Data collection Survey design

This study was conducted in the British Antarctic Survey long-term study area, termed the Western Core Box (WCB) (Figure 4.1) to the northwest of South Georgia (Atkinson et al. 2001; Trathan et al. 2003) during January-February 2002. This is in an area where lactating female fur seals from Bird Island are known to forage (Boyd 1999; Staniland et al. 2004).

Figure 4.1. Map showing the layout of transects and the study area. Also shown are the 200, 500 and 1000 m depth isobaths.

The distribution of fur seals along transects

The survey was designed to provide an quantitative estimate of krill density and transects were aligned perpendicular to the continental shelf break, the dominant topographical feature of the region (Trathan et al. 2003). On each day of the survey two 80 km transects were surveyed with the ship maintaining a speed of ~10 kts. All transects were surveyed twice with a gap of five days between replicates. Observations were made over an area of sea 300 m forward and to one side of the ship. Sightings were recorded in one of four distance bands (0-50 m, 51-100 m, 101-200 m and 201-300 m) perpendicular to the transect line. Observations were made continuously and were summed into 5-minute periods, following the methodologies of Tasker et al, (1993) as adapted by Van Franeker (1994), and analogous to methods used in previous studies in the region (Hunt et al. 1986; BIOMASS 1992;

Hunt et al. 1992; Van Franeker 1994; Whitehouse and Veit 1994; Veit 1995; Trathan et al. 1998b).

Adjustments were made for factors that may reduce the probability of detecting an animal including, distance from the observer, sea state, and dive behaviour. This provided a density estimate of seals km-2 with a minimum spatial resolution of ~1.6 km along the transect. The survey methods used to provide density estimates are described in more detail in Chapter Two.

Measuring the distribution of fur seals at sea using telemetry

A total of 59 female Antarctic fur seals were tracked from Bird Island between 03/01/02 and 04/03/02. Each animal was tagged over a single foraging trip lasting between 3 and 16 days.

Platform Transmitter Terminals (PTT, ST10, cast A-400, 13 x 4 x 2 cm. 200 g, Telonics Inc., 932 E. Impala Avenue, Mesa, AZ 85204, U.S.A.) were attached with epoxy glue to the fur along the dorsal midline between the scapulae (Boyd et al. 1998). PTT signals were received by the ARGOS satellite system (CLS Argos, 18 Ave Edoudard Belin, 31005 Toulouse, France). Upon the seal’s return to the study colony, the telemetry equipment was removed.

Accuracy of geographical location from PTTs

The ARGOS system allocates a Location Quality Index (LQI) to each location fix according to the level of confidence associated with the location provided. This confidence is based on the number of uplinks received by different satellites associated with each fix. At least four successive uplinks during a satellite pass are required for Location classes 0 (± >1000 m), 1 (±

350 – 1000 m), 2 (± 150 – 350 m) or 3 (±≤150 m), however, some fixes with less uplinks may still be useful (classes A, B or Z) (McConnell et al. 1992; Boyd et al. 1998; Vincent et al. 2002; Phillips et al. 2004). Boyd et al. (1998) quantified the degree of error associated with each of these classes by examining the locations received from two PTTs placed at a known location. The average location, provided by ARGOS for the transmitter placed at the known location, was within 1-2 km for the location classes 1, 2 and 3, with the maximum distance from the site being 6 km. On average, location class 0 gave a position of 3.8 km from the correct location; with a maximum distance of 8.6 km. Location classes A and B had maximum errors of 130 km.

Locations with LQI values 0, A, B and Z may still carry valid location data. For this reason the data was filtered by testing for the velocity required to travel between uplinks using the iterative forward-backward averaging filter described by McConnell et al. (1992). A velocity (Vi) was associated

with theithlocation such that:

 

Wherevi,jis the velocity between successive locationsiandj.

Locations with Vi greater than the estimated maximum velocity of a

seal (2.5 m s-1) were rejected, based on swimming speeds described by Boyd et al. (1998) and as used by Boyd et al. (2002). The filtering process resulted in the rejection of 17.8% of all uplinks, with rejection in different LQ classes as: Class B = 75%, Class A = 24%, Class 0 = 18%, Class 1 = 8%, Class 2 =

4%, Class 3 = 5%. Leaving 4994 uplinks over 59 individual foraging trips, this is summarized in Table 4.1.

Table 4.1. The number of uplinks within each Location Quality Class, and the number filtered out from each class.

Location Quality nstart nrejected

3 81 4 2 281 12 1 1263 103 0 1440 263 A 1217 299 B 1678 1275 Data smoothing

One approach to the initial visual comparison of two distributions is to generate a continuous smoothed surface. Each observation point carries information about neighbouring locations and smoothing aims to redistribute the information contained in a way that takes account of positive autocorrelation and reduces random variability (Seaman and Powell 1996; Matthiopoulos 2003a).

Kernel smoothing works by placing a probability density function (the kernel) centred over the target point and covering a neighbourhood around that point. The shape of the kernel may take a number of forms such as a square, triangle, or the most commonly applied, and also the one used in this study, gaussian. The kernel surface was only generated for that part of the 95% home range polygon of the telemetry data, i.e. the area where the animals spent 95% of their time, that fell within the area of the study box.

