How animals distribute themselves inspace: energy landscapes of Antarctic avian predators

[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 agiven environment, as well as how these costs vary in time and for different...

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Bibliographic Details
Published in:Movement Ecology
Main Authors: Masello, Juan F., Barbosa, Andrés, Kato, Akiko, Mattern, Thomas, Medeiros, Renata, Stockdale, Jennifer E., Kümmel, Marc N., Bustamante, Paco, Belliure, Josabel, Benzal, Jesús, Colominas-Ciuró, Roger, Menéndez-Blázquez, Javier, Griep, Sven, Goesmann, Alexander, Symondson, William O.C., Quillfeldt, Petra
Other Authors: German Research Foundation, Agencia Estatal de Investigación (España), Department of Agriculture (US), Natural Environment Research Council (UK), Federal Ministry of Education and Research (Germany), German Network for Bioinformatics Infrastructure, European Commission, Institut Universitaire de France
Format: Article in Journal/Newspaper
Language:English
Published: Springer Nature 2021
Subjects:
Online Access:http://hdl.handle.net/10261/244504
https://doi.org/10.1186/s40462-021-00255-9
https://doi.org/10.13039/501100004795
https://doi.org/10.13039/501100000270
https://doi.org/10.13039/501100011033
https://doi.org/10.13039/501100000780
https://doi.org/10.13039/100000199
https://doi.org/10.13039/501100002347
https://doi.org/10.13039/501100001659
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Summary:[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 agiven environment, as well as how these costs vary in time and for different animal populations. Organisms asdiverse as fish, mammals, and birds will move in areas of the energy landscape that result in minimised costs andmaximised energy gain. Recently, energy landscapes have been used to link energy gain and variable energy costsof 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 analysesof the diet, and leucocyte counts, we studied the response of gentoo (Pygoscelis papua) and chinstrap (Pygoscelisantarcticus) penguins to different energy landscapes and resources. We compared species and gentoo penguinpopulations with contrasting population trends. [Results]: Between populations, gentoo penguins from Livingston Island (Antarctica), a site with positive populationtrends, foraged in energy landscape sectors that implied lower foraging costs per energy gained compared withthose around New Island (Falkland/Malvinas Islands; sub-Antarctic), a breeding site with fluctuating energy costs offoraging, breeding success and populations. Between species, chinstrap penguins foraged in sectors of the energylandscape with lower foraging costs per bottom time, a proxy for energy gain. They also showed lowerphysiological stress, as revealed by leucocyte counts, and higher breeding success than gentoo penguins. In termsof 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 higherbreeding success that would explain the species’positive population trend in the Antarctic Peninsula. The lowerforaging ...