Evidence map›Paper›PMID 40376171›Full record

ArticleEcology and evolution2025

Coupling Genetic and Demographic Data to Reveal Dispersal Processes in Emperor Penguins.

Jimmy Garnier, Gemma Clucas, Jane Younger, Bilgecan Sen, Christophe Barbraud, Michelle LaRue, Alexander D Fraser, Sara Labrousse, Stéphanie Jenouvrier

Abstract read
In one paragraph

Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Jimmy GarnierCNRS, Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, LAMA Chambery France.ORCID https://orcid.org/0000-0002-0145-1028
Gemma ClucasCornell Lab of Ornithology Cornell University Ithaca New York USA.ORCID https://orcid.org/0000-0002-4305-1719
Jane YoungerMilner Centre for Evolution University of Bath Bath UK.ORCID https://orcid.org/0000-0001-5974-7350
Bilgecan SenUniversity of Maryland Center for Environmental Science Frostburg Maryland USA.ORCID https://orcid.org/0000-0002-7393-1805
Christophe BarbraudCentre d'Etudes Biologiques de Chizé CNRS-La Rochelle University UMR7372 Villiers en Bois France.ORCID https://orcid.org/0000-0003-0146-212X
Michelle LaRueSchool of Earth and Environment University of Canterbury Christchurch New Zealand.ORCID https://orcid.org/0000-0002-3886-6059
Alexander D FraserAustralian Antarctic Program Partnership Institute for Marine and Antarctic Studies, University of Tasmania, Nipaluna Hobart Tasmania Australia.ORCID https://orcid.org/0000-0003-1924-0015
Sara LabrousseLaboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), UMR 7159 Sorbonne-Université, CNRS, MNHN, IRD Paris France.ORCID https://orcid.org/0000-0002-8099-3254
Stéphanie JenouvrierBiology Department Woods Hole Oceanographic Institution Woods Hole Massachusetts USA.ORCID https://orcid.org/0000-0003-3324-2383

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dispersal is a ubiquitous phenomenon that affects the dynamics of the population and the evolution of natural populations; however, it is challenging to measure in most species. Furthermore, the influence of informed dispersal behaviors, referring to the nonrandom selection of breeding habitats by individuals, on species' responses to rapid global change is substantial but difficult to comprehend. Here, we present a modeling framework to assess the dispersal characteristics and behaviors of a metapopulation when observations provide information on its neutral genetic structure for a restricted sampling of locations. Our mechanistic-statistical model couples a deterministic model capturing the spatio-temporal dynamics of four genetic clusters across all breeding colonies by integrating demographic processes with genetic projections, with a probabilistic observation model describing the probability to sample an individual from a given genetic cluster. We apply this new framework to the emperor penguin, a species living in Antarctica and currently experiencing habitat loss. The model estimates the species' dispersal distance, rates of emigration, and behaviors associated with dispersal (informed or random). By incorporating these estimations with satellite censuses of breeding colonies, we can identify environmental and demographic factors that influence the dispersal of emperor penguins. Finally, we provide new global population forecasts for emperor penguins that can inform conservation actions in Antarctica.

Indexed as

dispersal distancedispersal kerneldispersal rangeemigration rates

Identifiers

PMID40376171
PMCPMC12078766

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Registered trials

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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.