Abstract:Individual wintering strategies and patterns of winter site fidelity in successive years are highly variable among seabird species. Yet, an understanding of consistency in timing of movements and the degree of site fidelity is essential for assessing how seabird populations might be influenced by, and respond to, changing conditions on wintering grounds. To explore annual variation in migratory movements and wintering areas, we applied bird-borne geolocators on Thick-billed Murres (Uria lomvia, n = 19) and Com… Show more
“…This could ultimately lead to variation in individual migration patterns of seabirds at some spatial scale across years. Indeed, although most species show high individual consistency in non-breeding destinations at a large spatial scale (Phillips et al 2005, Müller et al 2014, there are exceptions; in addition, in almost all species there is extensive variation both among and within individuals in routes, use of staging areas and timing (Quillfeldt et al 2010, Dias et al 2011, McFarlane Tranquilla et al 2014.…”
Seabirds show remarkable variability in migration strategies among individuals and populations. In this study, we analysed 47 migrations of 28 brown skuas Catharacta antarctica lonnbergi breeding on King George Island in the Maritime Antarctic. Brown skuas from this population used a large area during the non-breeding period north of 55°S, including parts of the Patagonian Shelf, Argentine Basin and South Brazil Shelf, areas which are characterised by high levels of marine productivity. However, individual birds utilised only a subset of these areas, adopting 1 of 4 distinct migration strategies to which they were highly faithful between years, and showed high repeatability in departure and arrival dates at the breeding ground. Although they spent the majority of the non-breeding season within a particular region, almost all individuals used the same area in the late winter, exploiting its seasonal peak in productivity. Overall, these results indicate consistent individual variation in migration strategies that may reflect a combination of genetic control and individual experience, but with considerable flexibility to shift distribution in response to prevailing environmental conditions. KEY WORDS: Catharacta antarctica lonnbergi · Seabird ecology · Light-level geolocation · Non-breeding distribution · Individual consistency · Ocean primary productivity · Migratory connectivity Contribution to the Theme Section 'Individual varability in seabird foraging and migration'
“…This could ultimately lead to variation in individual migration patterns of seabirds at some spatial scale across years. Indeed, although most species show high individual consistency in non-breeding destinations at a large spatial scale (Phillips et al 2005, Müller et al 2014, there are exceptions; in addition, in almost all species there is extensive variation both among and within individuals in routes, use of staging areas and timing (Quillfeldt et al 2010, Dias et al 2011, McFarlane Tranquilla et al 2014.…”
Seabirds show remarkable variability in migration strategies among individuals and populations. In this study, we analysed 47 migrations of 28 brown skuas Catharacta antarctica lonnbergi breeding on King George Island in the Maritime Antarctic. Brown skuas from this population used a large area during the non-breeding period north of 55°S, including parts of the Patagonian Shelf, Argentine Basin and South Brazil Shelf, areas which are characterised by high levels of marine productivity. However, individual birds utilised only a subset of these areas, adopting 1 of 4 distinct migration strategies to which they were highly faithful between years, and showed high repeatability in departure and arrival dates at the breeding ground. Although they spent the majority of the non-breeding season within a particular region, almost all individuals used the same area in the late winter, exploiting its seasonal peak in productivity. Overall, these results indicate consistent individual variation in migration strategies that may reflect a combination of genetic control and individual experience, but with considerable flexibility to shift distribution in response to prevailing environmental conditions. KEY WORDS: Catharacta antarctica lonnbergi · Seabird ecology · Light-level geolocation · Non-breeding distribution · Individual consistency · Ocean primary productivity · Migratory connectivity Contribution to the Theme Section 'Individual varability in seabird foraging and migration'
“…Hamer et al 2001, Gray et al 2005, Vander Zanden et al 2013, Oppel et al 2015 or with linear mixed-effects models with individual identities treated as a random effect (e.g. Dias et al 2011, Matich et al 2011, Grist et al 2014, McFarlane Tranquilla et al 2014, Kernaléguen et al 2015, Wake field et al 2015. Note that the framework proposed by Bolnick et al (2002), examining the within-individual component and between-individual component of a niche, is essentially the same as the residual variance (within-individual variance) and between-individual variance when using mixed-effects models.…”
Section: Diet (Trophic Position/carbon Source) Using Stable Isotopesmentioning
confidence: 99%
“…It is possible to study individual specialisation in space, and not only in trip characteristics, based on distances between the centroid of the locations at 2 different times for the same individuals tracked during the breeding season (Navarro & González-Solís 2009, Ceia et al 2014, or between centroids in different winters (Dias et al 2011, McFarlane Tranquilla et al 2014, Yamamoto et al 2014, Lea et al 2015. Distances can also be calculated between pairs of migratory routes (e.g.…”
Section: Spatial Analyses: Distance Between Centroids Of Distributionmentioning
confidence: 99%
“…evaluate the statistical significance of individual consistency), one approach is to use distances calculated for pairs of centroids or routes as the response variable in a (G)LMM with individual (same vs. different) as a random effect, and check for the significance of the random effect (Dias et al 2013). The second, and more widely used approach, is to compare the calculated within-individual distances with a null distribution of distances generated by reshuffling either locations or migratory tracks between individuals (Navarro & González-Solís 2009, Dias et al 2011, McFarlane Tranquilla et al 2014). This method based on distances does not take into account the spread of the locations around the centroids: hence, although useful to detect a shift in the general distribution, it would not detect a change only in range size.…”
Section: Spatial Analyses: Distance Between Centroids Of Distributionmentioning
confidence: 99%
“…This method has been used to compare foraging areas in consecutive trips during the breeding season , Pettex et al 2012, Soanes et al 2013, and areas used from one year to the next (Chilvers 2008, McFarlane Tranquilla et al 2014, Muller et al 2014). The problem is that it does not exploit the information on the complete UDs (cf.…”
Section: Spatial Analyses: Overlap Between Distributionsmentioning
There is increasing realisation that individuals in many animal populations differ substantially in resource, space or habitat use. Differences that cannot be attributed to any a priori way of classifying individuals (i.e. age, sex and other group effects) are often termed 'individual specialisation'. The aim of this paper is to assess the most common approaches for detecting and quantifying individual specialisation and consistencies in foraging behaviour, movement patterns and diet of marine predators using 3 types of data: conventional diet data, stable isotope ratios and tracking data. Methods using conventional diet data rely on a comparison between the proportions of each dietary source in the total diet and in the diet of individuals, or analyses of the statistical distribution of a prey metric (e.g. size); the latter often involves comparing ratios of individual and population variance. Approaches frequently used to analyse stable isotope or tracking data reduced to 1 dimension (trip characteristics, e.g. maximum trip distance or latitude/longitude at certain landmarks) include correlation tests and repeatability analysis. Finally, various spatial analyses are applied to other types of tracking data (e.g. distances between centroids of distributions or migratory routes, or overlap between distributions), and methods exist to compare habitat use. We discuss the advantages and disadvantages of these approaches, issues arising from other effects unrelated to individual specialisation per se (in particular those related to temporal scale) and potential solutions.
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