysis of the breeding model shows that the predictions are robust for parameters about which little is known, to the functional forms relating krill abundance to offspring and parent survival, and to the rules that parents use to allocate krill to their offspring. We evaluate expected reproductive success (offspring survival) and expected parental survival as functions of the amount of krill captured by the fishing fleet. Over the range of catch in our study, the reductions in reproductive success are essentially linear functions of krill catch with slope 1.5 and reductions in adult survival are also linear functions of krill catch, with but slopes less than I. That is, reductions in reproductive success and parental survival are linear functions of krill catch, but not 1:1. The reductions in offspring and parent survival are mainly determined by how long the fishing season lasts and the capacity for harvest, rather than when fishing begins.

In 1997, de la Mare presented a statistic (labelled the Composite Standardised Index at the Workshop on Area 48) to WG-EMM for generating a simple time-series index of many long-term datasets on Antarctic predators. The value of the index in a given year is dependent on the specific parameters present in that year. In this respect, the robustness of the index is dependent on that subset of parameters being representative of all the parameters for that year. Clearly, this will depend on how correlated the parameters are to each other. This paper reports on a Monte Carlo simulation study that helps specify general criteria for the inclusion of predator parameters in the index with regard to the potential effects of missing values as well as the degree of correlation between parameters. These trials found that, for inclusion in the index, parameters should be positively correlated with all other parameters and preferably with correlations greater than 0.3. If this criterion is met then these results indicate that a time series of 3 years for the full matrix of 8 parameters used in this study is sufficient for determining the correlation coefficients. These criteria need to be accepted with caution. Further work is required to take assess the effects of sampling variability on the performance of the index. Also, this work needs to account for the characteristics of the current CEMP database and to assess which of the available parameters might be included in the index. A general challenge is to identify how to incorporate parameters that are highly negatively correlated to the other parameters in analyses of the effects of prey availability and the environment on predators. The latitude given to accepting some parameters in the index needs to be evaluated in the wider context of being able to make correct decisions based on the index despite the uncertainties or variability in the behaviour of those parameters.