Poisbleau et al. 2015 E&E BDD

Original data collected in the field on females and eggs

Dehnhard_2015_PLoS_ONE_data

Body mass, body condition and egg mass data of individually marked male and female southern rockhopper penguins breeding on New Island, Falkland / Malvinas Islands. Analyses in Dehnhard et al. 2015 PLoS ONE were based on this dataset

Kernel density analyses of GPS data of Dolphin Gulls <i>Leucophaeus scoresbii.</i>

<p>The 50% kernel density distribution of tagged dolphin gulls that repeatedly attended seabird and seal colonies (denoted as ‘colony feeders’; sites 1–3 and 5–7) are marked red. The 50% kernel density distribution of birds that repeatedly attended mussel beds (denoted as ‘mussel feeders’; site 4) are marked light blue. GPS locations of colony feeders are marked with triangles, while those of mussel feeders are marked with circles. Mussels, seals and seabirds present per site: site 1) Imperial Shags <i>Leucocarbo atriceps</i>, Rockhopper Penguins <i>Eudyptes chrysocome</i>, Black-browed Albatrosses <i>Thalassarche melanophris</i>; site 2) Imperial and Rock shags, Fur Seals <i>Arctocephalus australis</i>; site 3) Rock Shags; site 4) Blue Mussel <i>Mytilus edulis chilensis</i>; site 5) Imperial and Rock shags; site 6) Imperial and Rock shags, Southern Giant-petrels <i>Macronectes giganteus</i>; site 7) Imperial and Rock shags. The Dolphin Gull colony at New I. is indicated with a white square. A second Dolphin Gull colony in the region is marked with a black square.</p

Distribution Patterns Predict Individual Specialization in the Diet of Dolphin Gulls

<div><p>Many animals show some degree of individual specialization in foraging strategies and diet. This has profound ecological and evolutionary implications. For example, populations containing diverse individual foraging strategies will respond in different ways to changes in the environment, thus affecting the capacity of the populations to adapt to environmental changes and to diversify. However, patterns of individual specialization have been examined in few species. Likewise it is usually unknown whether specialization is maintained over time, because examining the temporal scale at which specialization occurs can prove difficult in the field. In the present study, we analyzed individual specialization in foraging in Dolphin Gulls <i>Leucophaeus scoresbii</i>, a scavenger endemic to the southernmost coasts of South America. We used GPS position logging and stable isotope analyses (SIA) to investigate individual specialization in feeding strategies and their persistence over time. The analysis of GPS data indicated two major foraging strategies in Dolphin Gulls from New I. (Falkland Is./Islas Malvinas). Tagged individuals repeatedly attended either a site with mussel beds or seabird and seal colonies during 5 to 7 days of tracking. Females foraging at mussel beds were heavier than those foraging at seabird colonies. Nitrogen isotope ratios (δ¹⁵N) of Dolphin Gull blood cells clustered in two groups, showing that individuals were consistent in their preferred foraging strategies over a period of at least several weeks. The results of the SIA as well as the foraging patterns recorded revealed a high degree of specialization for particular feeding sites and diets by individual Dolphin Gulls. Individual differences in foraging behavior were not related to sex. Specialization in Dolphin Gulls may be favored by the advantages of learning and memorizing optimal feeding locations and behaviors. Specialized individuals may reduce search and handling time and thus, optimize their energy gain and/or minimize time spent foraging.</p></div

Additional file 1: of How animals distribute themselves in space: variable energy landscapes

Table S1. GAM investigating the sum of ODBAas a function of maximum dive depth. Table S2. Relationship between the sum of ODBA and maximum dive depth. Table S3. GAM investigating the bottom time as a function of event maximum depth. Table S4. Relationship between bottom time and event maximum depth. Figure S1. Depth zones. Figure S2. Distribution of depth during benthic (A) and pelagic (B) dives. Figure S3. Distribution in different depths of benthic (A) and pelagic (B) dives. Figure S4. Benthic dives example. Figure S5.Sum of ODBA versus maximum dive depth for benthic dives (South End, 2013). Figure S6. Sum of ODBA versus maximum dive depth for pelagic dives (South End, 2013). Figure S7. Sum of ODBA versus maximum dive depth for benthic dives (South End, 2014). Figure S8. Sum of ODBA versus maximum dive depth for pelagic dives (South End, 2014). Figure S9. Sum of ODBA versus maximum dive depth for benthic dives (North End, 2014). Figure S10. Sum of ODBA versus maximum dive depth for pelagic dives (North End, 2014). Figure S11. Bottom time versus event maximum depth for benthic dives (South End, 2013). Figure S12. Bottom time versus event maximum depth for pelagic dives (South End, 2013). Figure S13. Bottom time versus event maximum depth for benthic dives (South End, 2014). Figure S14. Bottom time versus event maximum depth for pelagic dives (South End, 2014). Figure S15. Bottom time versus event maximum depth for benthic dives (North End, 2014). Figure S16. Bottom time versus event maximum depth for pelagic dives (North End, 2014). Figure S17. Chlorophyll a concentration. (DOCX 2492 kb

Relationship between daily wind speed and daily wind direction on the daily foraging mass gain of southern rockhopper penguins.

<p>The graphical output of the GAM (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079487#pone-0079487-t002" target="_blank">Table 2</a> for details) shows foraging mass gain as a colour scale ranging from high foraging success in white to low foraging success in red, depending on wind speed (y-axis) and wind direction (x-axis).</p

Results for the GAM with daily foraging mass gain as dependent variable.

<p>Breeding season (2009/10 or 2010/11) and chick rearing stage (guard or crèche) were included as fixed factors, wind speed (in m/s) and wind direction (in degree, circularity was accounted for by a circular smoother) were included as continuous variables. In addition, we included the interaction between wind speed and wind direction (again accounting for the circularity with a smoother). n = 111 days, the model explained 15.8% of the deviance. Significant results are marked in bold.</p

Diet compositions of Dolphin Gulls <i>Leucophaeus scoresbii</i> based on stable isotope values.

<p>Food type contributions according to a Bayesian model in SIAR 4.0 (Stable Isotope Analysis in R), based on red blood cell stable isotope values of tagged birds (a). Density plots show the contributions of main prey types to diet in colony feeders (b), and in mussels feeders (c).</p