How animals distribute themselves in space: variable energy landscapes

• Published in: Frontiers in Zoology
• Main Authors: Masello, Juan F., Kato, Akiko, Sommerfeld, Julia, Mattern, Thomas, Quillfeldt, Petra
• Other Authors: Department of Animal Ecology & Systematics Germany, Justus-Liebig-Universität Gießen = Justus Liebig University (JLU), Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2017
• Subjects: Movement ecology; Ecological mechanism; Landscape of fear; Tri-axial acceleration; Non-lethal effects of predation; Energetic costs; Energy landscape; Foraging effort; Foraging strategy; Variable costs of foraging; [SDE]Environmental Sciences; Pygoscelis papua
• Online Access: https://hal.archives-ouvertes.fr/hal-01571295; https://doi.org/10.1186/s12983-017-0219-8

International audience Background: Foraging efficiency determines whether animals will be able to raise healthy broods, maintain theirown condition, avoid predators and ultimately increase their fitness. Using accelerometers and GPS loggers,features of the habitat and the way animals deal with variable conditions can be translated into energetic costsof movement, which, in turn, can be translated to energy landscapes.We investigated energy landscapes inGentoo Penguins Pygoscelis papua from two colonies at New Island, Falkland/Malvinas Islands.Results: In our study, the marine areas used by the penguins, parameters of dive depth and the proportion ofpelagic and benthic dives varied both between years and colonies. As a consequence, the energy landscapes alsovaried between the years, and we discuss how this was related to differences in food availability, which were alsoreflected in differences in carbon and nitrogen stable isotope values and isotopic niche metrics. In the second year,the energy landscape was characterized by lower foraging costs per energy gain, and breeding success was alsohigher in this year. Additionally, an area around three South American Fur Seal Arctocephalus australis colonies wasnever used.Conclusions: These results confirm that energy landscapes vary in time and that the seabirds forage in areas of theenergy landscapes that result in minimized energetic costs. Thus, our results support the view of energy landscapesand fear of predation as mechanisms underlying animal foraging behaviour. Furthermore, we show that energylandscapes are useful in linking energy gain and variable energy costs of foraging to breeding success.