 scenarios reflecting likely field sampling were also tested to compare how foraging behaviour differed depending on composition of the sample of birds tracked. Trip distance, trip duration, maximum distance travelled and size of area used were all significantly affected by the breeding site, and the number of chicks a tracked adult was raising. The effect of sex was also seen when examining trip distance, trip duration and colonies often means that samples of individuals used in tracking studies may fail to properly represent the traits of the population. a review of recent literature (Table 1) revealed that seabird tracking studies used a mean sample size of 29 individuals in each year of study (range 4-124, median = 23, n = 33). Sample size in these studies were found to represent a mean of only 1.4 % of the total colony size (range 0.001-25 %, n = 30 colonies), which is somewhat biased by the study of Stenhouse et al. (2012) who tracked 30 individuals from a colony of 65 pairs. If this study is excluded from the sample, we find that the sample size used at the remaining colonies represented only 0.7 % (range 0.001-6.6 %, n = 29 colonies) of the study colony. Birds were tracked for more than one field season in just four out of the 22 of the studies, eight of the studies reported the sex of tracked birds, and three covered a range of breeding stages (e.g. incubating and chick-rearing individuals). Whilst the reporting of these factors may not be applicable to all studies (e.g. for wintering area distributions). These factors are likely to influence the results of tracking studies undertaken during the breeding season when only small samples are used. These limited sampling regimes are likely to fail to adequately represent population-level characteristics. This is due in part, to the known variability in seabird foraging behaviour due to effects such as inter-individual differences caused by underlying physiology (Biro and Stamps 2010; Sommerfeld et al. 2013), sex-related differences (Weimerskirch et al. 2009;Pinet et al. 2012), age and experience, environmental factors (Chivers et al. 2012), location of breeding site (Hipfner et al. 2007), stage of breeding clutch size and size of colony (Wakefield et al. 2013). Soanes et al. (2013) highlight the need for researchers to explore and accept the limitations of their data sets before drawing conclusions on the location and extent of a whole colony's important foraging areas by considering the number of individuals and foraging trips included in a sample. Therefore, whilst it may not be possible to sample a large number of individuals from any particular colony, we should ensure that the individuals that are sampled are as representative as possible of the whole study population.
In this study, we examined the effect of a range of factors on the foraging behaviour of european shags Phalacrocorax aristotelis breeding at Puffin Island, Wales, great Britain a designated special protected area (SPa). The european shag is a good model species for testing these interactions given that they are a dimorphic species and can easily be sexed by their calls (Snow 1963). During breeding, they exhibit variability in the number of eggs laid per female (ranging from 1 to 4 eggs) and in chick-rearing success with not all chicks surviving to fledging. Furthermore, the distribution of breeding sites at this study colony can be classified into separate "sub-colonies" and 3 years worth of tracking data was collected from this species at this site allowing the effect of year on foraging behaviour to be investigated. The european shag is considered an amber listed species in europe (eaton et al. 2009) and at 494 breeding pairs the Puffin Island colony is the largest population in Wales (goddard 2010). This species has also been identified as having good potential for acting as a reliable ecological indicator on the state of the marine environment (Fortin et al. 2013). The effects of year, sex, age of chicks, timing of tracking, number of chicks being raised and the breeding site on foraging trip distance, duration, maximum distance travelled from the colony and the size of the area used were tested with the aim of determining which, if any, are the most important factors to consider when planning and undertaking a seabird tracking study. We then simulated different realistic sampling regimes to evaluate how sample selection can influence conclusions on apparent foraging characteristics.

