Juan F Masello1, Andres Barbosa2, Akiko Kato3, Thomas Mattern4,5, Renata Medeiros6,7, Jennifer E Stockdale6, Marc N Kümmel8, Paco Bustamante9,10, Josabel Belliure11, Jesús Benzal12, Roger Colominas-Ciuró2, Javier Menéndez-Blázquez2, Sven Griep8, Alexander Goesmann8, William O C Symondson6, Petra Quillfeldt4. 1. Department of Animal Ecology & Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26, D-35392, Giessen, Germany. juan.f.masello@bio.uni-giessen.de. 2. Department Ecología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, C/José Gutiérrez Abascal, 2, 28006, Madrid, Spain. 3. Centre d'Etudes Biologiques de Chizé, UMR7372 CNRS-Université La Rochelle, 79360, Villiers en Bois, France. 4. Department of Animal Ecology & Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26, D-35392, Giessen, Germany. 5. New Zealand Penguin Initiative, PO Box 6319, Dunedin, 9022, New Zealand. 6. Cardiff School of Biosciences, Cardiff University, The Sir Martin Evans Building, Museum Av, Cardiff, CF10 3AX, UK. 7. Cardiff School of Dentistry, Heath Park, Cardiff, CF14 4XY, UK. 8. Institute for Bioinformatics & Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring 58, D-35392, Giessen, Germany. 9. Littoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS-Université de La Rochelle, 17000, La Rochelle, France. 10. Institut Universitaire de France (IUF), 1 rue Descartes, 75005, Paris, France. 11. GLOCEE - Global Change Ecology and Evolution Group, Universidad de Alcalá, Madrid, Spain. 12. Estación Experimental de Zonas Áridas, CSIC, Almería, Spain.
Abstract
BACKGROUND: Energy landscapes provide an approach to the mechanistic basis of spatial ecology and decision-making in animals. This is based on the quantification of the variation in the energy costs of movements through a given environment, as well as how these costs vary in time and for different animal populations. Organisms as diverse as fish, mammals, and birds will move in areas of the energy landscape that result in minimised costs and maximised energy gain. Recently, energy landscapes have been used to link energy gain and variable energy costs of foraging to breeding success, revealing their potential use for understanding demographic changes. METHODS: Using GPS-temperature-depth and tri-axial accelerometer loggers, stable isotope and molecular analyses of the diet, and leucocyte counts, we studied the response of gentoo (Pygoscelis papua) and chinstrap (Pygoscelis antarcticus) penguins to different energy landscapes and resources. We compared species and gentoo penguin populations with contrasting population trends. RESULTS: Between populations, gentoo penguins from Livingston Island (Antarctica), a site with positive population trends, foraged in energy landscape sectors that implied lower foraging costs per energy gained compared with those around New Island (Falkland/Malvinas Islands; sub-Antarctic), a breeding site with fluctuating energy costs of foraging, breeding success and populations. Between species, chinstrap penguins foraged in sectors of the energy landscape with lower foraging costs per bottom time, a proxy for energy gain. They also showed lower physiological stress, as revealed by leucocyte counts, and higher breeding success than gentoo penguins. In terms of diet, we found a flexible foraging ecology in gentoo penguins but a narrow foraging niche for chinstraps. CONCLUSIONS: The lower foraging costs incurred by the gentoo penguins from Livingston, may favour a higher breeding success that would explain the species' positive population trend in the Antarctic Peninsula. The lower foraging costs in chinstrap penguins may also explain their higher breeding success, compared to gentoos from Antarctica but not their negative population trend. Altogether, our results suggest a link between energy landscapes and breeding success mediated by the physiological condition.
BACKGROUND: Energy landscapes provide an approach to the mechanistic basis of spatial ecology and decision-making in animals. This is based on the quantification of the variation in the energy costs of movements through a given environment, as well as how these costs vary in time and for different animal populations. Organisms as diverse as fish, mammals, and birds will move in areas of the energy landscape that result in minimised costs and maximised energy gain. Recently, energy landscapes have been used to link energy gain and variable energy costs of foraging to breeding success, revealing their potential use for understanding demographic changes. METHODS: Using GPS-temperature-depth and tri-axial accelerometer loggers, stable isotope and molecular analyses of the diet, and leucocyte counts, we studied the response of gentoo (Pygoscelis papua) and chinstrap (Pygoscelis antarcticus) penguins to different energy landscapes and resources. We compared species and gentoo penguin populations with contrasting population trends. RESULTS: Between populations, gentoo penguins from Livingston Island (Antarctica), a site with positive population trends, foraged in energy landscape sectors that implied lower foraging costs per energy gained compared with those around New Island (Falkland/Malvinas Islands; sub-Antarctic), a breeding site with fluctuating energy costs of foraging, breeding success and populations. Between species, chinstrap penguins foraged in sectors of the energy landscape with lower foraging costs per bottom time, a proxy for energy gain. They also showed lower physiological stress, as revealed by leucocyte counts, and higher breeding success than gentoo penguins. In terms of diet, we found a flexible foraging ecology in gentoo penguins but a narrow foraging niche for chinstraps. CONCLUSIONS: The lower foraging costs incurred by the gentoo penguins from Livingston, may favour a higher breeding success that would explain the species' positive population trend in the Antarctic Peninsula. The lower foraging costs in chinstrap penguins may also explain their higher breeding success, compared to gentoos from Antarctica but not their negative population trend. Altogether, our results suggest a link between energy landscapes and breeding success mediated by the physiological condition.
Entities:
Keywords:
Antarctica; Breeding success; Chinstrap penguin Pygoscelis antarcticus; Energy costs; Energy landscapes; Gentoo penguin Pygoscelis papua; Physiological condition; Physiological stress; Population trends; Sub-Antarctic
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