Krill population dynamics in the Scotia Sea: variability in growth and mortality within a single population
Introduction
In the Antarctic a link has been described between a major physical variable, the extent and duration of sea-ice in winter, and the population dynamics of Antarctic krill Euphausia superba, a key species in the marine foodweb of the region (Loeb et al., 1997). Variability in the abundance and population structure of Antarctic krill has also been linked with fluctuations in the reproductive performance of krill-dependent predators such as penguins and seals both within the sea-ice zone and in adjacent areas not directly affected by sea-ice Croxall et al., 1988, Croxall et al., 1999, Fraser et al., 1992. To understand the factors controlling the biological dynamics, it is therefore essential to define the scales over which physical and biological variability occurs. Measurement of the extent and duration of sea-ice, particularly using satellite telemetry, is relatively straightforward. Characterising the spatial extent of the biological variability is, however, more difficult and in the case of krill this requires a consideration of the population demography at the ocean basin scale.
Within the southwest Atlantic (Scotia Sea) Sector of the Southern Ocean the major areas of krill spawning are probably in the region near the western Antarctic Peninsula and along the southern Scotia Arc to the South Orkney Islands which are well within the area of winter sea-ice cover (Hewitt and Linen Low, 2000). These areas are believed to be the principal source of the population at South Georgia, where the krill population is thought not to be locally self-sustaining, and which lies to the north of the maximum extent of winter sea-ice (see Murphy et al., 1998). Although some level of concordance is evident in the inter-annual fluctuations in krill biomass across the Scotia Sea, based on data from the South Shetland Islands and South Georgia (Brierley et al., 1999), attempts to show similarities in the presence/absence of individual year classes of krill have proved problematic with strong year classes in one location apparently absent in the other and vice versa (see Murphy et al., 1998). Comparisons of year class strength have been made on the basis of the relative size (length) and strength (abundance), however, these analyses do not take into account the potential for regional differences in the processes governing the structure of the population in the two locations.
The size structure of a population is a function of recruitment, growth and mortality and if these conditions vary geographically this may be reflected in differences in the structure of sub-populations within a wider meta-population. Therefore, it might be unrealistic to track cohorts across large areas only on the basis of size, especially where samples are available from a very limited number of locations. It may be the case that the krill population of the Scotia Sea experiences common recruitment processes, possibly associated with large-scale physical sea-ice processes, but the subsequent regional differences in growth and mortality rates lead to appreciable differences in the size structure of the resulting adult populations. In particular, the position of a mode in the length–frequency distribution that represents a particular year class may be determined by the growth rate, while the dominance of individual modes will depend upon the mortality rate and relative year-class strength. Given that there is evidence that both the growth and mortality rates of krill at South Georgia are higher than the overall population estimate (Murphy and Reid, 2001, Reid, 2001), accounting for differences in growth and mortality may help resolve the extent to which there are common recruitment processes acting across the population of the Scotia Sea.
The aim of this paper is to take the krill length–frequency distributions from the South Shetland Islands and model the effects of regional differences in rates of growth and mortality to generate expected length–frequency distributions, and compare these with the observed length–frequency distributions from South Georgia. Long-term monitoring data on the population size structure of krill at the South Shetland Islands and South Georgia each year from 1991 to 2000 was used to examine: (a) the level of overlap in the position of the modal size classes when the effects of the higher growth rate at South Georgia were applied to the length–frequency distribution of krill from the South Shetland Islands, (b) the effects of the differences in mortality rates on the relative magnitude of modal size classes, and (c) the implications of these results both in terms of the presence of common recruitment events and inter-annual variation in the size structure of the adult population.
Section snippets
Data sources
In order to consider the connection between krill populations at the South Shetland Islands and South Georgia we have used the two longest annual time-series of krill population size structure from the Scotia Sea from 1991 to 2000. Data for the South Shetland Islands comes from the US Antarctic Marine Living Resources monitoring programme (see Hewitt and Demer, 1994) and that from South Georgia come from the measurement of krill in the diet of lactating female Antarctic fur seals (Arctocephalus
Results
Comparison of the raw length frequency distributions from the two regions shows there was little overlap in their relative size and position of the modes (Fig. 2). After the application of the growth rate correction function to the krill length frequency distribution from the South Shetland Islands there was a greater overlap of the modal size groups with those in the distributions from South Georgia (Table 1; Fig. 3). However, there were considerable differences in the relative magnitude of
Accounting for regional variation in population parameters
In this analysis, we attempted to investigate to what extent the differences in growth and mortality of krill at the South Shetland Islands and South Georgia could explain the differences in the population of krill in those two areas, despite them having a common source region. It is important, however, to avoid a circularity in which the length–frequency distributions from the two locations are made to converge by changing key parameters and then using the overlap in the length–frequency
Acknowledgements
We thank all of the (many) personnel who have measured krill at South Georgia and the South Shetland Islands. This paper is part of the DYNAMOE core-funded science programme of the British Antarctic Survey.
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