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The impact that pinnipeds, such as fur seals, and seabirds have on their prey, and particularly the consequent interactions with fisheries, is a continuing source of debate and controversy in a wide range of large-scale marine ecosystems and for many different species and species groups (Swartzman & Haar 1983; Alverson 1992; Wickens et al. 1992; Livingston 1993; Pascual & Adkinson 1994; Agnew 1997; Best, Crawford & Van der Elst 1997). Many studies have calculated the total prey requirements of predators such as seabirds, seals and whales to provide a regional assessment of the food requirement for maintenance of predator populations (Laws 1977; Croxall, Ricketts & Prince 1984; Doidge & Croxall 1985; Perez & McAlister 1993; Boyd, Arnbom & Fedak 1994; Joiris, Tahon & Holsbeek 1996; Croll & Tershy 1998; Green, Slip & Moore 1998; Wanless, Harris & Greenstreet 1998) or to examine the strength of food web interactions (Punt & Butterworth 1995; Springer, Piatt & van Cliet 1996; Yodiz 1998). With the additional importance of these components of food webs for biogeochemical cycling of carbon (Huntley, Lopez & Karl 1991) and also their utility for constraining oceanic carbon budgets (van Franneker, Bathmann & Mathot 1997; Priddle et al. 1998), there is a need to develop robust methods to investigate the role of predators at the top of marine food chains.
This study developed an algorithm to calculate the food consumptions of Antarctic fur seals Arctocephalus gazella Peters and macaroni penguins Eudyptes chrysolophus Brandt at South Georgia (54°S, 38°W) in the Southern Ocean (Croxall et al. 1988). Both these species are mainly dependent upon Antarctic krill Euphausia superba Dana for food in this region. The algorithm was developed: (i) to provide information about prey consumption in these two species because of their potential interactions with fisheries (Trathan et al. 1998); (ii) to show how large amounts of life-history and bioenergetics information can be synthesized to reduce, or define, uncertainty in the estimates of food consumption by marine predators; and (iii) to examine the sensitivity of estimates of food consumption to uncertainty in these input variables. The algorithm used standard Monte Carlo methods (Manly 1991) to define the level of uncertainty in estimates of food consumption. In this example, parameters were evaluated for a single year, in this case 1991, because this was the last year in which a population survey of Antarctic fur seals was carried out and therefore had the most complete data set in terms of the most critical variables. The algorithm, which has already been applied in the context of food web studies in the Southern Ocean (Priddle et al. 1998; Everson et al. 1999), provided information about total prey consumption, consumption of each dietary item, energy and carbon flux through a predator population and the energy and carbon sequestered within the population.
At its simplest level, the calculation of the gross food requirements of a population is a matter of multiplying the daily ration of an individual, r, by the population size, N, and then scaling up to whatever time scale is appropriate. However, both r and N are difficult to estimate. Moreover, r will be composed of a set of items (the diet) that depends upon the type of individual involved (e.g. reproductive, non-reproductive, adult, juvenile, male, female), and the class composition of N will depend upon when measurements are made. Both r and N are more properly represented as vectors than scalars. Therefore, the basic model being developed in this paper can be represented as a combination of two vectors, n and r, representing the number of individuals in each class and the total ration, given in a common currency such as Joules or moles of carbon, required by each class in a time period t. The total population ration will be:
- (eqn 1)
where the total ration in each time period is being summed over k time periods and I is a scalar. The values within n and r may also vary with time (t) because of changes in the composition of the population and the activity patterns of individuals. Each vector associated with each time interval would need to be derived separately.
The ration at any time is more commonly represented as an a × b matrix, Rt, where a is the number of classes of individual and b is the number of items in the diet. This matrix defines a different diet for each class of individual. The cells of this matrix might contain the proportion of the diet in the jth class of individual represented by each dietary item. Most often, this is expressed as a proportional frequency of occurrence of a dietary item and therefore it is necessary to transform the matrix into a common currency such as energy. In this case:
where Etis an a × b matrix equivalent to Rt but expressed in units of energy, and ct is a column (b × 1) vector whose elements are the product of wet mass and mass-specific energy for each prey type. Therefore a more complete model to that given by equation 1 would be:
- (eqn 3)
This shows the energy intake of the total population, which is represented by the sum across all time periods of all energy intakes (that are themselves determined by the diet composition) multiplied by the population size at each time interval. The vector ct in equation 2 can be replaced by a vector representing the product of wet mass and mass-specific carbon content of each item to give the estimated carbon consumption.