Field methods
european Shags (from here on referred to as "shags"), breeding on Puffin Island, great Britain (53.3°n, 4.0°W) were tracked using IgotU gT-120 gPS data loggers (Mobile action, Taiwan) over three consecutive breeding seasons (2010)(2011)(2012). Birds were caught whilst brooding chicks at their nests using a crooked pole. a total of 28, 31 and 25 individuals were instrumented in 2010, 2011 and 2012, respectively, 11 individuals were tracked in more than 1 year. loggers were deployed between the 9th May and 18th June of each year (which represents the main chick-rearing period for this species) and samples represented males and females, individuals breeding at three different sites, with different numbers of chicks at the time of tracking, and with varying ages of chicks (from 1 to 35 days) ( Table 2). loggers were attached to the back feathers with waterproof Tesa ® extra power tape (Wilson et al. 1997) and weighed 15 g when packaged, which equates to <1 % of a shags body weight. The battery life of these loggers was 5-6 days when set to record a location every 2 min. regular visits to nests before, during and after tracking allowed us to estimate the age of the chicks and allowed us to monitor the productivity of the nests which were compared to control nests each year to assess any detrimental effects of tracking. The number of chicks that reached approximately 30-35 days old per nest was recorded (this is the age when they became mobile and were difficult to assign to individual nests) as an estimate of the productivity of each nest. a Kruskal-Wallis one-way analysis was conducted on this productivity data to assess site differences.

Tracking data
The gPS devices did not always record a position every 120 s as programmed to do so, in part due to the diving activity of shags. This may provide a biased sample of the spatial distribution of foraging activity (Mcleay et al. 2010), and so gPS fixes were interpolated to every 10 s using the software r (r Development Core Team 2011) with the package "trip" (Sumner 2012). This package was also used to calculate the area covered on each foraging trip by calculating the time spent in a pre-defined grid of 1 × 1 km cells surrounding the breeding colony. The number of cells used on each trip was used to represent the size of the area (km 2 ) covered on each foraging trip. Total trip distance (km), trip duration (min), the maximum distance travelled from the colony (km) and the direction travelled were also calculated for each trip.

Statistical methods
We tested the effects of a range of categorical and continuous explanatory variables including: (1) sex of the bird, (2) number of chicks, (3) age of chicks at the time of tracking, (4) location of nest on the island, (5) date that tracking was undertaken and (6) year of tracking on the five foraging trip response variables described above. Our aim was to determine which, if any, might account for the variation in foraging behaviour that was observed between individuals. Total trip distance, trip duration and the maximum trip distance were ln-transformed. generalised estimation equations (gees) (liang and Zeger 1986) were used in the analysis; this allowed for compound correlation structures to be specified for each individual, in order to account for withinindividual correlation. They also are more suitable than the more commonly used general linear Models for understanding population effects rather than individual-specific effects. The models were implemented in the "geepack" version 1.1-6 package (Højsgaard et al. 2012) in the r software environment (r Development Core Team 2011). all models incorporated the same terms consisting of sex, number of chicks, year of study and location of breeding site as fixed factors and the numeric factors of age of chicks at time of tracking and the number of days into the tracking season that tracking was undertaken (days from 1st april of each year). The model outputs were analysed using one-way anOVas, and significant terms at p < 0.05 level were then submitted to post hoc Tukey comparison tests to ascertain within-factor differences. In addition to the main model, the circular statistic software Oriana (Kovach Computing Services, UK) for windows and the Watson-Williams F test (Batschelet 1981) were also used to analyse any differences in the direction travelled in relation to the explanatory variables. In all analyses, a significance level of p < 0.05 was applied.
Maps of time spent in pre-defined grid cells of 1 × 1 km were plotted to compare use of space by shags around the colony for the explanatory variables that were found to be significant after the gee model was run (those with a p value <0.05). Home-range areas were represented as the actual time spent in a pre-defined grid of 1 × 1 km cells surrounding the breeding colony (Page et al. 2006). The 1 × 1 km cells that the animals spent 100 % of their time was used to represent their area of active use and the cells that the animals spent 50 % of their time (after ranking for frequency of use was) used to represent their core-foraging areas (Casper et al. 2010;Soanes et al. 2013).
a range of sampling scenarios to represent