The choice of the smoothing parameter (h – the spread of the kernel around the data point) determines the level of smoothing. In choosing h it is

important to reconcile the potential effects of choosing a value that is too high resulting in the loss of information about the data distribution and anhthat is too small and the objective of the procedure is not accomplished. Hence the aim is to choose a value ofhthat provides an intermediate level of smoothing (Gitzen and Millspaugh 2003; Matthiopoulos 2003a). The appropriate value of h was determined by least-squares-cross validation (LSCV) (Silverman 1986; Gitzen and Millspaugh 2003). LSCV is a common method that allowsh

to be chosen so as to minimise the difference between the predicted surface using all the data and one fitted excluding the data point (Hemson et al. 2005). LSCV was estimated using available routines in the software package R (Wood 2001). A kernel withh=0.02 was chosen.

Scales of correlation

In a similar way to choosing the correct smoothing factor for kernel analysis, choosing the correct scale for comparing distributions is essential. Previous studies have shown relationships between predators and prey differ with the spatial scale at which they are measured. In particular these relationships may show no correlations at smaller scales and significant positive relationships at larger scales. Nevertheless, at the finest scale a relationship between predator and prey must exist as a predator has to eat (though this may depend on fine scale temporal as well as spatial resolution). The scale-dependency of these processes and the effect on measuring correlation means that positive relationships may only become apparent at larger scales, yet at these larger scales we also know that relationships must exist, a plot showing that both krill and fur seals are found within 100 km of

South Georgia is largely uninformative (Schneider and Duffy 1985; Schneider and Piatt 1986; Hunt and Schneider 1987; Rose and Leggett 1990; Hunt et al. 1992; Ritchie 1998; Fauchald 1999; Mehlum 1999; Reid 2001b; Davoren et al. 2002). With the serendipitous relationships that may potentially by expected in this kind of study, only making the comparison at one scale could lead to false interpretations of an unstable relationship that may be better revealed by investigation over a range of scales. Figure 4.2 illustrates how one may expect the strength of correlation to increase with the scale of comparison and highlights the range of highest ecological interest.

Figure 4.2. Idealized illustration of how we may expect the strength of correlation to increase with the increasing scale of comparison. Insufficient relationships may be found at smaller scales and beyond the asymptote the correlation is less meaningfull and may be at a scale that no longer allows insight to the underlying processes.

Krill density along transects

Within each transect krill density was estimated using a Simrad EK500 echosounder operating hull-mounted 38, 120 and 200 kHz transducers. Synchronous pulses from all transducers were transmitted every

were converted to average krill densities. For further details of target identification, target strength determination and calibration procedures see Brierley & Watkins (1995).

The minimum krill resolution available along transects at 2.5 second intervals was much smaller than that available for fur seals, and so krill density was averaged into bins along the transect at a scale to match those of fur seal densities. Correlations were assessed based on data from the whole acoustic interrogation depth (250 m) and to a depth representing the depth at which krill remains available to Antarctic fur seals (50 m).

Bathymetry

Depth was calculated from a raster layer in ArcInfo 8.0 GIS software (Environmental Systems Research Institute Inc., 1992-1999) with a spatial resolution of 0.001’ x 0.001’ (~1.716 km2). This layer was created by amalgamating all available depth records for the South Georgia region to create the highest resolution bathymetric map for the area. The bathymetric slope for each cell was calculated using the Spatial Analyst function of ArcInfo as the steepest slope between each cell and the surrounding eight cells.

Percentage slope 100

run rise

 Equation 4.2

Whereriseis the difference in depth (m) between two cells andrun is the distance between their centre points (m).

For subsequent analysis both the slope and depth layers were converted from raster to grid layers (a raster describes an area defined by its

(often irregular) contours, and a grid has a series of regular cells). Slope and depth values were then derived from these grid layers for any point of interest by using the Grid Analyst extension to ArcView 3.2 (Environmental Systems Research Institute Inc., 1992-1999).

Figure 4.3. Grid of bathymetric slope within the study region. Also shown are the 200, 500 and 1000 m depth isobaths.

Distance to land

Distance to land was calculated as the great circle distance to the study colony based on Bird Island for the satellite data and to the nearest point of South Georgia coast line for the transect derived data set. This was calculated using the Haversine formula (Gellert et al. 1989), in the form:

Distance = 120.deg. _                              2 2 1 2 sin . 2 . 2 cos 2 sin sin 2 1 2 x x x x a x x Equation 4.3

Where x1 and x2 are the locations in decimal degrees of the points between which distance is measured. This was calculated firstly for latitude (x) and secondly for longitude (y), with the total distance (z) estimated as:

2 2 y x z  Equation 4.4 4.2.2 Data Analysis

General patterns in distribution

Differences in seal density were examined: (1) between the northern and southern sectors of the study box (defined by a line running between 53°.72 S, 39°.59 W and 53°.46 S, –37°.63 W), (2) between the eastern and western sectors of the study box, defined as east and west of 38°.6 W longitude (the mid point of the study box) and (3) between regions defined by depth as follows: on-shelf (shallower than 500 m), shelf break (500 – 2000 m deep) and off-shelf region (deeper than 2000 m). Density data were non- normally distributed and so the differences in density between regions were investigated by comparing the medians using a Mann Whitney U test.

Figure 4.4. Map showing Western Core Box study area and spatial divisions based on depth (with cut points at 500 m and 2000 m), eastern and western and northern and southern sectors of the area.

Differences between the satellite and line transect derived distributions

The differences between the distributions of Antarctic fur seals derived by telemetry and transect methods were investigated by three different methods; (1) by qualitatively comparing two smoothed overall usage surfaces, (2) by using ‘virtual transects’ where the distribution of telemetry tracked seals falling along transect lines were compared with the distribution of seals along the transect line from the transect data. These were accompanied at different spatial scales and (3) by modelling the distribution of seals using General Additive Models (GAM) using the telemetry and the transect data, and comparing the distributions of seals for the whole WCB predicted by the models.