The majority of previous estimates of prey consumption from the total gross energy requirement of a population of predators have been derived by scaling up from measurements of energy expenditure at the level of individuals (Croll & Tershy 1998). Potentially, there is a high degree of uncertainty in these types of estimates because errors (associated with both measurement and because of natural variability) will be additive across all input variables (e.g. metabolic rate, digestive efficiency, growth rate) and multiplicative across population size and time. Except in a few cases (Furness 1978; Shelton et al. 1997; Stenson, Hammill & Lawson 1997; Warren, Shelton & Stenson 1997) this type of uncertainty, together with other uncertainties about the demographic and behavioural features of the life histories of individuals, has not traditionally been incorporated into estimates of prey consumed.
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This study examined the food consumption by fur seals and macaroni penguins at South Georgia. The estimates of food consumption are most sensitive to uncertainty in demographic variables but consumption by these predators can normally be estimated with a CV of < 0·3, even when there is a relatively large uncertainty associated with many demographic variables. Morphological and physiological variables have a relatively minor role in this type of bioenergetics calculation for these species. Indeed, in the case of the species examined in the present study it may not even be necessary to know these in detail because they could be estimated from allometric equations. The additional uncertainty associated with this approach would be unlikely to add substantially to the overall uncertainty associated with the estimate of food consumption.
The importance of carrying out sensitivity analyses has been recognized in similar studies (Furness 1978; Shelton et al. 1997; Warren, Shelton & Stenson 1997; Croll & Tershy 1998) and these have also concluded that estimates of population energy flux and prey consumption are most sensitive to the variables representing population size. Although this result is not surprising, the present study adds to this by quantifying the relative sensitivity to different variables and providing a method for computing confidence intervals around estimates. However, the present study also does not distinguish between uncertainty associated with measurement errors and environmental stochasticity. Overall, the importance of these results is that, for each predator, it should be possible to parameterize the algorithm with all available data and, where empirical data do not exist for that species, to substitute data, particularly about physiological rather than demographic variables, from related species or by using a best guess with appropriately wide confidence intervals. Running the algorithm will then show how sensitive the final result is to uncertainty in different variables.
A number of tests can be conducted to examine the accuracy of the results. For example, dividing of the total population food consumption by the number of individuals in the population shows average daily food consumption by Antarctic fur seals (Fig. 4) and macaroni penguins (mean = 1·2 kg day−1) that is close to that calculated for similar predators of krill by Croll & Tershy (1998). Overall, the results from the algorithm are consistent with expectation in terms of the resultant gross efficiency of individuals (Humphries 1979), the total annual food consumption by individuals, especially in relation to age or body size, and the per capita metabolic heat production (Costa, Croxall & Duck 1989; Davis, Croxall & O’Connell 1989).
There are at least two important features of this approach to estimating food consumption by pinnipeds and seabirds that can be developed from this algorithm. These are: (i) to integrate a dynamic approach into the algorithm in order to examine changing patterns of prey consumption as a result of changes in population size and structure; and (ii) to place the calculated food consumption in a spatial context so long as the section of a population using a specific region is known. Dynamic (spatial and temporal) models of prey consumption, together with associated levels of uncertainty, are a logical extension to current approaches to examining the population dynamics of pinnipeds and seabirds and their potential impacts upon prey species (Butterworth et al. 1995; Punt & Butterworth 1995). Coupling the algorithm developed in the present study with physiologically based models of starvation duration (Øritsland & Markussen 1990) in the context of variable prey densities may also provide a means of predicting predator population responses to changes in food distribution and abundance. In addition, the algorithm presented here has the potential to partition the impacts of predators both between different prey species and between different age/length classes of those species (Fig. 6). This presents an opportunity to provide a direct link between estimating the impact of predators and age- or size-structured models of the prey population.
