Evidence map›Paper›PMID 34001240›Full record

ArticleMovement ecology2021

How animals distribute themselves in space: energy landscapes of Antarctic avian predators.

Juan F Masello, Andres Barbosa, Akiko Kato, Thomas Mattern, Renata Medeiros, Jennifer E Stockdale, Marc N Kümmel, Paco Bustamante, Josabel Belliure, Jesús Benzal and 6 more

Open access · goldAbstract read
In one paragraph

Article in Movement ecology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
5.4field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 6 institutions in 5 countries.

Juan F MaselloDepartment of Animal Ecology & Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26, D-35392, Giessen, Germany. juan.f.masello@bio.uni-giessen.de.ORCID http://orcid.org/0000-0002-6826-4016
Andres BarbosaDepartment Ecología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, C/José Gutiérrez Abascal, 2, 28006, Madrid, Spain.
Akiko KatoCentre d'Etudes Biologiques de Chizé, UMR7372 CNRS-Université La Rochelle, 79360, Villiers en Bois, France.
Thomas MatternDepartment of Animal Ecology & Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26, D-35392, Giessen, Germany.
Renata MedeirosCardiff School of Biosciences, Cardiff University, The Sir Martin Evans Building, Museum Av, Cardiff, CF10 3AX, UK.
Jennifer E StockdaleCardiff School of Biosciences, Cardiff University, The Sir Martin Evans Building, Museum Av, Cardiff, CF10 3AX, UK.
Marc N KümmelInstitute for Bioinformatics & Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring 58, D-35392, Giessen, Germany.
Paco BustamanteLittoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS-Université de La Rochelle, 17000, La Rochelle, France.
Josabel BelliureGLOCEE - Global Change Ecology and Evolution Group, Universidad de Alcalá, Madrid, Spain.
Jesús BenzalEstación Experimental de Zonas Áridas, CSIC, Almería, Spain.
Roger Colominas-CiuróDepartment Ecología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, C/José Gutiérrez Abascal, 2, 28006, Madrid, Spain.
Javier Menéndez-BlázquezDepartment Ecología Evolutiva, Museo Nacional de Ciencias Naturales, CSIC, C/José Gutiérrez Abascal, 2, 28006, Madrid, Spain.
Sven GriepInstitute for Bioinformatics & Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring 58, D-35392, Giessen, Germany.
Alexander GoesmannInstitute for Bioinformatics & Systems Biology, Justus Liebig University Giessen, Heinrich-Buff-Ring 58, D-35392, Giessen, Germany.
William O C SymondsonCardiff School of Biosciences, Cardiff University, The Sir Martin Evans Building, Museum Av, Cardiff, CF10 3AX, UK.
Petra QuillfeldtDepartment of Animal Ecology & Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26, D-35392, Giessen, Germany.
Justus-Liebig-Universität Gießen · DECardiff University · GBMuseo Nacional de Ciencias Naturales · ESCentre National de la Recherche Scientifique · FREstacion Experimental de Zonas Aridas · ESUniversidad de Alcalá · ES

Funding

Agencia Estatal de Investigación CTM2015-64720-RBMBF 031A533Centre for Ecology and Hydrology NBAF967German Science Foundation MA 2574/6-1
6 · The paper itself

Abstract

backgroundEnergy 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.

methodsUsing 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.

resultsBetween 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.

conclusionsThe 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.

Indexed as

AntarcticaBreeding successChinstrap penguin Pygoscelis antarcticusEnergy costsEnergy landscapesGentoo penguin Pygoscelis papuaPhysiological conditionPhysiological stressPopulation trendsSub-Antarctic

Identifiers

PMID34001240
PMCPMC8127181
OpenAlexW3160289665

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.