The seasonal variability in consumption (Fig. 5) suggests that, on average, the susceptibility of individuals to competition with fisheries may be greater during the winter than during the summer. Declines in consumption of food during the summer are associated with time spent fasting ashore during breeding and the reduced energy expenditure associated with a relatively low level of activity. Nevertheless, this analysis did not consider the varying spatial demand for food in relation to season. In winter, animals may be dispersed over a larger range (Boyd et al. 1998) so that the demand for food may be more dispersed. This could reduce potential competition with fisheries that are mainly concentrated at South Georgia (Trathan et al. 1998). In summer, demand for food will be greatest in the vicinity of the breeding colony so that competition with fisheries could increase in the summer and this could result in reduced population productivity (Mangel & Switzer 1998). However, at present the krill fishery at South Georgia occurs in the winter (Everson & Goss 1991), thus reducing the potential for competition with fur seals and penguins during the critical reproductive stages of the annual cycle.
Variations in total food consumption by the population of fur seals in relation to age suggest that reduction in food availability is likely to have its greatest effect on juveniles and the young, relatively productive, age classes (Fig. 4). However, this would appear to be mainly caused by the greater relative numbers of these individuals in the population because per capita food consumption in these younger age classes was less than in the older age classes (Fig. 4b). The lower per capita food requirements of females compared with males, and also between young and old individuals, suggest that these different groups will need to forage on different prey densities and may have different sensitivities to reduction in prey availability. Adult male fur seals are likely to be most vulnerable to reduction in mean prey density although, unlike females, they do not have to forage within a restricted radius of the breeding colony in the summer season when females are generally supporting pups (Boyd et al. 1998).
This study has shown that the annual prey requirements of Antarctic fur seals and macaroni penguins are in the order of 11·9 million tonnes, with an approximate range, based upon the 95% confidence intervals, of 6·4–17·4 million tonnes. The diet of macaroni penguins in winter is not known, although for Antarctic fur seals it appears that krill can form an important component of the diet (Reid 1995). The last published estimate of krill standing stock for the region of the Scotia Sea, which includes the region around South Georgia, came from a survey in 1980 which suggested there was a krill standing stock of 35·8 million tonnes (CV = 0·14; Trathan et al. 1995). There are, in addition to Antarctic fur seals and macaroni penguins, other avian (especially other penguins, and prions) and marine mammal (especially baleen whales) predators in the region that are mainly dependent upon krill. Even given the wide error range in the current estimate of krill consumption by fur seals and macaroni penguins at South Georgia, overall, this suggests that predators in the region could be responsible for a substantial part of the annual mortality of krill at South Georgia.
The same krill survey (Trathan et al. 1995) suggested there was a krill standing stock around South Georgia in the order of 1·5 million tonnes. The results of this study suggest that, even if predators are only present in the region around South Georgia during the summer, a high rate of influx of krill into the region is required, or that there is a particularly high rate of growth of krill at South Georgia, in order to sustain the fur seal and macaroni penguin populations in the region. Another independent analysis of the food requirements of breeding seabirds at South Georgia (Croxall, Ricketts & Prince 1984) suggested a similar discrepancy between food requirements and the standing stock of krill.
An important deficiency in current methods for estimating food consumption by marine predator populations is the difficulty of estimating the diet matrix (Rt, equation 2) and its associated uncertainty. In the current study, a highly simplified diet matrix was used with no added uncertainty. Diet analysis has several unquantified elements that contribute to uncertainty (e.g. errors in prey species/type composition, unknown levels of sampling bias, errors in the estimation of prey size from hard parts; Croxall 1993) which, if they had been included in the present analysis, could have added to the degree of estimation error on the food consumption. The small range of species involved in the diet of the predators being examined in the present study will have reduced the effect of errors in the estimation of prey species composition. While there are ways of tackling specific types of uncertainty in diets, such as uncertainty in the prey size distribution (Hammond & Rothery 1996), others types of uncertainty that relate particularly to the different degree of representation of prey species in scat or crop samples, and obtaining a representative range of samples from the various classes within the predator population (e.g. male, female, adult, juvenile), remain to be solved.
Even considering these caveats, this study has demonstrated, for two krill predators in the Antarctic, how an algorithm provides an opportunity to examine the overall consumption of prey by marine predators. Using different input data sets, it would be possible to develop this on a regional basis from current knowledge of the movements and distributions of predators (e.g. based on satellite tags; Boyd et al. 1998; McConnell et al. 1999). Such an approach could be used to quantify spatial, temporal and ecological overlap with fisheries.